diff --git a/AUTHORS.md b/AUTHORS.md index 1412c879..64480a32 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -15,6 +15,7 @@ If you contributed to this code, please list your name and affiliation here: Schulze, Matthias, Institute of Aeroelasticity, Deutsches Zentrum für Luft- und Raumfahrt e.V. Handojo, Vega, Institute of Aeroelasticity, Deutsches Zentrum für Luft- und Raumfahrt e.V. Baier, Jan, Institute of Aeroelasticity, Deutsches Zentrum für Luft- und Raumfahrt e.V. +Carvalho, Francisco, Institute of Aeroelasticity, Deutsches Zentrum für Luft- und Raumfahrt e.V. ``` diff --git a/README.md b/README.md index 35c190eb..b03ce80c 100644 --- a/README.md +++ b/README.md @@ -74,6 +74,16 @@ There are two GUIs to visualize a simulation model (the Model Viewer) and to com model-viewer loads-compare ``` +## Tutorials & Examples +There is a growing number of tutorials based on Jupyter notebooks. You can either have a look at the static html tutorials or use the Jupyter notebooks interactively. For the latter, start a jupyter notebook server, which will open a dashboard in your web browser. Then open one of the *.ipynb notebooks from ./doc/tutorials and walk through the tutorials step-by-step. + +[View html tutorials](https://dlr-ae.github.io/LoadsKernel/tutorials/) + +or + +``` +jupyter notebook +``` # License This software is developed for scientific applications and is delivered as open source without any liability (BSD 3-Clause, please see [LICENSE](LICENSE) for details). For every new aircraft, a validation against test data and/or other simulation tools is highly recommended and in the responsibility of the user. @@ -103,23 +113,8 @@ Development branch [![Regression Tests](https://github.com/DLR-AE/LoadsKernel/actions/workflows/regression-tests.yml/badge.svg?branch=devel)](https://github.com/DLR-AE/LoadsKernel/actions/workflows/regression-tests.yml) [![Coding style](https://github.com/DLR-AE/LoadsKernel/actions/workflows/coding-style.yml/badge.svg?branch=devel)](https://github.com/DLR-AE/LoadsKernel/actions/workflows/coding-style.yml) -# Internal Part (DLR) - -## Examples -There are a number of typical examples, which cover different analyses and simulations. The examples are stored in an additional (internal) DLR GitLab repository: - -``` -git clone https://gitlab.dlr.de/loads-kernel/loads-kernel-examples.git -``` - -## Tutorials -In the ./doc folder, there are is a growing number of tutorials based on Jupyter notebooks. You can either have a look at the [static html tutorials](https://loads-kernel.pages.gitlab.dlr.de/loads-kernel/tutorials/) or use the Jupyter notebooks interactively. Start the jupyter notebook server, which will open a dashborad in your web browser. Then navigate to the ./doc/tutorials, open one of the *.ipynb notebooks and you can walk through the tutorials step-by-step. - -``` -jupyter notebook -``` +# DLR-Internal Long-Term Continuous Integration -## Continuous Integration In addition to the public regression testing (see above), additional and more comprehensive tests are performed and compared against long standing reference results. This is an internal process and the status of the continuous integration pipelines can only be accessed from within DLR: Master branch [![pipeline status](https://gitlab.dlr.de/loads-kernel/loads-kernel/badges/master/pipeline.svg)](https://gitlab.dlr.de/loads-kernel/loads-kernel/-/commits/master) diff --git a/doc/tutorials/DC3_model/JCLs/jcl_dc3_flutter.py b/doc/tutorials/DC3_model/JCLs/jcl_dc3_flutter.py new file mode 100755 index 00000000..6c71008c --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/jcl_dc3_flutter.py @@ -0,0 +1,243 @@ +""" +Job Control File documentation +The Job Control (jcl) is a python class which defines the model and and the simulation and is imported at +the beginning of every simulation. Unlike a conventional parameter file, this allows scripting/programming +of the input, e.g. to convert units, generate mutiple load cases, etc. +Note that this documentation of parameters is comprehensive, but a) not all parameters are necessary for +every kind of simulation and b) some parameters are for experts only --> your JCL might be much smaller. +""" +import os +import pathlib + +import numpy as np + +from loadskernel.units import ft2m, tas2Ma + + +class jcl: + + def __init__(self): + model_root = pathlib.Path(__file__).parent.parent.resolve() + + # Give your aircraft a name and set some general parameters + self.general = {'aircraft': 'DC3', + # Reference span width (from tip to tip) + 'b_ref': 29.0, + # Reference chord length + 'c_ref': 3.508, + # Reference area + 'A_ref': 91.7, + # Mean aerodynamic center, also used as moments reference point + 'MAC_ref': [8.566, 0.0, 0.0], + } + """ + The electronic flight control system (EFCS) provides the "wireing" of the pilot commands + xi, eta and zeta with the control surface deflections. This is aircraft-specific and needs + to be implemented as a python module. + """ + # Electronic flight control system + self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module + # Path where to find the EFCS module + 'path': os.path.join(model_root, 'efcs'), + } + # Read the structural geometry + self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs + # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R) + 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')], + # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft + # 'filename_shell': [], + # bdf file(s) with MONPNT-cards + # 'filename_monpnt': 'monpnt.bdf', + # The following matrices are required for some mass methods. However, the stiffness is geometry + # and not mass dependent. Overview: + # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000' + # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # bdf file(s) with ASET1-card - required for mass method = 'guyan' + # matrix R_trans frum B2000 - required for mass method = 'B2000' + 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'), + 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'), + } + # Settings for the aerodynamic model + self.aero = {'method': 'freq_dom', + # 'mona_steady' - steady trim and quasi-steady time domain simulations + # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust + # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc + # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities + # 'cfd_steady' - steady trim + # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust + # + # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated. + # You will still see deformations, but there is no coupling. + 'flex': True, + # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards + 'method_caero': 'CAERO1', + # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order. + 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1'), + ], + # DMI Matrix for camber and twist correction. Same order as the aerogrid. + 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')], + # bdf file(s) with AESURF-cards + 'filename_aesurf': [os.path.join(model_root, 'aero', 'vt', 'vt.AESURF'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AESURF'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AESURF'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AESURF'), + ], + # bdf file(s) with AELIST-cards + 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AELIST'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AELIST'), + ], + # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z' + 'hingeline': 'y', + # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices) + 'method_AIC': 'dlm', + 'key': ['VC'], + 'Ma': [0.50], + # reduced frequencies for DLM, Nastran Definition! + 'k_red': [0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0], + # number of poles for rational function approximation (RFA) + 'n_poles': 4, + } + # Set the was in which the aerodynamic forces are applied to the structure. + self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran' + # Possibility to use only a subset of the structural grid for splining. True or False + 'splinegrid': False, + # bdf file(s) with GRIDs to ne used + 'filename_splinegrid': ['splinegrid.bdf'] + } + # Settings for the structural dynamics. + self.mass = {'method': 'modalanalysis', # Inplemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000' + 'key': ['M3'], + # MGG via DMAP Alter and OP4 - always required + 'filename_h5': [os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5')], + # True or False, omits first six modes + 'omit_rb_modes': True, + # list(s) of modes to use + 'modes': [np.arange(1, 22)], + } + # Modal damping can be applied as a factor of the stiffness matrix. + self.damping = {'method': 'modal', + 'damping': 0.02, + } + # The international standard atmosphere (ISA) + self.atmo = {'method': 'ISA', + 'key': ['FL000', 'FL055', 'FL075', 'FL210'], + # Altitude in meters + 'h': ft2m([0, 5500, 7500, 21000,]), + } + # Setting of the rigid body equations of motion + self.eom = {'version': 'waszak'} # 'linear' or 'waszak' + """ + The trimcase defines the maneuver load case, one dictionary per load case. + There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or + import an excel sheet. + """ + self.trimcase = [{'desc': 'CC.M3.OVCFL000.KE-Method', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 1, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.PK-Method', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 2, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }] + """ + For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]. + A time simulation is triggered if the simcase contains at least 'dt' and 't_final' + """ + self.simcase = [{'gust': False, # True or False, enables 1-cosine gust according to CS-25 + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': True, + # flutter parameters for k and ke method + 'flutter_para': {'method': 'ke', 'k_red': np.linspace(3.0, 0.001, 100)}}, + {'gust': False, # True or False, enables 1-cosine gust according to CS-25 + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': True, + # flutter parameters for pk method + 'flutter_para': {'method': 'pk', 'Vtas': np.linspace(20.0, 300.0, 20)}}] + # End diff --git a/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust.py b/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust.py new file mode 100755 index 00000000..46f5735a --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust.py @@ -0,0 +1,810 @@ +""" +Job Control File documentation +The Job Control (jcl) is a python class which defines the model and and the simulation and is imported at +the beginning of every simulation. Unlike a conventional parameter file, this allows scripting/programming +of the input, e.g. to convert units, generate mutiple load cases, etc. +Note that this documentation of parameters is comprehensive, but a) not all parameters are necessary for +every kind of simulation and b) some parameters are for experts only --> your JCL might be much smaller. +""" +import numpy as np +import os +from loadskernel.units import ft2m, tas2Ma +import pathlib + + +class jcl: + + def __init__(self): + model_root = pathlib.Path(__file__).parent.parent.resolve() + + # Give your aircraft a name and set some general parameters + self.general = {'aircraft': 'DC3', + # Reference span width (from tip to tip) + 'b_ref': 29.0, + # Reference chord length + 'c_ref': 3.508, + # Reference area + 'A_ref': 91.7, + # Mean aerodynamic center, also used as moments reference point + 'MAC_ref': [8.566, 0.0, 0.0], + } + """ + The electronic flight control system (EFCS) provides the "wireing" of the pilot commands + xi, eta and zeta with the control surface deflections. This is aircraft-specific and needs + to be implemented as a python module. + """ + # Electronic flight control system + self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module + # Path where to find the EFCS module + 'path': os.path.join(model_root, 'efcs'), + } + # Read the structural geometry + self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs + # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R) + 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')], + # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft + # 'filename_shell': [], + # bdf file(s) with MONPNT-cards + 'filename_monpnt': os.path.join(model_root, 'fem', 'export_monitoring-stations.csv'), + # The following matrices are required for some mass methods. However, the stiffness is geometry + # and not mass dependent. Overview: + # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000' + # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # bdf file(s) with ASET1-card - required for mass method = 'guyan' + # matrix R_trans frum B2000 - required for mass method = 'B2000' + 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'), + 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'), + } + # Settings for the aerodynamic model + self.aero = {'method': 'mona_unsteady', + # 'mona_steady' - steady trim and quasi-steady time domain simulations + # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust + # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc + # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities + # 'cfd_steady' - steady trim + # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust + # + # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated. + # You will still see deformations, but there is no coupling. + 'flex': True, + # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards + 'method_caero': 'CAERO1', + # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order. + 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1'), + ], + # DMI Matrix for camber and twist correction. Same order as the aerogrid. + 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')], + # bdf file(s) with AESURF-cards + 'filename_aesurf': [os.path.join(model_root, 'aero', 'vt', 'vt.AESURF'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AESURF'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AESURF'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AESURF'), + ], + # bdf file(s) with AELIST-cards + 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AELIST'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AELIST'), + ], + # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z' + 'hingeline': 'y', + # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices) + 'method_AIC': 'dlm', + 'key': ['VC'], + 'Ma': [0.27], + # reduced frequencies for DLM, Nastran Definition! + 'k_red': [0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0], + # number of poles for rational function approximation (RFA) + 'n_poles': 4, + } + # Set the was in which the aerodynamic forces are applied to the structure. + self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran' + # Possibility to use only a subset of the structural grid for splining. True or False + 'splinegrid': False, + # bdf file(s) with GRIDs to ne used + 'filename_splinegrid': ['splinegrid.bdf'] + } + # Settings for the structural dynamics. + self.mass = {'method': 'modalanalysis', + # Implemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000' + 'key': ['M3'], + # MGG via DMAP Alter and OP4 - always required + 'filename_h5': [ + os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5'), + ], + # True or False, omits first six modes + 'omit_rb_modes': True, + # list(s) of modes to use + 'modes': [np.arange(1, 21)], + } + # Modal damping can be applied as a factor of the stiffness matrix. + self.damping = {'method': 'modal', + 'damping': 0.02, + } + # The international standard atmosphere (ISA) + self.atmo = {'method': 'ISA', + 'key': ['FL000', 'FL055', 'FL075', 'FL210'], + # Altitude in meters + 'h': ft2m([0, 5500, 7500, 21000, ]), + } + # Setting of the rigid body equations of motion + self.eom = {'version': 'waszak'} # 'linear' or 'waszak' + + """ + This section controls the automatic plotting and selection of dimensioning load cases. + Simply put a list of names of the monitoring stations (e.g. ['MON1', 'MON2',...]) into the dictionary + of possible load plots listed below. This will generate a pdf document and nastran force and moment + cards for the dimensioning load cases. + """ + self.loadplots = {'potatos_fz_mx': [], + 'potatos_mx_my': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + 'potatos_fz_my': [], + 'potatos_fy_mx': [], + 'potatos_mx_mz': [], + 'potatos_my_mz': [], + 'cuttingforces_wing': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + } + """ + The trimcase defines the maneuver load case, one dictionary per load case. + There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or + import an excel sheet. + """ + self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 1, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 2, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 3, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 4, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 5, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 6, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 7, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 8, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 9, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 10, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + } + ] + """ + For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]. + A time simulation is triggered if the simcase contains at least 'dt' and 't_final' + """ + self.simcase = [{'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 9.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 16.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 23.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 30.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 37.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 51.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 65.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 79.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 93.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }, + {'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 3.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 107.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, + 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + } + ] + # End diff --git a/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust_H23.py b/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust_H23.py new file mode 100755 index 00000000..b53f84d7 --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/jcl_dc3_gust_H23.py @@ -0,0 +1,232 @@ +""" +Job Control File documentation +The Job Control (jcl) is a python class which defines the model and and the simulation and is imported at +the beginning of every simulation. Unlike a conventional parameter file, this allows scripting/programming +of the input, e.g. to convert units, generate mutiple load cases, etc. +Note that this documentation of parameters is comprehensive, but a) not all parameters are necessary for +every kind of simulation and b) some parameters are for experts only --> your JCL might be much smaller. +""" +import numpy as np +import os +from loadskernel.units import ft2m, tas2Ma +import pathlib + + +class jcl: + + def __init__(self): + model_root = pathlib.Path(__file__).parent.parent.resolve() + + # Give your aircraft a name and set some general parameters + self.general = {'aircraft': 'DC3', + # Reference span width (from tip to tip) + 'b_ref': 29.0, + # Reference chord length + 'c_ref': 3.508, + # Reference area + 'A_ref': 91.7, + # Mean aerodynamic center, also used as moments reference point + 'MAC_ref': [8.566, 0.0, 0.0], + } + """ + The electronic flight control system (EFCS) provides the "wireing" of the pilot commands + xi, eta and zeta with the control surface deflections. This is aircraft-specific and needs + to be implemented as a python module. + """ + # Electronic flight control system + self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module + # Path where to find the EFCS module + 'path': os.path.join(model_root, 'efcs'), + } + # Read the structural geometry + self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs + # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R) + 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')], + # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft + # 'filename_shell': [], + # bdf file(s) with MONPNT-cards + 'filename_monpnt': os.path.join(model_root, 'fem', 'export_monitoring-stations.csv'), + # The following matrices are required for some mass methods. However, the stiffness is geometry + # and not mass dependent. Overview: + # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000' + # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # bdf file(s) with ASET1-card - required for mass method = 'guyan' + # matrix R_trans frum B2000 - required for mass method = 'B2000' + 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'), + 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'), + } + # Settings for the aerodynamic model + self.aero = {'method': 'mona_unsteady', + # 'mona_steady' - steady trim and quasi-steady time domain simulations + # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust + # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc + # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities + # 'cfd_steady' - steady trim + # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust + # + # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated. + # You will still see deformations, but there is no coupling. + 'flex': True, + # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards + 'method_caero': 'CAERO1', + # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order. + 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1'), + ], + # DMI Matrix for camber and twist correction. Same order as the aerogrid. + 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')], + # bdf file(s) with AESURF-cards + 'filename_aesurf': [os.path.join(model_root, 'aero', 'vt', 'vt.AESURF'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AESURF'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AESURF'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AESURF'), + ], + # bdf file(s) with AELIST-cards + 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AELIST'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AELIST'), + ], + # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z' + 'hingeline': 'y', + # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices) + 'method_AIC': 'dlm', + 'key': ['VC'], + 'Ma': [0.27], + # reduced frequencies for DLM, Nastran Definition! + 'k_red': [0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0], + # number of poles for rational function approximation (RFA) + 'n_poles': 4, + } + # Set the was in which the aerodynamic forces are applied to the structure. + self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran' + # Possibility to use only a subset of the structural grid for splining. True or False + 'splinegrid': False, + # bdf file(s) with GRIDs to ne used + 'filename_splinegrid': ['splinegrid.bdf'] + } + # Settings for the structural dynamics. + self.mass = {'method': 'modalanalysis', + # Implemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000' + 'key': ['M3'], + # MGG via DMAP Alter and OP4 - always required + 'filename_h5': [ + os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5'), + ], + # True or False, omits first six modes + 'omit_rb_modes': True, + # list(s) of modes to use + 'modes': [np.arange(1, 21)], + } + # Modal damping can be applied as a factor of the stiffness matrix. + self.damping = {'method': 'modal', + 'damping': 0.02, + } + # The international standard atmosphere (ISA) + self.atmo = {'method': 'ISA', + 'key': ['FL000', 'FL055', 'FL075', 'FL210'], + # Altitude in meters + 'h': ft2m([0, 5500, 7500, 21000, ]), + } + # Setting of the rigid body equations of motion + self.eom = {'version': 'waszak'} # 'linear' or 'waszak' + + """ + This section controls the automatic plotting and selection of dimensioning load cases. + Simply put a list of names of the monitoring stations (e.g. ['MON1', 'MON2',...]) into the dictionary + of possible load plots listed below. This will generate a pdf document and nastran force and moment + cards for the dimensioning load cases. + """ + self.loadplots = {'potatos_fz_mx': [], + 'potatos_mx_my': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + 'potatos_fz_my': [], + 'potatos_fy_mx': [], + 'potatos_mx_mz': [], + 'potatos_my_mz': [], + 'cuttingforces_wing': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + } + """ + The trimcase defines the maneuver load case, one dictionary per load case. + There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or + import an excel sheet. + """ + self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 1, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }] + """ + For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]. + A time simulation is triggered if the simcase contains at least 'dt' and 't_final' + """ + self.simcase = [{'dt': 0.01, # Time step size of the output in [s] + # Final simulation time in [s] + 't_final': 2.0, + # True or False, enables 1-cosine gust according to CS-25 + 'gust': True, + # Gust gradient H (half gust length) in [m] + 'gust_gradient': 23.0, + # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top, + # 90 = gust from the right, 270 = gust from the left, arbitrary values possible + # (rotation of gust direction vector about Nastran's x-axis pointing backwards) + 'gust_orientation': 0, + # Gust parameters according to CS-25 to calculate the gust velocity + # MD is the dive speed in Mach + 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, 'MD': 0.334, + 'T1': 0.0}, + # Alternatively, give gust velocity / Vtas directly + # 'WG_TAS': 0.1, + # True or False, enables continuous turbulence excitation + 'turbulence': False, + # True or False, calculates limit turbulence according to CS-25 + 'limit_turbulence': False, + # True or False, enables playback of control surface signals via efcs + 'cs_signal': False, + # True or False, enables a generic controller e.g. to maintain p, q and r + 'controller': False, + # True or False, enables a generic landing gear + 'landinggear': False, + # True or False, enables calculation of rigid and elastic derivatives + 'derivatives': False, + # True or False, enables flutter check with k, ke or pk method + 'flutter': False, + }] + # End diff --git a/doc/tutorials/DC3_model/JCLs/jcl_dc3_maneuvers.py b/doc/tutorials/DC3_model/JCLs/jcl_dc3_maneuvers.py new file mode 100755 index 00000000..bfb08304 --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/jcl_dc3_maneuvers.py @@ -0,0 +1,317 @@ +""" +Job Control File documentation +The Job Control (jcl) is a python class which defines the model and and the simulation and is imported at +the beginning of every simulation. Unlike a conventional parameter file, this allows scripting/programming +of the input, e.g. to convert units, generate mutiple load cases, etc. +Note that this documentation of parameters is comprehensive, but a) not all parameters are necessary for +every kind of simulation and b) some parameters are for experts only --> your JCL might be much smaller. +""" +import numpy as np +import os +from loadskernel.units import ft2m, tas2Ma +from loadskernel import jcl_helper +import pathlib + + +class jcl: + + def __init__(self): + + model_root = pathlib.Path(__file__).parent.parent.resolve() + + # Give your aircraft a name and set some general parameters + self.general = {'aircraft': 'DC3', + # Reference span width (from tip to tip) + 'b_ref': 29.0, + # Reference chord length + 'c_ref': 3.508, + # Reference area + 'A_ref': 91.7, + # Mean aerodynamic center, also used as moments reference point + 'MAC_ref': [8.566, 0.0, 0.0], + } + """ + The electronic flight control system (EFCS) provides the "wireing" of the pilot commands + xi, eta and zeta with the control surface deflections. This is aircraft-specific and needs + to be implemented as a python module. + """ + # Electronic flight control system + self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module + # Path where to find the EFCS module + 'path': os.path.join(model_root, 'efcs'), + } + # Read the structural geometry + self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs + # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R) + 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')], + # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft + # 'filename_shell': [], + # bdf file(s) with MONPNT-cards + 'filename_monpnt': os.path.join(model_root, 'fem', 'export_monitoring-stations.csv'), + # The following matrices are required for some mass methods. However, the stiffness is geometry + # and not mass dependent. Overview: + # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000' + # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # bdf file(s) with ASET1-card - required for mass method = 'guyan' + # matrix R_trans frum B2000 - required for mass method = 'B2000' + 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'), + 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'), + } + # Settings for the aerodynamic model + self.aero = {'method': 'mona_steady', + # 'mona_steady' - steady trim and quasi-steady time domain simulations + # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust + # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc + # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities + # 'cfd_steady' - steady trim + # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust + # + # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated. + # You will still see deformations, but there is no coupling. + 'flex': True, + # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards + 'method_caero': 'CAERO1', + # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order. + 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1'), + ], + # DMI Matrix for camber and twist correction. Same order as the aerogrid. + 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')], + # bdf file(s) with AESURF-cards + 'filename_aesurf': [os.path.join(model_root, 'aero', 'vt', 'vt.AESURF'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AESURF'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AESURF'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AESURF'), + ], + # bdf file(s) with AELIST-cards + 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AELIST'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AELIST'), + ], + # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z' + 'hingeline': 'y', + # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices) + 'method_AIC': 'vlm', + 'key': ['VC', 'VD'], + 'Ma': [0.27, 0.34], + } + # Set the was in which the aerodynamic forces are applied to the structure. + self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran' + # Possibility to use only a subset of the structural grid for splining. True or False + 'splinegrid': False, + # bdf file(s) with GRIDs to ne used + 'filename_splinegrid': ['splinegrid.bdf'] + } + # Settings for the structural dynamics. + self.mass = {'method': 'modalanalysis', # Inplemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000' + 'key': ['M3'], + # MGG via DMAP Alter and OP4 - always required + 'filename_h5': [os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5')], + # True or False, omits first six modes + 'omit_rb_modes': True, + # list(s) of modes to use + 'modes': [np.arange(1, 71), np.arange(1, 71), np.arange(1, 71), np.arange(1, 71)]} + # Modal damping can be applied as a factor of the stiffness matrix. + self.damping = {'method': 'modal', + 'damping': 0.02} + # The international standard atmosphere (ISA) + self.atmo = {'method': 'ISA', + 'key': ['FL000', 'FL055', 'FL075', 'FL210'], + # Altitude in meters + 'h': ft2m([0, 5500, 7500, 21000,])} + # Setting of the rigid body equations of motion + self.eom = {'version': 'waszak'} # 'linear' or 'waszak' + + """ + This section controls the automatic plotting and selection of dimensioning load cases. + Simply put a list of names of the monitoring stations (e.g. ['MON1', 'MON2',...]) into the dictionary + of possible load plots listed below. This will generate a pdf document and nastran force and moment + cards for the dimensioning load cases. + """ + self.loadplots = {'potatos_fz_mx': [], + 'potatos_mx_my': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + 'potatos_fz_my': [], + 'potatos_fy_mx': [], + 'potatos_mx_mz': [], + 'potatos_my_mz': [], + 'cuttingforces_wing': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', + 'WL17', 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', + 'WR31', 'WR29', 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', + 'WR15', 'WR13', 'WR11', 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + } + """ + The trimcase defines the maneuver load case, one dictionary per load case. + There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or + import an excel sheet. + """ + self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 1, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.pushdown', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 2, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': -1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.pullup', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 3, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 2.5, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0, + # Pitch rate in rad/s + 'q': 0.0, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.roll_right', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 4, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.349, + # Pitch rate in rad/s + 'q': 0.0, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.roll_left', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 5, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': -0.349, + # Pitch rate in rad/s + 'q': 0.0, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }] + """ + For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]. + A time simulation is triggered if the simcase contains at least 'dt' and 't_final' + """ + self.simcase = jcl_helper.generate_empty_listofdicts(self.trimcase) + # End diff --git a/doc/tutorials/DC3_model/JCLs/jcl_dc3_trim.py b/doc/tutorials/DC3_model/JCLs/jcl_dc3_trim.py new file mode 100755 index 00000000..979bbde2 --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/jcl_dc3_trim.py @@ -0,0 +1,257 @@ +""" +Job Control File documentation +The Job Control (jcl) is a python class which defines the model and and the simulation and is imported at +the beginning of every simulation. Unlike a conventional parameter file, this allows scripting/programming +of the input, e.g. to convert units, generate mutiple load cases, etc. +Note that this documentation of parameters is comprehensive, but a) not all parameters are necessary for +every kind of simulation and b) some parameters are for experts only --> your JCL might be much smaller. +""" +import numpy as np +import os +from loadskernel.units import ft2m, tas2Ma +from loadskernel import jcl_helper +import pathlib + + +class jcl: + + def __init__(self): + + model_root = pathlib.Path(__file__).parent.parent.resolve() + + # Give your aircraft a name and set some general parameters + self.general = {'aircraft': 'DC3', + # Reference span width (from tip to tip) + 'b_ref': 29.0, + # Reference chord length + 'c_ref': 3.508, + # Reference area + 'A_ref': 91.7, + # Mean aerodynamic center, also used as moments reference point + 'MAC_ref': [8.566, 0.0, 0.0], + } + """ + The electronic flight control system (EFCS) provides the "wireing" of the pilot commands + xi, eta and zeta with the control surface deflections. This is aircraft-specific and needs + to be implemented as a python module. + """ + # Electronic flight control system + self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module + # Path where to find the EFCS module + 'path': os.path.join(model_root, 'efcs'), + } + # Read the structural geometry + self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs + # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R) + 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')], + # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft + # 'filename_shell': [], + # bdf file(s) with MONPNT-cards + 'filename_monpnt': os.path.join(model_root, 'fem', 'export_monitoring-stations.csv'), + # The following matrices are required for some mass methods. However, the stiffness is geometry + # and not mass dependent. Overview: + # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000' + # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' + # bdf file(s) with ASET1-card - required for mass method = 'guyan' + # matrix R_trans frum B2000 - required for mass method = 'B2000' + 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'), + 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'), + } + # Settings for the aerodynamic model + self.aero = {'method': 'mona_steady', + # 'mona_steady' - steady trim and quasi-steady time domain simulations + # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust + # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc + # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities + # 'cfd_steady' - steady trim + # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust + # + # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated. + # You will still see deformations, but there is no coupling. + 'flex': True, + # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards + 'method_caero': 'CAERO1', + # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order. + 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1')], + # DMI Matrix for camber and twist correction. Same order as the aerogrid. + 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')], + # bdf file(s) with AESURF-cards + 'filename_aesurf': [os.path.join(model_root, 'aero', 'vt', 'vt.AESURF'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AESURF'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AESURF'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AESURF') + ], + # bdf file(s) with AELIST-cards + 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'), + os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'), + os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'), + os.path.join(model_root, 'aero', 'left-wing', 'left-wing.AELIST'), + os.path.join(model_root, 'aero', 'right-wing', 'right-wing.AELIST') + ], + # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z' + 'hingeline': 'y', + # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices) + 'method_AIC': 'vlm', + 'key': ['VC', 'VD'], + 'Ma': [0.27, 0.34], + } + # Set the was in which the aerodynamic forces are applied to the structure. + self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran' + # Possibility to use only a subset of the structural grid for splining. True or False + 'splinegrid': False, + # bdf file(s) with GRIDs to ne used + 'filename_splinegrid': ['splinegrid.bdf'] + } + # Settings for the structural dynamics. + self.mass = {'method': 'modalanalysis', # Inplemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000' + 'key': ['M3'], + # MGG via DMAP Alter and OP4 - always required + 'filename_h5': [os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5')], + # True or False, omits first six modes + 'omit_rb_modes': True, + # list(s) of modes to use + 'modes': [np.arange(1, 71), np.arange(1, 71), np.arange(1, 71), np.arange(1, 71)], + } + # Modal damping can be applied as a factor of the stiffness matrix. + self.damping = {'method': 'modal', + 'damping': 0.02, + } + # The international standard atmosphere (ISA) + self.atmo = {'method': 'ISA', + 'key': ['FL000', 'FL055', 'FL075', 'FL210'], + # Altitude in meters + 'h': ft2m([0, 5500, 7500, 21000,]), + } + # Setting of the rigid body equations of motion + self.eom = {'version': 'waszak'} # 'linear' or 'waszak' + + """ + This section controls the automatic plotting and selection of dimensioning load cases. + Simply put a list of names of the monitoring stations (e.g. ['MON1', 'MON2',...]) into the dictionary + of possible load plots listed below. This will generate a pdf document and nastran force and moment + cards for the dimensioning load cases. + """ + self.loadplots = {'potatos_fz_mx': [], + 'potatos_mx_my': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', 'WL17', + 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', 'WR31', 'WR29', + 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', 'WR15', 'WR13', 'WR11', + 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + 'potatos_fz_my': [], + 'potatos_fy_mx': [], + 'potatos_mx_mz': [], + 'potatos_my_mz': [], + 'cuttingforces_wing': ['WL01', 'WL03', 'WL05', 'WL07', 'WL09', 'WL11', 'WL13', 'WL15', + 'WL17', 'WL19', 'WL21', 'WL23', 'WL25', 'WL27', 'WL29', 'WL31', + 'WR31', 'WR29', 'WR27', 'WR25', 'WR23', 'WR21', 'WR19', 'WR17', + 'WR15', 'WR13', 'WR11', 'WR09', 'WR07', 'WR05', 'WR03', 'WR01'], + } + """ + The trimcase defines the maneuver load case, one dictionary per load case. + There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or + import an excel sheet. + """ + self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 1, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.pushdown', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 2, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': -1.0, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0 / 180.0 * np.pi, + # Pitch rate in rad/s + 'q': 0.0 / 180.0 * np.pi, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }, + {'desc': 'CC.M3.OVCFL000.pullup', # Descriptive string of the maneuver case + # Kind of trim condition, blank for trim about all three axes, for more trim conditions see + # trim_conditions.py + 'maneuver': '', + # Subcase ID number, for Nastran in acending order + 'subcase': 3, + # Setting of the operational point + # The flight speed is given by the Mach number + 'Ma': tas2Ma(70.0, 0.0), + # Aero key + 'aero': 'VC', + # Atmo key + 'altitude': 'FL000', + # Mass key + 'mass': 'M3', + # Load factor Nz + 'Nz': 2.5, + # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down) + # Roll rate in rad/s + 'p': 0.0, + # Pitch rate in rad/s + 'q': 0.0, + # Yaw rate in rad/s + 'r': 0.0, + # Roll acceleration in rad/s^2 + 'pdot': 0.0, + # Pitch acceleration in rad/s^2 + 'qdot': 0.0, + # Yaw acceleration in rad/s^2 + 'rdot': 0.0, + }] + """ + For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]. + A time simulation is triggered if the simcase contains at least 'dt' and 't_final' + """ + self.simcase = jcl_helper.generate_empty_listofdicts(self.trimcase) + # End diff --git a/doc/tutorials/DC3_model/JCLs/launch.py b/doc/tutorials/DC3_model/JCLs/launch.py new file mode 100644 index 00000000..9bc9632b --- /dev/null +++ b/doc/tutorials/DC3_model/JCLs/launch.py @@ -0,0 +1,7 @@ +from loadskernel import program_flow + +# Here you launch the Loads Kernel with your job +k = program_flow.Kernel('jcl_dc3_gust_H23', pre=True, main=True, post=True, test=False, + path_input='../JCLs', + path_output='../../DC3_results') +k.run() diff --git a/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST b/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST new file mode 100644 index 00000000..36a1b1b3 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST @@ -0,0 +1,33 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AELIST SID E1 THRU Ei +$------><------><------><------><------><------><------><------><------> +AELIST 3333001 3333001 THRU 3333035 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF b/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF new file mode 100644 index 00000000..48327275 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF @@ -0,0 +1,39 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AESURF ID LABEL CID ALID1 CID2 ALID2 EFF +$------><------><------><------><------><------><------><------><------> +AESURF 3333001 ELE-LFT 333 3333001 1.00000 +$ Coordinate system with y-axis along leading edge +$ +$ +CORD2R 333 19.9070 -4.2056 1.86699 19.9070 -4.2056 2.86700+ ++ 20.9070 -4.2066 .866999 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1 b/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1 new file mode 100644 index 00000000..776e1ecc --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1 @@ -0,0 +1,40 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ +CAERO1 3331001 1001 0 1 9 1+ ++ 18.2614-.542535 1.86699 2.74351 18.0200 0.00000 1.86699 2.98499 +$ +CAERO1 3332001 1001 0 6 5 1+ ++ 19.6580-3.67998 1.86699 .249194 18.2614-.542535 1.86699 1.64885 +$ +CAERO1 3333001 1001 0 7 5 1+ ++ 19.9070-4.20569 1.86699 1.09797 19.9103-.542535 1.86699 1.09466 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST b/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST new file mode 100644 index 00000000..ab949132 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST @@ -0,0 +1,33 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:51 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AELIST SID E1 THRU Ei +$------><------><------><------><------><------><------><------><------> +AELIST 5404001 5404001 THRU 5404080 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF b/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF new file mode 100644 index 00000000..18bbbda9 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF @@ -0,0 +1,39 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:51 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AESURF ID LABEL CID ALID1 CID2 ALID2 EFF +$------><------><------><------><------><------><------><------><------> +AESURF 5404001 AIL-LFT 531 5404001 1.00000 +$ Coordinate system with y-axis along leading edge +$ +$ +CORD2R 531 10.8351 -13.729 .929000 10.8351 -13.652 1.92601+ ++ 11.8322 -13.731 -.07386 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1 b/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1 new file mode 100644 index 00000000..53b36fad --- /dev/null +++ b/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1 @@ -0,0 +1,44 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:51 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ +$ +CAERO1 5401001 1001 0 7 12 1+ ++ 6.88999-3.68000 .150999 4.32000 6.88999 0.00000 .150999 4.32000 +$ +CAERO1 5402001 1001 0 5 12 1+ ++ 7.65850-6.34324 .357169 3.55150 6.88999-3.68000 .150999 4.32000 +$ +CAERO1 5403001 1001 0 20 10 1+ ++ 9.78999-13.7299 .929000 1.04512 7.65850-6.34324 .357169 2.61390 +$ +CAERO1 5404001 1001 0 20 4 1+ ++ 10.8351-13.7299 .929000 .374879 10.2724-6.34324 .357169 .937596 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST b/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST new file mode 100644 index 00000000..b01208ab --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST @@ -0,0 +1,33 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AELIST SID E1 THRU Ei +$------><------><------><------><------><------><------><------><------> +AELIST 3343001 3343001 THRU 3343035 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF b/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF new file mode 100644 index 00000000..3e8ce226 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF @@ -0,0 +1,39 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AESURF ID LABEL CID ALID1 CID2 ALID2 EFF +$------><------><------><------><------><------><------><------><------> +AESURF 3343001 ELE-RIG 334 3343001 1.00000 +$ Coordinate system with y-axis along leading edge +$ +$ +CORD2R 334 19.9103 .542535 1.86699 19.9103 .542535 2.86700+ ++ 20.9103 .543439 .866999 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1 b/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1 new file mode 100644 index 00000000..3437a8a3 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1 @@ -0,0 +1,40 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ +CAERO1 3341001 1001 0 1 9 1+ ++ 18.0200 0.00000 1.86699 2.98499 18.2614 .542535 1.86699 2.74351 +$ +CAERO1 3342001 1001 0 6 5 1+ ++ 18.2614 .542535 1.86699 1.64885 19.6580 3.67998 1.86699 .249194 +$ +CAERO1 3343001 1001 0 7 5 1+ ++ 19.9103 .542535 1.86699 1.09466 19.9070 4.20569 1.86699 1.09797 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST b/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST new file mode 100644 index 00000000..8511e18b --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST @@ -0,0 +1,33 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:16 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AELIST SID E1 THRU Ei +$------><------><------><------><------><------><------><------><------> +AELIST 6404001 6404001 THRU 6404080 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF b/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF new file mode 100644 index 00000000..81853eb4 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF @@ -0,0 +1,39 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:16 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AESURF ID LABEL CID ALID1 CID2 ALID2 EFF +$------><------><------><------><------><------><------><------><------> +AESURF 6404001 AIL-RIG 631 6404001 1.00000 +$ Coordinate system with y-axis along leading edge +$ +$ +CORD2R 631 10.2724 6.34324 .357169 10.2724 6.26606 1.35418+ ++ 11.2695 6.34492 -.64569 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1 b/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1 new file mode 100644 index 00000000..e124380f --- /dev/null +++ b/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1 @@ -0,0 +1,44 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:16 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ +$ +CAERO1 6401001 1001 0 7 12 1+ ++ 6.88999 0.00000 .150999 4.32000 6.88999 3.68000 .150999 4.32000 +$ +CAERO1 6402001 1001 0 5 12 1+ ++ 6.88999 3.68000 .150999 4.32000 7.65850 6.34324 .357169 3.55150 +$ +CAERO1 6403001 1001 0 20 10 1+ ++ 7.65850 6.34324 .357169 2.61390 9.78999 13.7299 .929000 1.04512 +$ +CAERO1 6404001 1001 0 20 4 1+ ++ 10.2724 6.34324 .357169 .937596 10.8351 13.7299 .929000 .374879 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/vt/vt.AELIST b/doc/tutorials/DC3_model/aero/vt/vt.AELIST new file mode 100644 index 00000000..944cb7f2 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/vt/vt.AELIST @@ -0,0 +1,33 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AELIST SID E1 THRU Ei +$------><------><------><------><------><------><------><------><------> +AELIST 3322001 3322001 THRU 3322030 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/vt/vt.AESURF b/doc/tutorials/DC3_model/aero/vt/vt.AESURF new file mode 100644 index 00000000..693484e4 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/vt/vt.AESURF @@ -0,0 +1,39 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$AESURF ID LABEL CID ALID1 CID2 ALID2 EFF +$------><------><------><------><------><------><------><------><------> +AESURF 3322001 RUD 311 3322001 1.00000 +$ Coordinate system with y-axis along leading edge +$ +$ +CORD2R 311 19.6128 0.00+0 1.86699 19.6128 -1.0000 1.86699+ ++ 20.6122 1.00000 1.83232 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/aero/vt/vt.CAERO1 b/doc/tutorials/DC3_model/aero/vt/vt.CAERO1 new file mode 100644 index 00000000..81382fc7 --- /dev/null +++ b/doc/tutorials/DC3_model/aero/vt/vt.CAERO1 @@ -0,0 +1,37 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ +CAERO1 3321001 1001 0 6 5 1+ ++ 17.4160 0.00000 1.86699 2.19684 19.0639 0.00000 4.72499 .647993 +$ +CAERO1 3322001 1001 0 6 5 1+ ++ 19.6128 0.00000 1.86699 1.64715 19.7119 0.00000 4.72499 1.00000 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/efcs/efcs_dc3.py b/doc/tutorials/DC3_model/efcs/efcs_dc3.py new file mode 100644 index 00000000..6b4c81ba --- /dev/null +++ b/doc/tutorials/DC3_model/efcs/efcs_dc3.py @@ -0,0 +1,27 @@ +import copy +import numpy as np + + +class Efcs(): + + def __init__(self): + self.keys = ['RUD', 'ELE-LFT', 'ELE-RIG', 'AIL-LFT', 'AIL-RIG'] + self.Ux2 = np.zeros(5) + + def cs_mapping(self, commands): + command_xi = commands[0] + command_eta = commands[1] + command_zeta = commands[2] + + Ux2 = copy.deepcopy(self.Ux2) + # positive xi (stick to the right) => roll to the right + Ux2[3] += command_xi + Ux2[4] -= command_xi + + # positive eta (stick pulled) => nose up + Ux2[1] -= command_eta + Ux2[2] -= command_eta + + # negative zeta (right pedal pushed) => yaw to the right + Ux2[0] -= command_zeta + return Ux2 diff --git a/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5 b/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5 new file mode 100644 index 00000000..1a4573e9 Binary files /dev/null and b/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5 differ diff --git a/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5 b/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5 new file mode 100644 index 00000000..e22d8580 Binary files /dev/null and b/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5 differ diff --git a/doc/tutorials/DC3_model/fem/export_FUS.csv b/doc/tutorials/DC3_model/fem/export_FUS.csv new file mode 100644 index 00000000..81e31dbf --- /dev/null +++ b/doc/tutorials/DC3_model/fem/export_FUS.csv @@ -0,0 +1,48 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ FUS nodes ref is 2meters in frontof nose +$ GRID ID CP X1 X2 X3 CD +GRID 100001 2.0000 0.000 1.550 +GRID 100002 3.9431 0.000 1.550 +GRID 100003 5.8862 0.000 1.550 +GRID 100004 7.8293 0.000 1.550 +GRID 100005 9.7724 0.000 1.550 +GRID 100006 11.7155 0.000 1.550 +GRID 100007 13.6586 0.000 1.550 +GRID 100008 15.6017 0.000 1.550 +GRID 100009 17.5448 0.000 1.550 +GRID 100010 19.4879 0.000 1.550 +GRID 100011 21.4310 0.000 1.550 + +$ FUS rigid body +$RBE2 EID GN CM GM1 GM2 GM3 GM4 GM5+ +$ GM6 GM7 GM8 etc ALPHA TREF +RBE2 100000 100004 123456 100001 100002 100003 100005 100006 + 100007 100008 100009 100010 100011 + + +$ FUS mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 110001 100001 0 19.313 0.000 0.000 0.000 + + 0.358 0.000 0.000 0.000 0.000 0.000 +CONM2 110002 100002 0 141.008 0.000 0.000 0.000 + + 139.474 0.000 0.000 0.000 0.000 0.000 +CONM2 110003 100003 0 172.413 0.000 0.000 0.000 + + 254.959 0.000 0.000 0.000 0.000 0.000 +CONM2 110004 100004 0 184.190 0.000 0.000 0.000 + + 310.855 0.000 0.000 0.000 0.000 0.000 +CONM2 110005 100005 0 180.264 0.000 0.000 0.000 + + 291.400 0.000 0.000 0.000 0.000 0.000 +CONM2 110006 100006 0 178.301 0.000 0.000 0.000 + + 281.984 0.000 0.000 0.000 0.000 0.000 +CONM2 110007 100007 0 162.599 0.000 0.000 0.000 + + 213.852 0.000 0.000 0.000 0.000 0.000 +CONM2 110008 100008 0 146.896 0.000 0.000 0.000 + + 157.686 0.000 0.000 0.000 0.000 0.000 +CONM2 110009 100009 0 117.454 0.000 0.000 0.000 + + 80.606 0.000 0.000 0.000 0.000 0.000 +CONM2 110010 100010 0 74.272 0.000 0.000 0.000 + + 20.382 0.000 0.000 0.000 0.000 0.000 +CONM2 110011 100011 0 31.090 0.000 0.000 0.000 + + 1.495 0.000 0.000 0.000 0.000 0.000 diff --git a/doc/tutorials/DC3_model/fem/export_left-nacell.csv b/doc/tutorials/DC3_model/fem/export_left-nacell.csv new file mode 100644 index 00000000..04544f82 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/export_left-nacell.csv @@ -0,0 +1,22 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ NAC-P nodes +$ GRID ID CP X1 X2 X3 CD +GRID 54100001 5.129 -2.745 0.000 +GRID 54100002 5.897 -2.745 0.000 +GRID 54100003 7.105 -2.745 0.000 + + +$NAC-P rigid body connect to wing LRA +$RBE2 EID GN CM GM1 GM2 GM3 GM4 GM5+ +$ GM6 GM7 GM8 etc ALPHA TREF +RBE2 5410000054090006 123456541000015410000254100003 +$ NAC-S mass model +$ CONM2 EID G CID M X1 X2 + +$ I11 I21 I22 I31 I32 I33 X3 +CONM2 541100154100001 0 287.8 0.00 0.00 + + 73.257 0.00 0.00 0.00 0.00 0.00 0.00 +CONM2 541100254100002 0 470.95 0.00 0.00 + + 102.107 0.00 0.00 0.00 0.00 0.00 0.00 +CONM2 541100354100003 0 183.15 0.00 0.00 + + 33.408 0.00 0.00 0.00 0.00 0.00 0.00 diff --git a/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv b/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv new file mode 100644 index 00000000..e5b0805d --- /dev/null +++ b/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv @@ -0,0 +1,211 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 + + +$ Right +MONPNT1 WR01 + 123456WR090001 0 8.0184 0.0000 0.1973 0 +AECOMP WR090001SET1 64090001 +SET1 6409000164090001THRU 6409003164090101THRU 6409013164090201+ ++ THRU 6409023164100001THRU 64100003 + +MONPNT1 WR03 + 123456WR090003 0 8.0184 1.0525 0.1973 0 +AECOMP WR090003SET1 64090003 +SET1 6409000364090003THRU 6409003164090103THRU 6409013164090203+ ++ THRU 6409023164100001THRU 64100003 + +MONPNT1 WR05 + 123456WR090005 0 8.0184 2.1013 0.1973 0 +AECOMP WR090005SET1 64090005 +SET1 6409000564090005THRU 6409003164090105THRU 6409013164090205+ ++ THRU 6409023164100001THRU 64100003 + +MONPNT1 WR07 + 123456WR090007 0 8.0184 3.1537 0.1973 0 +AECOMP WR090007SET1 64090007 +SET1 6409000764090007THRU 6409003164090107THRU 6409013164090207+ ++ THRU 64090231 + + +$ Local +CORD2R 641 0 8.01838 3.6800 0.1973 8.01838 3.6800 1.1973 + 8.1000 3.6625 0.1973 + + +$ Right +MONPNT1 WR09 + 123456WR090009 0 8.1115 4.1169 0.2297 641 +AECOMP WR090009SET1 64090009 +SET1 6409000964090009THRU 6409003164090109THRU 6409013164090209+ ++ THRU 64090231 + +MONPNT1 WR11 + 123456WR090011 0 8.2979 4.9907 0.2947 641 +AECOMP WR090011SET1 64090011 +SET1 6409001164090011THRU 6409003164090111THRU 6409013164090211+ ++ THRU 64090231 + +MONPNT1 WR13 + 123456WR090013 0 8.4842 5.8646 0.3596 641 +AECOMP WR090013SET1 64090013 +SET1 6409001364090013THRU 6409003164090111THRU 6409013164090213+ ++ THRU 64090231 + +MONPNT1 WR15 + 123456WR090015 0 8.6705 6.7384 0.4246 641 +AECOMP WR090015SET1 64090015 +SET1 6409001564090015THRU 6409003164090115THRU 6409013164090215+ ++ THRU 64090231 + +MONPNT1 WR17 + 123456WR090017 0 8.8568 7.6122 0.4895 641 +AECOMP WR090017SET1 64090017 +SET1 6409001764090017THRU 6409003164090117THRU 6409013164090217+ ++ THRU 64090231 + +MONPNT1 WR19 + 123456WR090019 0 9.0432 8.4861 0.5545 641 +AECOMP WR090019SET1 64090019 +SET1 6409001964090019THRU 6409003164090119THRU 6409013164090219+ ++ THRU 64090231 + +MONPNT1 WR21 + 123456WR090021 0 9.2295 9.3599 0.6194 641 +AECOMP WR090021SET1 64090021 +SET1 6409002164090021THRU 6409003164090121THRU 6409013164090221+ ++ THRU 64090231 + +MONPNT1 WR23 + 123456WR090023 0 9.4158 10.2335 0.6844 641 +AECOMP WR090023SET1 64090023 +SET1 6409002364090023THRU 6409003164090123THRU 6409013164090223+ ++ THRU 64090231 + +MONPNT1 WR25 + 123456WR090025 0 9.6021 11.1073 0.7493 641 +AECOMP WR090025SET1 64090025 +SET1 6409002564090025THRU 6409003164090125THRU 6409013164090225+ ++ THRU 64090231 + +MONPNT1 WR27 + 123456WR090027 0 9.7884 11.9810 0.8143 641 +AECOMP WR090027SET1 64090027 +SET1 6409002764090027THRU 6409003164090127THRU 6409013164090227+ ++ THRU 64090231 + +MONPNT1 WR29 + 123456WR090029 0 9.9747 12.8545 0.8792 641 +AECOMP WR090029SET1 64090029 +SET1 6409002964090029THRU 6409003164090129THRU 6409013164090229+ ++ THRU 64090231 + +MONPNT1 WR31 + 123456WR090031 0 10.1607 13.7275 0.9441 641 +AECOMP WR090031SET1 64090031 +SET1 64090031640900316409013164090231 + + + +$ Left W +MONPNT1 WL01 + 123456WL090001 0 8.0184 0.0000 0.1973 0 +AECOMP WL090001SET1 54090001 +SET1 5409000154090001THRU 5409003154090101THRU 5409013154090201+ ++ THRU 5409023154100001THRU 54100003 + +MONPNT1 WL03 + 123456WL090003 0 8.0184 -1.0525 0.1973 0 +AECOMP WL090003SET1 54090003 +SET1 5409000354090003THRU 5409003154090103THRU 5409013154090203+ ++ THRU 5409023154100001THRU 54100003 + +MONPNT1 WL05 + 123456WL090005 0 8.0184 -2.1013 0.1973 0 +AECOMP WL090005SET1 54090005 +SET1 5409000554090005THRU 5409003154090105THRU 5409013154090205+ ++ THRU 5409023154100001THRU 54100003 + +MONPNT1 WL07 + 123456WL090007 0 8.0184 -3.1537 0.1973 0 +AECOMP WL090007SET1 54090007 +SET1 5409000754090007THRU 5409003154090107THRU 5409013154090207+ ++ THRU 54090231 + + +$ Local +CORD2R 541 0 8.01838 -3.6800 0.1973 8.01838 -3.6800 1.1973 + 8.1000 -3.6625 0.1973 + + +$ Left W +MONPNT1 WL09 + 123456WL090009 0 8.1115 -4.1169 0.2297 541 +AECOMP WL090009SET1 54090009 +SET1 5409000954090009THRU 5409003154090109THRU 5409013154090209+ ++ THRU 54090231 + +MONPNT1 WL11 + 123456WL090011 0 8.2979 -4.9907 0.2947 541 +AECOMP WL090011SET1 54090011 +SET1 5409001154090011THRU 5409003154090111THRU 5409013154090211+ ++ THRU 54090231 + +MONPNT1 WL13 + 123456WL090013 0 8.4842 -5.8646 0.3596 541 +AECOMP WL090013SET1 54090013 +SET1 5409001354090013THRU 5409003154090113THRU 5409013154090213+ ++ THRU 54090231 + +MONPNT1 WL15 + 123456WL090015 0 8.6705 -6.7384 0.4246 541 +AECOMP WL090015SET1 54090015 +SET1 5409001554090015THRU 5409003154090115THRU 5409013154090215+ ++ THRU 54090231 + +MONPNT1 WL17 + 123456WL090017 0 8.8568 -7.6122 0.4895 541 +AECOMP WL090017SET1 54090017 +SET1 5409001754090017THRU 5409003154090117THRU 5409013154090217+ ++ THRU 54090231 + +MONPNT1 WL19 + 123456WL090019 0 9.0432 -8.4861 0.5545 541 +AECOMP WL090019SET1 54090019 +SET1 5409001954090019THRU 5409003154090119THRU 5409013154090219+ ++ THRU 54090231 + +MONPNT1 WL21 + 123456WL090021 0 9.2295 -9.3599 0.6194 541 +AECOMP WL090021SET1 54090021 +SET1 5409002154090021THRU 5409003154090121THRU 5409013154090221+ ++ THRU 54090231 + +MONPNT1 WL23 + 123456WL090023 0 9.4158-10.2335 0.6844 541 +AECOMP WL090023SET1 54090023 +SET1 5409002354090023THRU 5409003154090123THRU 5409013154090223+ ++ THRU 54090231 + +MONPNT1 WL25 + 123456WL090025 0 9.6021-11.1073 0.7493 541 +AECOMP WL090025SET1 54090025 +SET1 5409002554090025THRU 5409003154090125THRU 5409013154090225+ ++ THRU 54090231 + +MONPNT1 WL27 + 123456WL090027 0 9.7884-11.9810 0.8143 541 +AECOMP WL090027SET1 54090027 +SET1 5409002754090027THRU 5409003154090127THRU 5409013154090227+ ++ THRU 54090231 + +MONPNT1 WL29 + 123456WL090029 0 9.9747-12.8545 0.8792 541 +AECOMP WL090029SET1 54090029 +SET1 5409002954090029THRU 5409003154090129THRU 5409013154090229+ ++ THRU 54090231 + +MONPNT1 WL31 + 123456WL090031 0 10.1607-13.7275 0.9441 541 +AECOMP WL090031SET1 54090031 +SET1 54090031540900315409013154090231 diff --git a/doc/tutorials/DC3_model/fem/export_right-nacell.csv b/doc/tutorials/DC3_model/fem/export_right-nacell.csv new file mode 100644 index 00000000..5ceacc10 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/export_right-nacell.csv @@ -0,0 +1,22 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ NAC-S nodes +$ GRID ID CP X1 X2 X3 CD +GRID 64100001 5.129 2.745 0.000 +GRID 64100002 5.897 2.745 0.000 +GRID 64100003 7.105 2.745 0.000 + + +$NAC-S rigid body connect to wing LRA +$RBE2 EID GN CM GM1 GM2 GM3 GM4 GM5+ +$ GM6 GM7 GM8 etc ALPHA TREF +RBE2 6410000064090006 123456641000016410000264100003 +$ NAC-S mass model +$ CONM2 EID G CID M X1 X2 + +$ I11 I21 I22 I31 I32 I33 X3 +CONM2 641100164100001 0 287.8 0.00 0.00 + + 73.257 0.00 0.00 0.00 0.00 0.00 0.00 +CONM2 641100264100002 0 470.95 0.00 0.00 + + 102.107 0.00 0.00 0.00 0.00 0.00 0.00 +CONM2 641100364100003 0 183.15 0.00 0.00 + + 33.408 0.00 0.00 0.00 0.00 0.00 0.00 diff --git a/doc/tutorials/DC3_model/fem/left-ht/export_left-ht.csv b/doc/tutorials/DC3_model/fem/left-ht/export_left-ht.csv new file mode 100644 index 00000000..b9c42b3f --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/export_left-ht.csv @@ -0,0 +1,45 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$left-ht +$CBAR EID PID GA GB X1 X2 X3 + +$+ w1A w2A w3A w1B w2B w3B +CBAR 3338001 33380013339000133390002 0.0000 0.0000 1.0000 +CBAR 3338002 33380023339000233390003 0.0000 0.0000 1.0000 +CBAR 3338003 33380033339000333390004 0.0000 0.0000 1.0000 +CBAR 3338004 33380043339000433390005 0.0000 0.0000 1.0000 +CBAR 3338005 33380053339000533390006 0.0000 0.0000 1.0000 +CBAR 3338006 33380063339000633390007 0.0000 0.0000 1.0000 +CBAR 3338007 33380073339000733390008 0.0000 0.0000 1.0000 + + + + +$ PBAR PID MID A I1 I2 J NSM +PBAR 3338001 3330011.214E-22.000E-49.420E-39.590E-3 0.000 +PBAR 3338002 3330011.001E-29.633E-56.430E-36.540E-3 0.000 +PBAR 3338003 3330018.032E-36.214E-54.149E-34.220E-3 0.000 +PBAR 3338004 3330016.268E-33.796E-52.537E-32.581E-3 0.000 +PBAR 3338005 3330014.724E-32.164E-51.449E-31.474E-3 0.000 +PBAR 3338006 3330013.399E-31.132E-57.500E-47.690E-4 0.000 +PBAR 3338007 3330012.290E-35.236E-63.431E-43.547E-4 0.000 + + +$ left ht mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 333810133390001 0 10.663 0.00 0.00 0.00 + + 0.00 0.00 8.6823 0.00 0.00 0.00 +CONM2 333810233390002 0 17.841 0.00 0.00 0.00 + + 0.00 0.00 12.5584 0.00 0.00 0.00 +CONM2 333810333390003 0 14.466 0.00 0.00 0.00 + + 0.00 0.00 8.5231 0.00 0.00 0.00 +CONM2 333810433390004 0 11.445 0.00 0.00 0.00 + + 0.00 0.00 5.3348 0.00 0.00 0.00 +CONM2 333810533390005 0 8.777 0.00 0.00 0.00 + + 0.00 0.00 3.1377 0.00 0.00 0.00 +CONM2 333810633390006 0 6.463 0.00 0.00 0.00 + + 0.00 0.00 1.7012 0.00 0.00 0.00 +CONM2 333810733390007 0 4.502 0.00 0.00 0.00 + + 0.00 0.00 0.8284 0.00 0.00 0.00 +CONM2 333810833390008 0 1.442 0.00 0.00 0.00 + + 0.00 0.00 0.1707 0.00 0.00 0.00 diff --git a/doc/tutorials/DC3_model/fem/left-ht/left-ht.CORD2R_LREFAX b/doc/tutorials/DC3_model/fem/left-ht/left-ht.CORD2R_LREFAX new file mode 100644 index 00000000..5500346a --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/left-ht.CORD2R_LREFAX @@ -0,0 +1,41 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ direction cosine matrix +$ ex ey ez +$ 0.95664 0.29126 -0.00000 +$ 0.29126 -0.95664 0.00000 +$ -0.00000 -0.00000 -1.00000 +$ +$ +CORD2R 3339001 0 18.9631 -2.9-18 1.86699 18.9631 3.38-17 .866999+ ++ 19.4415 .145628 1.36699 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-ht/left-ht.DMI_W2GJb b/doc/tutorials/DC3_model/fem/left-ht/left-ht.DMI_W2GJb new file mode 100644 index 00000000..96889584 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/left-ht.DMI_W2GJb @@ -0,0 +1,35 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ W2GJ factors - profile camber line and twist +$ +DMI W2GJ 0 2 1 0 74 1 +DMI W2GJ 1 1 0.000+0 2 0.000+0 3 0.000+0 + 4 0.000+0 5 0.000+0 6 0.000+0 7 0.000+0 + 8 0.000+0 9 0.000+0 10 0.000+0 11 0.000+0 + 12 0.000+0 13 0.000+0 14 0.000+0 15 0.000+0 + 16 0.000+0 17 0.000+0 18 0.000+0 19 0.000+0 + 20 0.000+0 21 0.000+0 22 0.000+0 23 0.000+0 + 24 0.000+0 25 0.000+0 26 0.000+0 27 0.000+0 + 28 0.000+0 29 0.000+0 30 0.000+0 31 0.000+0 + 32 0.000+0 33 0.000+0 34 0.000+0 35 0.000+0 + 36 0.000+0 37 0.000+0 38 0.000+0 39 0.000+0 + 40 0.000+0 41 0.000+0 42 0.000+0 43 0.000+0 + 44 0.000+0 45 0.000+0 46 0.000+0 47 0.000+0 + 48 0.000+0 49 0.000+0 50 0.000+0 51 0.000+0 + 52 0.000+0 53 0.000+0 54 0.000+0 55 0.000+0 + 56 0.000+0 57 0.000+0 58 0.000+0 59 0.000+0 + 60 0.000+0 61 0.000+0 62 0.000+0 63 0.000+0 + 64 0.000+0 65 0.000+0 66 0.000+0 67 0.000+0 + 68 0.000+0 69 0.000+0 70 0.000+0 71 0.000+0 + 72 0.000+0 73 0.000+0 74 0.000+0 diff --git a/doc/tutorials/DC3_model/fem/left-ht/left-ht.GRID_LREFAX_3330001 b/doc/tutorials/DC3_model/fem/left-ht/left-ht.GRID_LREFAX_3330001 new file mode 100644 index 00000000..0d502be4 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/left-ht.GRID_LREFAX_3330001 @@ -0,0 +1,40 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ GRIDs of load reference axis defined by input card LREFAX 3330001 +$ +GRID 33390001 18.9631-2.99-18 1.86699 +GRID 33390002 19.1360-.567768 1.86699 +GRID 33390003 19.3204-1.17339 1.86699 +GRID 33390004 19.5048-1.77901 1.86699 +GRID 33390005 19.6892-2.38462 1.86699 +GRID 33390006 19.8736-2.99025 1.86699 +GRID 33390007 20.0579-3.59587 1.86699 +GRID 33390008 20.2436-4.20568 1.86699 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-ht/left-ht.MAT_ZR b/doc/tutorials/DC3_model/fem/left-ht/left-ht.MAT_ZR new file mode 100644 index 00000000..85728ea1 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/left-ht.MAT_ZR @@ -0,0 +1,31 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +MAT1 333001 7.00+10 2.69+10 0.3 0.0 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-ht/left-ht.RBE2_LREFAX_3330001 b/doc/tutorials/DC3_model/fem/left-ht/left-ht.RBE2_LREFAX_3330001 new file mode 100644 index 00000000..ba136897 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-ht/left-ht.RBE2_LREFAX_3330001 @@ -0,0 +1,54 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:30 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +GRID 33390201 21.0049-5.94-18 1.86699 +GRID 33390101 18.0200-5.94-18 1.86699 +RBE2 3339010133390001123456 3339010133390201 +GRID 33390202 21.0049-.567768 1.86699 +GRID 33390102 18.2727-.567768 1.86699 +RBE2 3339010233390002123456 3339010233390202 +GRID 33390203 21.0049-1.17339 1.86699 +GRID 33390103 18.5422-1.17339 1.86699 +RBE2 3339010333390003123456 3339010333390203 +GRID 33390204 21.0049-1.77901 1.86699 +GRID 33390104 18.8118-1.77901 1.86699 +RBE2 3339010433390004123456 3339010433390204 +GRID 33390205 21.0049-2.38462 1.86699 +GRID 33390105 19.0814-2.38462 1.86699 +RBE2 3339010533390005123456 3339010533390205 +GRID 33390206 21.0049-2.99025 1.86699 +GRID 33390106 19.3509-2.99025 1.86699 +RBE2 3339010633390006123456 3339010633390206 +GRID 33390207 21.0049-3.59587 1.86699 +GRID 33390107 19.6205-3.59587 1.86699 +RBE2 3339010733390007123456 3339010733390207 +GRID 33390208 21.0049-4.20568 1.86699 +GRID 33390108 19.8919-4.20568 1.86699 +RBE2 3339010833390008123456 3339010833390208 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-wing/export_left-wing.csv b/doc/tutorials/DC3_model/fem/left-wing/export_left-wing.csv new file mode 100644 index 00000000..6a9190e2 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-wing/export_left-wing.csv @@ -0,0 +1,144 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ Left-w +$CBAR EID PID GA GB X1 X2 X3 + +$+ w1A w2A w3A w1B w2B w3B +CBAR 5408001 54080015409000154090002 0.0000 0.0000 1.0000 +CBAR 5408002 54080025409000254090003 0.0000 0.0000 1.0000 +CBAR 5408003 54080035409000354090004 0.0000 0.0000 1.0000 +CBAR 5408004 54080045409000454090005 0.0000 0.0000 1.0000 +CBAR 5408005 54080055409000554090006 0.0000 0.0000 1.0000 +CBAR 5408006 54080065409000654090007 0.0000 0.0000 1.0000 +CBAR 5408007 54080075409000754090008 0.0000 0.0000 1.0000 +CBAR 5408008 54080085409000854090009 0.0000 0.0000 1.0000 +CBAR 5408009 54080095409000954090010 0.0000 0.0000 1.0000 +CBAR 5408010 54080105409001054090011 0.0000 0.0000 1.0000 +CBAR 5408011 54080115409001154090012 0.0000 0.0000 1.0000 +CBAR 5408012 54080125409001254090013 0.0000 0.0000 1.0000 +CBAR 5408013 54080135409001354090014 0.0000 0.0000 1.0000 +CBAR 5408014 54080145409001454090015 0.0000 0.0000 1.0000 +CBAR 5408015 54080155409001554090016 0.0000 0.0000 1.0000 +CBAR 5408016 54080165409001654090017 0.0000 0.0000 1.0000 +CBAR 5408017 54080175409001754090018 0.0000 0.0000 1.0000 +CBAR 5408018 54080185409001854090019 0.0000 0.0000 1.0000 +CBAR 5408019 54080195409001954090020 0.0000 0.0000 1.0000 +CBAR 5408020 54080205409002054090021 0.0000 0.0000 1.0000 +CBAR 5408021 54080215409002154090022 0.0000 0.0000 1.0000 +CBAR 5408022 54080225409002254090023 0.0000 0.0000 1.0000 +CBAR 5408023 54080235409002354090024 0.0000 0.0000 1.0000 +CBAR 5408024 54080245409002454090025 0.0000 0.0000 1.0000 +CBAR 5408025 54080255409002554090026 0.0000 0.0000 1.0000 +CBAR 5408026 54080265409002654090027 0.0000 0.0000 1.0000 +CBAR 5408027 54080275409002754090028 0.0000 0.0000 1.0000 +CBAR 5408028 54080285409002854090029 0.0000 0.0000 1.0000 +CBAR 5408029 54080295409002954090030 0.0000 0.0000 1.0000 +CBAR 5408030 54080305409003054090031 0.0000 0.0000 1.0000 + + + + + + + + +$ PBAR PID MID A I1 I2 J NSM +PBAR 5408001 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408002 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408003 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408004 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408005 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408006 5400013.553E-24.376E-42.553E-32.663E-3 0.0000 +PBAR 5408007 5400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 5408008 5400013.446E-24.039E-42.403E-32.504E-3 0.0000 +PBAR 5408009 5400013.237E-23.410E-42.116E-32.201E-3 0.0000 +PBAR 5408010 5400013.034E-22.866E-41.857E-31.928E-3 0.0000 +PBAR 5408011 5400012.838E-22.396E-41.622E-31.682E-3 0.0000 +PBAR 5408012 5400012.650E-21.994E-41.412E-31.462E-3 0.0000 +PBAR 5408013 5400012.468E-21.649E-41.223E-31.264E-3 0.0000 +PBAR 5408014 5400012.293E-21.356E-41.054E-31.088E-3 0.0000 +PBAR 5408015 5400012.125E-21.108E-49.038E-49.315E-4 0.0000 +PBAR 5408016 5400011.964E-28.989E-57.706E-47.931E-4 0.0000 +PBAR 5408017 5400011.810E-27.242E-56.531E-46.712E-4 0.0000 +PBAR 5408018 5400011.662E-25.788E-55.499E-45.644E-4 0.0000 +PBAR 5408019 5400011.521E-24.589E-54.597E-44.712E-4 0.0000 +PBAR 5408020 5400011.386E-23.605E-53.813E-43.904E-4 0.0000 +PBAR 5408021 5400011.258E-22.805E-53.137E-43.207E-4 0.0000 +PBAR 5408022 5400011.137E-22.159E-52.556E-42.610E-4 0.0000 +PBAR 5408023 5400011.022E-21.642E-52.062E-42.103E-4 0.0000 +PBAR 5408024 5400019.136E-31.234E-51.644E-41.675E-4 0.0000 +PBAR 5408025 5400018.114E-39.140E-61.295E-41.318E-4 0.0000 +PBAR 5408026 5400017.156E-36.660E-61.005E-41.022E-4 0.0000 +PBAR 5408027 5400016.260E-34.780E-67.681E-57.801E-5 0.0000 +PBAR 5408028 5400015.426E-33.360E-65.762E-55.846E-5 0.0000 +PBAR 5408029 5400014.654E-32.300E-64.233E-54.291E-5 0.0000 +PBAR 5408030 5400013.943E-31.600E-63.035E-53.074E-5 0.0000 + + + + + +$ Left wing mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 541000154090001 -1 59.720 10.5112 0.0000 0.1747 + + 0.00 0.00 37.9722 0.000 0.0000 0.0000 +CONM2 541000254090002 -1 56.440 10.5112 -0.5262 0.1747 + + 0.00 0.00 75.9433 0.000 0.0000 0.0000 +CONM2 541000354090003 -1 55.440 10.5112 -1.0525 0.1747 + + 0.00 0.00 75.9433 0.000 0.0000 0.0000 +CONM2 541000454090004 -1 55.440 10.5112 -1.5787 0.1747 + + 0.00 0.00 75.6792 0.000 0.0000 0.0000 +CONM2 541000554090005 -1 55.440 10.5112 -2.1013 0.1747 + + 0.00 0.00 66.3853 0.000 0.0000 0.0000 +CONM2 541000654090006 -1 55.440 10.5112 -2.4987 0.1747 + + 0.00 0.00 75.9448 0.000 0.0000 0.0000 +CONM2 541000754090007 -1 50.440 10.5112 -3.1537 0.1747 + + 0.00 0.00 85.2402 0.000 0.0000 0.0000 +CONM2 541000854090008 -1 50.440 10.5112 -3.6800 0.1747 + + 0.00 0.00 69.5011 0.000 0.0000 0.0000 +CONM2 541000954090009 -1 46.711 10.3220 -4.1169 0.2079 + + 0.00 0.00 55.5330 0.000 0.0000 0.0000 +CONM2 541001054090010 -1 40.074 10.5521 -4.5539 0.2410 + + 0.00 0.00 48.7313 0.000 0.0000 0.0000 +CONM2 541001154090011 -1 37.527 10.5726 -4.9908 0.2741 + + 0.00 0.00 42.5973 0.000 0.0000 0.0000 +CONM2 541001254090012 -1 35.070 10.5930 -5.4277 0.3072 + + 0.00 0.00 37.0827 0.000 0.0000 0.0000 +CONM2 541001354090013 -1 22.702 10.6135 -5.8646 0.3403 + + 0.00 0.00 32.1402 0.000 0.0000 0.0000 +CONM2 541001454090014 -1 21.422 10.6339 -6.3016 0.3735 + + 0.00 0.00 27.7267 0.000 0.0000 0.0000 +CONM2 541001554090015 -1 19.231 10.6543 -6.7385 0.4066 + + 0.00 0.00 23.7991 0.000 0.0000 0.0000 +CONM2 541001654090016 -1 18.126 10.6748 -7.1754 0.4397 + + 0.00 0.00 20.3172 0.000 0.0000 0.0000 +CONM2 541001754090017 -1 16.108 10.6952 -7.6123 0.4728 + + 0.00 0.00 17.2447 0.000 0.0000 0.0000 +CONM2 541001854090018 -1 15.177 10.7157 -8.0493 0.5059 + + 0.00 0.00 14.5451 0.000 0.0000 0.0000 +CONM2 541001954090019 -1 13.330 10.7361 -8.4862 0.5391 + + 0.00 0.00 12.1843 0.000 0.0000 0.0000 +CONM2 541002054090020 -1 12.569 10.7566 -8.9231 0.5722 + + 0.00 0.00 10.1313 0.000 0.0000 0.0000 +CONM2 541002154090021 -1 10.891 10.7770 -9.3600 0.6053 + + 0.00 0.00 8.3566 0.000 0.0000 0.0000 +CONM2 541002254090022 -1 10.297 10.7974 -9.7970 0.6384 + + 0.00 0.00 6.8312 0.000 0.0000 0.0000 +CONM2 541002354090023 -1 8.786 10.8179-10.2338 0.6715 + + 0.00 0.00 5.5302 0.000 0.0000 0.0000 +CONM2 541002454090024 -1 8.357 10.8383-10.6707 0.7047 + + 0.00 0.00 4.4292 0.000 0.0000 0.0000 +CONM2 541002554090025 -1 7.010 10.8587-11.1076 0.7378 + + 0.00 0.00 3.5048 0.000 0.0000 0.0000 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Operating System: Linux +$ Date & Time: 2024-03-28 15:15:54 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ direction cosine matrix +$ ex ey ez +$ 1.00000 -0.00000 -0.00000 +$ -0.00000 -1.00000 0.00000 +$ -0.00000 -0.00000 -1.00000 +$ +$ +CORD2R 5409001 0 8.01838 -5.9-18 .197264 8.01838 1.56-18 -.80273+ ++ 8.51838 -4.8-16 -.30273 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-wing/left-wing.DMI_W2GJb b/doc/tutorials/DC3_model/fem/left-wing/left-wing.DMI_W2GJb new file mode 100644 index 00000000..f0393ffc --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-wing/left-wing.DMI_W2GJb @@ -0,0 +1,123 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:51 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ W2GJ factors - profile camber line and twist +$ +DMI W2GJ 0 2 1 0 424 1 +DMI W2GJ 1 1-.094282 2-.031148 3.0319988 + 4.0385214 5.0449264 6.0509660 7.0568410 + 8.0625353 9.0681444 10.0737672 11.0793890 + 12.0850309 13-.094282 14-.031148 15.0319988 + 16.0385214 17.0449264 18.0509660 19.0568410 + 20.0625353 21.0681444 22.0737672 23.0793890 + 24.0850309 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a/doc/tutorials/DC3_model/fem/left-wing/left-wing.GRID_LREFAX_5400001 b/doc/tutorials/DC3_model/fem/left-wing/left-wing.GRID_LREFAX_5400001 new file mode 100644 index 00000000..1f54d293 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-wing/left-wing.GRID_LREFAX_5400001 @@ -0,0 +1,63 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:55 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ GRIDs of load reference axis defined by input card LREFAX 5400001 +$ +GRID 54090001 8.01838-5.97-18 .197264 +GRID 54090002 8.01838-.526238 .197264 +GRID 54090003 8.01838-1.05246 .197264 +GRID 54090004 8.01838-1.57870 .197264 +GRID 54090005 8.01838-2.10126 .197264 +GRID 54090006 8.01838-2.49870 .197264 +GRID 54090007 8.01838-3.15374 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+$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-wing/left-wing.MAT_ZR b/doc/tutorials/DC3_model/fem/left-wing/left-wing.MAT_ZR new file mode 100644 index 00000000..a751db24 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-wing/left-wing.MAT_ZR @@ -0,0 +1,31 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:55 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +MAT1 540001 7.00+10 2.69+10 0.3 0.0 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/left-wing/left-wing.RBE2_LREFAX_5400001 b/doc/tutorials/DC3_model/fem/left-wing/left-wing.RBE2_LREFAX_5400001 new file mode 100644 index 00000000..9ed84808 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/left-wing/left-wing.RBE2_LREFAX_5400001 @@ -0,0 +1,123 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:55 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +GRID 54090201 11.2100-1.11-15 .150999 +GRID 54090101 6.88999-1.11-15 .150999 +RBE2 5409010154090001123456 5409010154090201 +GRID 54090202 11.2100-.526238 .150999 +GRID 54090102 6.88999-.526238 .150999 +RBE2 5409010254090002123456 5409010254090202 +GRID 54090203 11.2100-1.05246 .150999 +GRID 54090103 6.88999-1.05246 .150999 +RBE2 5409010354090003123456 5409010354090203 +GRID 54090204 11.2100-1.57870 .150999 +GRID 54090104 6.88999-1.57870 .150999 +RBE2 5409010454090004123456 5409010454090204 +GRID 54090205 11.2100-2.10126 .150999 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9.53730-12.8542 .861207 +RBE2 5409012954090029123456 5409012954090229 +GRID 54090230 11.2100-13.2908 .895006 +GRID 54090130 9.66328-13.2908 .895006 +RBE2 5409013054090030123456 5409013054090230 +GRID 54090231 11.2100-13.7254 .928650 +GRID 54090131 9.78999-13.7299 .929000 +RBE2 5409013154090031123456 5409013154090231 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-ht/export_right-ht.csv b/doc/tutorials/DC3_model/fem/right-ht/export_right-ht.csv new file mode 100644 index 00000000..5c3122e1 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/export_right-ht.csv @@ -0,0 +1,45 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$right-h +$CBAR EID PID GA GB X1 X2 X3 + +$+ w1A w2A w3A w1B w2B w3B +CBAR 3348001 33480013349000133490002 0.0000 0.0000 1.0000 +CBAR 3348002 33480023349000233490003 0.0000 0.0000 1.0000 +CBAR 3348003 33480033349000333490004 0.0000 0.0000 1.0000 +CBAR 3348004 33480043349000433490005 0.0000 0.0000 1.0000 +CBAR 3348005 33480053349000533490006 0.0000 0.0000 1.0000 +CBAR 3348006 33480063349000633490007 0.0000 0.0000 1.0000 +CBAR 3348007 33480073349000733490008 0.0000 0.0000 1.0000 + + + + +$ PBAR PID MID A I1 I2 J NSM +PBAR 3348001 3340011.214E-22.000E-49.420E-39.590E-3 0.000 +PBAR 3348002 3340011.001E-29.633E-56.430E-36.540E-3 0.000 +PBAR 3348003 3340018.032E-36.214E-54.149E-34.220E-3 0.000 +PBAR 3348004 3340016.268E-33.796E-52.537E-32.581E-3 0.000 +PBAR 3348005 3340014.724E-32.164E-51.449E-31.474E-3 0.000 +PBAR 3348006 3340013.399E-31.132E-57.500E-47.690E-4 0.000 +PBAR 3348007 3340012.290E-35.236E-63.431E-43.547E-4 0.000 + + +$ right ht mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 334810133490001 0 10.663 0.00 0.00 0.00 + + 0.00 0.00 8.6823 0.000 0.00 0.00 +CONM2 334810233490002 0 17.841 0.00 0.00 0.00 + + 0.00 0.00 12.5584 0.000 0.00 0.00 +CONM2 334810333490003 0 14.466 0.00 0.00 0.00 + + 0.00 0.00 8.5231 0.000 0.00 0.00 +CONM2 334810433490004 0 11.445 0.00 0.00 0.00 + + 0.00 0.00 5.3348 0.000 0.00 0.00 +CONM2 334810533490005 0 8.777 0.00 0.00 0.00 + + 0.00 0.00 3.1377 0.000 0.00 0.00 +CONM2 334810633490006 0 6.463 0.00 0.00 0.00 + + 0.00 0.00 1.7012 0.000 0.00 0.00 +CONM2 334810733490007 0 4.502 0.00 0.00 0.00 + + 0.00 0.00 0.8284 0.000 0.00 0.00 +CONM2 334810833490008 0 1.442 0.00 0.00 0.00 + + 0.00 0.00 0.1707 0.000 0.00 0.00 diff --git a/doc/tutorials/DC3_model/fem/right-ht/right-ht.CORD2R_LREFAX b/doc/tutorials/DC3_model/fem/right-ht/right-ht.CORD2R_LREFAX new file mode 100644 index 00000000..04d1448d --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/right-ht.CORD2R_LREFAX @@ -0,0 +1,41 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ direction cosine matrix +$ ex ey ez +$ 0.95664 0.29126 -0.00000 +$ -0.29126 0.95664 0.00000 +$ 0.00000 -0.00000 1.00000 +$ +$ +CORD2R 3349001 0 18.9631 2.99-18 1.86699 18.9631 6.27-17 2.86700+ ++ 19.4415 -.14562 2.36700 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-ht/right-ht.DMI_W2GJb b/doc/tutorials/DC3_model/fem/right-ht/right-ht.DMI_W2GJb new file mode 100644 index 00000000..8c652947 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/right-ht.DMI_W2GJb @@ -0,0 +1,35 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ W2GJ factors - profile camber line and twist +$ +DMI W2GJ 0 2 1 0 74 1 +DMI W2GJ 1 1 0.000+0 2 0.000+0 3 0.000+0 + 4 0.000+0 5 0.000+0 6 0.000+0 7 0.000+0 + 8 0.000+0 9 0.000+0 10 0.000+0 11 0.000+0 + 12 0.000+0 13 0.000+0 14 0.000+0 15 0.000+0 + 16 0.000+0 17 0.000+0 18 0.000+0 19 0.000+0 + 20 0.000+0 21 0.000+0 22 0.000+0 23 0.000+0 + 24 0.000+0 25 0.000+0 26 0.000+0 27 0.000+0 + 28 0.000+0 29 0.000+0 30 0.000+0 31 0.000+0 + 32 0.000+0 33 0.000+0 34 0.000+0 35 0.000+0 + 36 0.000+0 37 0.000+0 38 0.000+0 39 0.000+0 + 40 0.000+0 41 0.000+0 42 0.000+0 43 0.000+0 + 44 0.000+0 45 0.000+0 46 0.000+0 47 0.000+0 + 48 0.000+0 49 0.000+0 50 0.000+0 51 0.000+0 + 52 0.000+0 53 0.000+0 54 0.000+0 55 0.000+0 + 56 0.000+0 57 0.000+0 58 0.000+0 59 0.000+0 + 60 0.000+0 61 0.000+0 62 0.000+0 63 0.000+0 + 64 0.000+0 65 0.000+0 66 0.000+0 67 0.000+0 + 68 0.000+0 69 0.000+0 70 0.000+0 71 0.000+0 + 72 0.000+0 73 0.000+0 74 0.000+0 diff --git a/doc/tutorials/DC3_model/fem/right-ht/right-ht.GRID_LREFAX_3340001 b/doc/tutorials/DC3_model/fem/right-ht/right-ht.GRID_LREFAX_3340001 new file mode 100644 index 00000000..0e18f84f --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/right-ht.GRID_LREFAX_3340001 @@ -0,0 +1,40 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ GRIDs of load reference axis defined by input card LREFAX 3340001 +$ +GRID 33490001 18.9631 2.99-18 1.86699 +GRID 33490002 19.1360 .567768 1.86699 +GRID 33490003 19.3204 1.17339 1.86699 +GRID 33490004 19.5048 1.77901 1.86699 +GRID 33490005 19.6892 2.38462 1.86699 +GRID 33490006 19.8736 2.99025 1.86699 +GRID 33490007 20.0579 3.59587 1.86699 +GRID 33490008 20.2436 4.20568 1.86699 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-ht/right-ht.MAT_ZR b/doc/tutorials/DC3_model/fem/right-ht/right-ht.MAT_ZR new file mode 100644 index 00000000..b9a09297 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/right-ht.MAT_ZR @@ -0,0 +1,31 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +MAT1 334001 7.00+10 2.69+10 0.3 0.0 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-ht/right-ht.RBE2_LREFAX_3340001 b/doc/tutorials/DC3_model/fem/right-ht/right-ht.RBE2_LREFAX_3340001 new file mode 100644 index 00000000..7a60bfb3 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-ht/right-ht.RBE2_LREFAX_3340001 @@ -0,0 +1,54 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-21 10:03:49 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +GRID 33490201 21.0049 5.94-18 1.86699 +GRID 33490101 18.0200 5.94-18 1.86699 +RBE2 3349010133490001123456 3349010133490201 +GRID 33490202 21.0049 .567768 1.86699 +GRID 33490102 18.2727 .567768 1.86699 +RBE2 3349010233490002123456 3349010233490202 +GRID 33490203 21.0049 1.17339 1.86699 +GRID 33490103 18.5422 1.17339 1.86699 +RBE2 3349010333490003123456 3349010333490203 +GRID 33490204 21.0049 1.77901 1.86699 +GRID 33490104 18.8118 1.77901 1.86699 +RBE2 3349010433490004123456 3349010433490204 +GRID 33490205 21.0049 2.38462 1.86699 +GRID 33490105 19.0814 2.38462 1.86699 +RBE2 3349010533490005123456 3349010533490205 +GRID 33490206 21.0049 2.99025 1.86699 +GRID 33490106 19.3509 2.99025 1.86699 +RBE2 3349010633490006123456 3349010633490206 +GRID 33490207 21.0049 3.59587 1.86699 +GRID 33490107 19.6205 3.59587 1.86699 +RBE2 3349010733490007123456 3349010733490207 +GRID 33490208 21.0049 4.20568 1.86699 +GRID 33490108 19.8919 4.20568 1.86699 +RBE2 3349010833490008123456 3349010833490208 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-wing/export_right-wing.csv b/doc/tutorials/DC3_model/fem/right-wing/export_right-wing.csv new file mode 100644 index 00000000..30cb9c51 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/export_right-wing.csv @@ -0,0 +1,144 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ Right- +$CBAR EID PID GA GB X1 X2 X3 + +$+ w1A w2A w3A w1B w2B w3B +CBAR 6408001 64080016409000164090002 0.0000 0.0000 1.0000 +CBAR 6408002 64080026409000264090003 0.0000 0.0000 1.0000 +CBAR 6408003 64080036409000364090004 0.0000 0.0000 1.0000 +CBAR 6408004 64080046409000464090005 0.0000 0.0000 1.0000 +CBAR 6408005 64080056409000564090006 0.0000 0.0000 1.0000 +CBAR 6408006 64080066409000664090007 0.0000 0.0000 1.0000 +CBAR 6408007 64080076409000764090008 0.0000 0.0000 1.0000 +CBAR 6408008 64080086409000864090009 0.0000 0.0000 1.0000 +CBAR 6408009 64080096409000964090010 0.0000 0.0000 1.0000 +CBAR 6408010 64080106409001064090011 0.0000 0.0000 1.0000 +CBAR 6408011 64080116409001164090012 0.0000 0.0000 1.0000 +CBAR 6408012 64080126409001264090013 0.0000 0.0000 1.0000 +CBAR 6408013 64080136409001364090014 0.0000 0.0000 1.0000 +CBAR 6408014 64080146409001464090015 0.0000 0.0000 1.0000 +CBAR 6408015 64080156409001564090016 0.0000 0.0000 1.0000 +CBAR 6408016 64080166409001664090017 0.0000 0.0000 1.0000 +CBAR 6408017 64080176409001764090018 0.0000 0.0000 1.0000 +CBAR 6408018 64080186409001864090019 0.0000 0.0000 1.0000 +CBAR 6408019 64080196409001964090020 0.0000 0.0000 1.0000 +CBAR 6408020 64080206409002064090021 0.0000 0.0000 1.0000 +CBAR 6408021 64080216409002164090022 0.0000 0.0000 1.0000 +CBAR 6408022 64080226409002264090023 0.0000 0.0000 1.0000 +CBAR 6408023 64080236409002364090024 0.0000 0.0000 1.0000 +CBAR 6408024 64080246409002464090025 0.0000 0.0000 1.0000 +CBAR 6408025 64080256409002564090026 0.0000 0.0000 1.0000 +CBAR 6408026 64080266409002664090027 0.0000 0.0000 1.0000 +CBAR 6408027 64080276409002764090028 0.0000 0.0000 1.0000 +CBAR 6408028 64080286409002864090029 0.0000 0.0000 1.0000 +CBAR 6408029 64080296409002964090030 0.0000 0.0000 1.0000 +CBAR 6408030 64080306409003064090031 0.0000 0.0000 1.0000 + + + + + + + + +$ PBAR PID MID A I1 I2 J NSM +PBAR 6408001 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408002 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408003 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408004 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408005 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408006 6400013.553E-24.376E-42.553E-32.663E-3 0.0000 +PBAR 6408007 6400013.553E-24.375E-42.553E-32.663E-3 0.0000 +PBAR 6408008 6400013.446E-24.039E-42.403E-32.504E-3 0.0000 +PBAR 6408009 6400013.237E-23.410E-42.116E-32.201E-3 0.0000 +PBAR 6408010 6400013.034E-22.866E-41.857E-31.928E-3 0.0000 +PBAR 6408011 6400012.838E-22.396E-41.622E-31.682E-3 0.0000 +PBAR 6408012 6400012.650E-21.994E-41.412E-31.462E-3 0.0000 +PBAR 6408013 6400012.468E-21.649E-41.223E-31.264E-3 0.0000 +PBAR 6408014 6400012.293E-21.356E-41.054E-31.088E-3 0.0000 +PBAR 6408015 6400012.125E-21.108E-49.038E-49.315E-4 0.0000 +PBAR 6408016 6400011.964E-28.989E-57.706E-47.931E-4 0.0000 +PBAR 6408017 6400011.810E-27.242E-56.531E-46.712E-4 0.0000 +PBAR 6408018 6400011.662E-25.788E-55.499E-45.644E-4 0.0000 +PBAR 6408019 6400011.521E-24.589E-54.597E-44.712E-4 0.0000 +PBAR 6408020 6400011.386E-23.605E-53.813E-43.904E-4 0.0000 +PBAR 6408021 6400011.258E-22.805E-53.137E-43.207E-4 0.0000 +PBAR 6408022 6400011.137E-22.159E-52.556E-42.610E-4 0.0000 +PBAR 6408023 6400011.022E-21.642E-52.062E-42.103E-4 0.0000 +PBAR 6408024 6400019.136E-31.234E-51.644E-41.675E-4 0.0000 +PBAR 6408025 6400018.114E-39.140E-61.295E-41.318E-4 0.0000 +PBAR 6408026 6400017.156E-36.660E-61.005E-41.022E-4 0.0000 +PBAR 6408027 6400016.260E-34.780E-67.681E-57.801E-5 0.0000 +PBAR 6408028 6400015.426E-33.360E-65.762E-55.846E-5 0.0000 +PBAR 6408029 6400014.654E-32.300E-64.233E-54.291E-5 0.0000 +PBAR 6408030 6400013.943E-31.600E-63.035E-53.074E-5 0.0000 + + + + + +$ right wing mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 641000164090001 -1 59.720 10.5112 0.0000 0.1747 + + 0.00 0.00 37.9722 0.000 0.0000 0.0000 +CONM2 641000264090002 -1 56.440 10.5112 0.5262 0.1747 + + 0.00 0.00 75.9433 0.000 0.0000 0.0000 +CONM2 641000364090003 -1 55.440 10.5112 1.0525 0.1747 + + 0.00 0.00 75.9433 0.000 0.0000 0.0000 +CONM2 641000464090004 -1 55.440 10.5112 1.5787 0.1747 + + 0.00 0.00 75.6792 0.000 0.0000 0.0000 +CONM2 641000564090005 -1 55.440 10.5112 2.1013 0.1747 + + 0.00 0.00 66.3853 0.000 0.0000 0.0000 +CONM2 641000664090006 -1 55.440 10.5112 2.4987 0.1747 + + 0.00 0.00 75.9448 0.000 0.0000 0.0000 +CONM2 641000764090007 -1 50.440 10.5112 3.1537 0.1747 + + 0.00 0.00 85.2402 0.000 0.0000 0.0000 +CONM2 641000864090008 -1 50.440 10.5112 3.6800 0.1747 + + 0.00 0.00 69.5011 0.000 0.0000 0.0000 +CONM2 641000964090009 -1 46.711 10.3220 4.1169 0.2079 + + 0.00 0.00 55.5330 0.000 0.0000 0.0000 +CONM2 641001064090010 -1 40.074 10.5521 4.5539 0.2410 + + 0.00 0.00 48.7313 0.000 0.0000 0.0000 +CONM2 641001164090011 -1 37.527 10.5726 4.9908 0.2741 + + 0.00 0.00 42.5973 0.000 0.0000 0.0000 +CONM2 641001264090012 -1 35.070 10.5930 5.4277 0.3072 + + 0.00 0.00 37.0827 0.000 0.0000 0.0000 +CONM2 641001364090013 -1 22.702 10.6135 5.8646 0.3403 + + 0.00 0.00 32.1402 0.000 0.0000 0.0000 +CONM2 641001464090014 -1 21.422 10.6339 6.3016 0.3735 + + 0.00 0.00 27.7267 0.000 0.0000 0.0000 +CONM2 641001564090015 -1 19.231 10.6543 6.7385 0.4066 + + 0.00 0.00 23.7991 0.000 0.0000 0.0000 +CONM2 641001664090016 -1 18.126 10.6748 7.1754 0.4397 + + 0.00 0.00 20.3172 0.000 0.0000 0.0000 +CONM2 641001764090017 -1 16.108 10.6952 7.6123 0.4728 + + 0.00 0.00 17.2447 0.000 0.0000 0.0000 +CONM2 641001864090018 -1 15.177 10.7157 8.0493 0.5059 + + 0.00 0.00 14.5451 0.000 0.0000 0.0000 +CONM2 641001964090019 -1 13.330 10.7361 8.4862 0.5391 + + 0.00 0.00 12.1843 0.000 0.0000 0.0000 +CONM2 641002064090020 -1 12.569 10.7566 8.9231 0.5722 + + 0.00 0.00 10.1313 0.000 0.0000 0.0000 +CONM2 641002164090021 -1 10.891 10.7770 9.3600 0.6053 + + 0.00 0.00 8.3566 0.000 0.0000 0.0000 +CONM2 641002264090022 -1 10.297 10.7974 9.7970 0.6384 + + 0.00 0.00 6.8312 0.000 0.0000 0.0000 +CONM2 641002364090023 -1 8.786 10.8179 10.2338 0.6715 + + 0.00 0.00 5.5302 0.000 0.0000 0.0000 +CONM2 641002464090024 -1 8.357 10.8383 10.6707 0.7047 + + 0.00 0.00 4.4292 0.000 0.0000 0.0000 +CONM2 641002564090025 -1 7.010 10.8587 11.1076 0.7378 + + 0.00 0.00 3.5048 0.000 0.0000 0.0000 +CONM2 6410012664090026 -1 6.744 10.8792 11.5445 0.7709 + + 0.00 0.00 2.7359 0.000 0.0000 0.0000 +CONM2 641002764090027 -1 5.558 10.8996 11.9813 0.8040 + + 0.00 0.00 2.1038 0.000 0.0000 0.0000 +CONM2 641002864090028 -1 4.453 10.9200 12.4182 0.8371 + + 0.00 0.00 1.5901 0.000 0.0000 0.0000 +CONM2 641002964090029 -1 4.426 10.9404 12.8550 0.8702 + + 0.00 0.00 1.1782 0.000 0.0000 0.0000 +CONM2 641003064090030 -1 3.478 10.9608 13.2918 0.9034 + + 0.00 0.00 0.8524 0.000 0.0000 0.0000 +CONM2 641003164090031 -1 1.304 10.9811 13.7276 0.9364 + + 0.00 0.00 0.3002 0.000 0.0000 0.0000 diff --git a/doc/tutorials/DC3_model/fem/right-wing/right-wing.CORD2R_LREFAX b/doc/tutorials/DC3_model/fem/right-wing/right-wing.CORD2R_LREFAX new file mode 100644 index 00000000..a37df2bd --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/right-wing.CORD2R_LREFAX @@ -0,0 +1,41 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:19 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ direction cosine matrix +$ ex ey ez +$ 1.00000 -0.00000 -0.00000 +$ 0.00000 1.00000 0.00000 +$ 0.00000 -0.00000 1.00000 +$ +$ +CORD2R 6409001 0 8.01838 5.97-18 .197264 8.01838 1.35-17 1.19726+ ++ 8.51838 4.92-16 .697264 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-wing/right-wing.DMI_W2GJb b/doc/tutorials/DC3_model/fem/right-wing/right-wing.DMI_W2GJb new file mode 100644 index 00000000..f40a2179 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/right-wing.DMI_W2GJb @@ -0,0 +1,123 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:16 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ W2GJ factors - profile camber line and twist +$ +DMI W2GJ 0 2 1 0 424 1 +DMI W2GJ 1 1-.094282 2-.031148 3.0319988 + 4.0385214 5.0449264 6.0509660 7.0568410 + 8.0625353 9.0681444 10.0737672 11.0793890 + 12.0850309 13-.094282 14-.031148 15.0319988 + 16.0385214 17.0449264 18.0509660 19.0568410 + 20.0625353 21.0681444 22.0737672 23.0793890 + 24.0850309 25-.094282 26-.031148 27.0319988 + 28.0385214 29.0449264 30.0509660 31.0568410 + 32.0625353 33.0681444 34.0737672 35.0793890 + 36.0850309 37-.094282 38-.031148 39.0319988 + 40.0385214 41.0449264 42.0509660 43.0568410 + 44.0625353 45.0681444 46.0737672 47.0793890 + 48.0850309 49-.094282 50-.031148 51.0319988 + 52.0385214 53.0449264 54.0509660 55.0568410 + 56.0625353 57.0681444 58.0737672 59.0793890 + 60.0850309 61-.094282 62-.031148 63.0319988 + 64.0385214 65.0449264 66.0509660 67.0568410 + 68.0625353 69.0681444 70.0737672 71.0793890 + 72.0850309 73-.094282 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236-.043914 237.0241712 238.0367182 239.0422711 + 240.0475366 241.0526148 242.0575850 243.0625221 + 244.0673892 245-.102007 246-.043691 247.0245603 + 248.0368518 249.0423634 250.0475996 251.0526580 + 252.0576094 253.0625346 254.0673898 255-.102515 + 256-.043447 257.0249856 258.0369979 259.0424643 + 260.0476684 261.0527053 262.0576359 263.0625483 + 264.0673906 265-.103070 266-.043180 267.0254524 + 268.0371583 269.0425751 270.0477440 271.0527571 + 272.0576651 273.0625633 274.0673914 275-.103677 + 276-.042886 277.0259672 278.0373353 279.0426973 + 280.0478274 281.0528143 282.0576973 283.0625799 + 284.0673923 285-.104344 286-.042561 287.0265377 + 288.0375314 289.0428328 290.0479198 291.0528777 + 292.0577330 293.0625982 294.0673933 295-.105080 + 296-.042199 297.0271736 298.0377501 299.0429838 + 300.0480228 301.0529483 302.0577727 303.0626186 + 304.0673944 305-.105898 306-.041793 307.0278866 + 308.0379955 309.0431533 310.0481383 311.0530275 + 312.0578173 313.0626415 314.0673957 315-.106811 + 316-.041336 317.0286917 318.0382726 319.0433448 + 320.0482689 321.0531170 322.0578676 323.0626674 + 324.0673971 325-.107836 326-.040817 327.0296081 + 328.0385883 329.0435628 330.0484176 331.0532189 + 332.0579249 333.0626968 334.0673987 335-.108997 + 336-.040222 337.0306605 338.0389510 339.0438134 + 340.0485883 341.0533359 342.0579907 343.0627306 + 344.0674005 345.0719010 346.0763304 347.0807527 + 348.0852405 349.0718957 350.0763203 351.0807368 + 352.0852186 353.0718900 354.0763095 355.0807199 + 356.0851953 357.0718839 358.0762980 359.0807018 + 360.0851704 361.0718775 362.0762857 363.0806824 + 364.0851438 365.0718705 366.0762725 367.0806616 + 368.0851152 369.0718630 370.0762583 371.0806393 + 372.0850845 373.0718549 374.0762430 375.0806152 + 376.0850514 377.0718462 378.0762264 379.0805892 + 380.0850156 381.0718367 382.0762085 383.0805610 + 384.0849768 385.0718264 386.0761889 387.0805302 + 388.0849345 389.0718151 390.0761676 391.0804966 + 392.0848884 393.0718027 394.0761441 395.0804598 + 396.0848377 397.0717891 398.0761183 399.0804192 + 400.0847819 401.0717740 402.0760897 403.0803742 + 404.0847202 405.0717571 406.0760578 407.0803242 + 408.0846514 409.0717382 410.0760221 411.0802681 + 412.0845743 413.0717169 414.0759818 415.0802048 + 416.0844874 417.0716927 418.0759359 419.0801328 + 420.0843887 421.0716649 422.0758833 423.0800503 + 424.0842754 diff --git a/doc/tutorials/DC3_model/fem/right-wing/right-wing.GRID_LREFAX_6400001 b/doc/tutorials/DC3_model/fem/right-wing/right-wing.GRID_LREFAX_6400001 new file mode 100644 index 00000000..f968752a --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/right-wing.GRID_LREFAX_6400001 @@ -0,0 +1,63 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:19 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ GRIDs of load reference axis defined by input card LREFAX 6400001 +$ +GRID 64090001 8.01838 5.97-18 .197264 +GRID 64090002 8.01838 .526238 .197264 +GRID 64090003 8.01838 1.05246 .197264 +GRID 64090004 8.01838 1.57870 .197264 +GRID 64090005 8.01838 2.10126 .197264 +GRID 64090006 8.01838 2.49870 .197264 +GRID 64090007 8.01838 3.15374 .197264 +GRID 64090008 8.01838 3.68000 .197264 +GRID 64090009 8.11154 4.11691 .229739 +GRID 64090010 8.20471 4.55383 .262215 +GRID 64090011 8.29787 4.99074 .294691 +GRID 64090012 8.39103 5.42766 .327167 +GRID 64090013 8.48419 5.86458 .359643 +GRID 64090014 8.57735 6.30149 .392118 +GRID 64090015 8.67052 6.73841 .424594 +GRID 64090016 8.76368 7.17533 .457070 +GRID 64090017 8.85684 7.61223 .489544 +GRID 64090018 8.95000 8.04914 .522020 +GRID 64090019 9.04316 8.48605 .554495 +GRID 64090020 9.13632 8.92295 .586969 +GRID 64090021 9.22948 9.35985 .619444 +GRID 64090022 9.32264 9.79675 .651918 +GRID 64090023 9.41578 10.2335 .684389 +GRID 64090024 9.50895 10.6705 .716865 +GRID 64090025 9.60209 11.1073 .749333 +GRID 64090026 9.69525 11.5442 .781807 +GRID 64090027 9.78838 11.9810 .814273 +GRID 64090028 9.88153 12.4178 .846744 +GRID 64090029 9.97465 12.8545 .879204 +GRID 64090030 10.0677 13.2913 .911667 +GRID 64090031 10.1607 13.7275 .944090 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-wing/right-wing.MAT_ZR b/doc/tutorials/DC3_model/fem/right-wing/right-wing.MAT_ZR new file mode 100644 index 00000000..125c4054 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/right-wing.MAT_ZR @@ -0,0 +1,31 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:20 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +MAT1 640001 7.00+10 2.69+10 0.3 0.0 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/right-wing/right-wing.RBE2_LREFAX_6400001 b/doc/tutorials/DC3_model/fem/right-wing/right-wing.RBE2_LREFAX_6400001 new file mode 100644 index 00000000..ac6e30fa --- /dev/null +++ b/doc/tutorials/DC3_model/fem/right-wing/right-wing.RBE2_LREFAX_6400001 @@ -0,0 +1,123 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-28 15:15:19 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +GRID 64090201 11.2100 1.11-15 .150999 +GRID 64090101 6.88999 1.11-15 .150999 +RBE2 6409010164090001123456 6409010164090201 +GRID 64090202 11.2100 .526238 .150999 +GRID 64090102 6.88999 .526238 .150999 +RBE2 6409010264090002123456 6409010264090202 +GRID 64090203 11.2100 1.05246 .150999 +GRID 64090103 6.88999 1.05246 .150999 +RBE2 6409010364090003123456 6409010364090203 +GRID 64090204 11.2100 1.57870 .150999 +GRID 64090104 6.88999 1.57870 .150999 +RBE2 6409010464090004123456 6409010464090204 +GRID 64090205 11.2100 2.10126 .150999 +GRID 64090105 6.88999 2.10126 .150999 +RBE2 6409010564090005123456 6409010564090205 +GRID 64090206 11.2100 2.49870 .150999 +GRID 64090106 6.88999 2.49870 .150999 +RBE2 6409010664090006123456 6409010664090206 +GRID 64090207 11.2100 3.15374 .150999 +GRID 64090107 6.88999 3.15374 .150999 +RBE2 6409010764090007123456 6409010764090207 +GRID 64090208 11.2100 3.68000 .150999 +GRID 64090108 6.88999 3.68000 .150999 +RBE2 6409010864090008123456 6409010864090208 +GRID 64090209 11.2100 4.11693 .184824 +GRID 64090109 7.01608 4.11693 .184824 +RBE2 6409010964090009123456 6409010964090209 +GRID 64090210 11.2100 4.55388 .218649 +GRID 64090110 7.14216 4.55388 .218649 +RBE2 6409011064090010123456 6409011064090210 +GRID 64090211 11.2100 4.99081 .252474 +GRID 64090111 7.26824 4.99081 .252474 +RBE2 6409011164090011123456 6409011164090211 +GRID 64090212 11.2100 5.42776 .286299 +GRID 64090112 7.39433 5.42776 .286299 +RBE2 6409011264090012123456 6409011264090212 +GRID 64090213 11.2100 5.86471 .320124 +GRID 64090113 7.52041 5.86471 .320124 +RBE2 6409011364090013123456 6409011364090213 +GRID 64090214 11.2100 6.30164 .353949 +GRID 64090114 7.64649 6.30164 .353949 +RBE2 6409011464090014123456 6409011464090214 +GRID 64090215 11.2100 6.73858 .387774 +GRID 64090115 7.77257 6.73858 .387774 +RBE2 6409011564090015123456 6409011564090215 +GRID 64090216 11.2100 7.17553 .421599 +GRID 64090116 7.89866 7.17553 .421599 +RBE2 6409011664090016123456 6409011664090216 +GRID 64090217 11.2100 7.61246 .455423 +GRID 64090117 8.02474 7.61246 .455423 +RBE2 6409011764090017123456 6409011764090217 +GRID 64090218 11.2100 8.04940 .489248 +GRID 64090118 8.15082 8.04940 .489248 +RBE2 6409011864090018123456 6409011864090218 +GRID 64090219 11.2100 8.48635 .523073 +GRID 64090119 8.27690 8.48635 .523073 +RBE2 6409011964090019123456 6409011964090219 +GRID 64090220 11.2100 8.92328 .556897 +GRID 64090120 8.40298 8.92328 .556897 +RBE2 6409012064090020123456 6409012064090220 +GRID 64090221 11.2100 9.36021 .590722 +GRID 64090121 8.52906 9.36021 .590722 +RBE2 6409012164090021123456 6409012164090221 +GRID 64090222 11.2100 9.79715 .624547 +GRID 64090122 8.65514 9.79715 .624547 +RBE2 6409012264090022123456 6409012264090222 +GRID 64090223 11.2100 10.2340 .658367 +GRID 64090123 8.78121 10.2340 .658367 +RBE2 6409012364090023123456 6409012364090223 +GRID 64090224 11.2100 10.6710 .692194 +GRID 64090124 8.90730 10.6710 .692194 +RBE2 6409012464090024123456 6409012464090224 +GRID 64090225 11.2100 11.1078 .726014 +GRID 64090125 9.03336 11.1078 .726014 +RBE2 6409012564090025123456 6409012564090225 +GRID 64090226 11.2100 11.5448 .759840 +GRID 64090126 9.15945 11.5448 .759840 +RBE2 6409012664090026123456 6409012664090226 +GRID 64090227 11.2100 11.9817 .793659 +GRID 64090127 9.28551 11.9817 .793659 +RBE2 6409012764090027123456 6409012764090227 +GRID 64090228 11.2100 12.4186 .827484 +GRID 64090128 9.41159 12.4186 .827484 +RBE2 6409012864090028123456 6409012864090228 +GRID 64090229 11.2100 12.8554 .861300 +GRID 64090129 9.53765 12.8554 .861300 +RBE2 6409012964090029123456 6409012964090229 +GRID 64090230 11.2100 13.2923 .895122 +GRID 64090130 9.66372 13.2923 .895122 +RBE2 6409013064090030123456 6409013064090230 +GRID 64090231 11.2100 13.7254 .928650 +GRID 64090131 9.78999 13.7299 .929000 +RBE2 6409013164090031123456 6409013164090231 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/sets_for_splines.bdf b/doc/tutorials/DC3_model/fem/sets_for_splines.bdf new file mode 100644 index 00000000..9bda3986 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/sets_for_splines.bdf @@ -0,0 +1,17 @@ +$ SET1 cards required for SPLINE1 cards +$------><------><------><------><------><------><------><------><------> +$ vt +SET1 332 33290001 THRU 3329000933290101 THRU 3329010933290201+ ++ THRU 33290209 +$ left-ht +SET1 333 33390001 THRU 3339000833390101 THRU 3339010833390201+ ++ THRU 33390208 +$ right-ht +SET1 334 33490001 THRU 3349000833490101 THRU 3349010833490201+ ++ THRU 33490208 +$ left-wing +SET1 540 54090001 THRU 5409003154090101 THRU 5409013154090201+ ++ THRU 54090231 +$ right-wing +SET1 640 64090001 THRU 6409003164090101 THRU 6409013164090201+ ++ THRU 64090231 diff --git a/doc/tutorials/DC3_model/fem/structure_only.bdf b/doc/tutorials/DC3_model/fem/structure_only.bdf new file mode 100644 index 00000000..04ed9450 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/structure_only.bdf @@ -0,0 +1,72 @@ +$ +$----------------------------------------------------------------------- +$ Interface FUS/Right-wing, FUS/Left_wing +$------><------><------><------><------><------><------><------><------> +RBE2 200001 100004 1234566409000154090001 +$ +$ Interface FUS/Right-ht, FUS/Left-ht +$------><------><------><------><------><------><------><------><------> +RBE2 200002 100010 1234563349000133390001 +$ +$ Interface FUS/Vt +$------><------><------><------><------><------><------><------><------> +RBE2 200003 100010 12345633290001 +$ +$ +$----------------------------------------------------------------------- +$ Fuselage +include '../fem/export_FUS.csv' +$----------------------------------------------------------------------- +$ Left-wing +$ LRA +include '../fem/left-wing/left-wing.GRID_LREFAX_5400001' +include '../fem/left-wing/left-wing.RBE2_LREFAX_5400001' +include '../fem/left-wing/left-wing.CORD2R_LREFAX' +$ bdf model +include '../fem/left-wing/left-wing.MAT_ZR' +include '../fem/left-wing/export_left-wing.csv' +$----------------------------------------------------------------------- +$ Right-wing +$ LRA +include '../fem/right-wing/right-wing.GRID_LREFAX_6400001' +include '../fem/right-wing/right-wing.RBE2_LREFAX_6400001' +include '../fem/right-wing/right-wing.CORD2R_LREFAX' +$ bdf model +include '../fem/right-wing/right-wing.MAT_ZR' +include '../fem/right-wing//export_right-wing.csv' +$----------------------------------------------------------------------- +$ Left-ht +$ LRA +include '../fem/left-ht/left-ht.GRID_LREFAX_3330001' +include '../fem/left-ht/left-ht.RBE2_LREFAX_3330001' +include '../fem/left-ht/left-ht.CORD2R_LREFAX' +$ bdf model +include '../fem/left-ht/left-ht.MAT_ZR' +include '../fem/left-ht/export_left-ht.csv' +$----------------------------------------------------------------------- +$ Right-ht +$ LRA +include '../fem/right-ht/right-ht.GRID_LREFAX_3340001' +include '../fem/right-ht/right-ht.RBE2_LREFAX_3340001' +include '../fem/right-ht/right-ht.CORD2R_LREFAX' +$ bdf model +include '../fem/right-ht/right-ht.MAT_ZR' +include '../fem/right-ht/export_right-ht.csv' +$----------------------------------------------------------------------- +$ Vt +$ LRA +include '../fem/vt/vt.GRID_LREFAX_3320001' +include '../fem/vt/vt.RBE2_LREFAX_3320001' +include '../fem/vt/vt.CORD2R_LREFAX' +$ bdf model +include '../fem/vt/vt.MAT_ZR' +include '../fem/vt/export_vt.csv' +$----------------------------------------------------------------------- +$ Left-nacell +include '../fem/export_left-nacell.csv' +$----------------------------------------------------------------------- +$ Right-nacell +include '../fem/export_right-nacell.csv' +$----------------------------------------------------------------------- + + diff --git a/doc/tutorials/DC3_model/fem/uset.op2 b/doc/tutorials/DC3_model/fem/uset.op2 new file mode 100755 index 00000000..048d9337 Binary files /dev/null and b/doc/tutorials/DC3_model/fem/uset.op2 differ diff --git a/doc/tutorials/DC3_model/fem/vt/export_vt.csv b/doc/tutorials/DC3_model/fem/vt/export_vt.csv new file mode 100644 index 00000000..1b949969 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/export_vt.csv @@ -0,0 +1,48 @@ +$ Mono, 10pt, column width 17mm, no spacing +$2345678 +$ HTP main bea +$CBAR EID PID GA GB X1 X2 X3 + +$+ w1A w2A w3A w1B w2B w3B +CBAR 3328001 33280013329000133290002 0.0000 1.0000 0.0000 +CBAR 3328002 33280023329000233290003 0.0000 1.0000 0.0000 +CBAR 3328003 33280033329000333290004 0.0000 1.0000 0.0000 +CBAR 3328004 33280043329000433290005 0.0000 1.0000 0.0000 +CBAR 3328005 33280053329000533290006 0.0000 1.0000 0.0000 +CBAR 3328006 33280063329000633290007 0.0000 1.0000 0.0000 +CBAR 3328007 33280073329000733290008 0.0000 1.0000 0.0000 +CBAR 3328008 33280083329000833290009 0.0000 1.0000 0.0000 + + +$ PBAR PID MID A I1 I2 J NSM +PBAR 3328001 3320012.567E-26.000E-43.703E-23.760E-2 0.000 +PBAR 3328002 3320012.201E-23.710E-42.726E-22.769E-2 0.000 +PBAR 3328003 3320011.864E-22.662E-41.956E-21.987E-2 0.000 +PBAR 3328004 3320011.554E-21.855E-41.363E-21.384E-2 0.000 +PBAR 3328005 3320011.273E-21.247E-49.158E-39.303E-3 0.000 +PBAR 3328006 3320011.020E-28.020E-55.894E-35.987E-3 0.000 +PBAR 3328007 3320017.948E-31.000E-43.590E-33.650E-3 0.000 +PBAR 3328008 3320015.979E-32.787E-52.045E-32.078E-3 0.000 + + + +$ vt mass model +$ CONM2 EID G CID M X1 X2 X3 + +$ I11 I21 I22 I31 I32 I33 +CONM2 332810133290001 0 13.375 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 20.8867 +CONM2 332810233290002 0 23.065 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 31.0587 +CONM2 332810333290003 0 19.654 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 22.5508 +CONM2 332810433290004 0 16.515 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 15.9235 +CONM2 332810533290005 0 13.650 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 10.8769 +CONM2 332810633290006 0 11.057 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 7.1371 +CONM2 332810733290007 0 8.737 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 4.4562 +CONM2 332810833290008 0 6.690 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 2.6126 +CONM2 332810933290009 0 2.458 0.00 0.00 0.00 + + 0.00 0.00 0.00 0.000 0.00 0.7053 diff --git a/doc/tutorials/DC3_model/fem/vt/vt.CORD2R_LREFAX b/doc/tutorials/DC3_model/fem/vt/vt.CORD2R_LREFAX new file mode 100644 index 00000000..115e0ca6 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/vt.CORD2R_LREFAX @@ -0,0 +1,41 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ direction cosine matrix +$ ex ey ez +$ 0.94182 0.33611 0.00000 +$ 0.00000 0.00000 -1.00000 +$ -0.33611 0.94182 0.00000 +$ +$ +CORD2R 3329001 0 18.5153 -2.9-15 1.86699 18.5153 -1.0000 1.86699+ ++ 18.9862 -.50000 1.69894 +$ +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/vt/vt.DMI_W2GJb b/doc/tutorials/DC3_model/fem/vt/vt.DMI_W2GJb new file mode 100644 index 00000000..b3e4362f --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/vt.DMI_W2GJb @@ -0,0 +1,32 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ +$ W2GJ factors - profile camber line and twist +$ +DMI W2GJ 0 2 1 0 60 1 +DMI W2GJ 1 1 0.000+0 2 0.000+0 3 0.000+0 + 4 0.000+0 5 0.000+0 6 0.000+0 7 0.000+0 + 8 0.000+0 9 0.000+0 10 0.000+0 11 0.000+0 + 12 0.000+0 13 0.000+0 14 0.000+0 15 0.000+0 + 16 0.000+0 17 0.000+0 18 0.000+0 19 0.000+0 + 20 0.000+0 21 0.000+0 22 0.000+0 23 0.000+0 + 24 0.000+0 25 0.000+0 26 0.000+0 27 0.000+0 + 28 0.000+0 29 0.000+0 30 0.000+0 31 0.000+0 + 32 0.000+0 33 0.000+0 34 0.000+0 35 0.000+0 + 36 0.000+0 37 0.000+0 38 0.000+0 39 0.000+0 + 40 0.000+0 41 0.000+0 42 0.000+0 43 0.000+0 + 44 0.000+0 45 0.000+0 46 0.000+0 47 0.000+0 + 48 0.000+0 49 0.000+0 50 0.000+0 51 0.000+0 + 52 0.000+0 53 0.000+0 54 0.000+0 55 0.000+0 + 56 0.000+0 57 0.000+0 58 0.000+0 59 0.000+0 + 60 0.000+0 diff --git a/doc/tutorials/DC3_model/fem/vt/vt.GRID_LREFAX_3320001 b/doc/tutorials/DC3_model/fem/vt/vt.GRID_LREFAX_3320001 new file mode 100644 index 00000000..fc7a7756 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/vt.GRID_LREFAX_3320001 @@ -0,0 +1,41 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +$ GRIDs of load reference axis defined by input card LREFAX 3320001 +$ +GRID 33290001 18.5153-2.95-15 1.86699 +GRID 33290002 18.6428-2.58-15 2.22425 +GRID 33290003 18.7703-2.22-15 2.58150 +GRID 33290004 18.8978-1.85-15 2.93875 +GRID 33290005 19.0253-1.48-15 3.29600 +GRID 33290006 19.1528-1.11-15 3.65323 +GRID 33290007 19.2803-7.49-16 4.01048 +GRID 33290008 19.4078-3.82-16 4.36775 +GRID 33290009 19.5352-1.46-17 4.72498 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/vt/vt.MAT_ZR b/doc/tutorials/DC3_model/fem/vt/vt.MAT_ZR new file mode 100644 index 00000000..5bb48b39 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/vt.MAT_ZR @@ -0,0 +1,31 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +MAT1 332001 7.00+10 2.69+10 0.3 0.0 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/vt/vt.RBE2_LREFAX_3320001 b/doc/tutorials/DC3_model/fem/vt/vt.RBE2_LREFAX_3320001 new file mode 100644 index 00000000..d325b511 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/vt/vt.RBE2_LREFAX_3320001 @@ -0,0 +1,57 @@ +$ +$*********************************************************************** +$ Created by ModGen (Version 2.2401.01 (latest version)) +$ Operating System: Linux +$ Date & Time: 2024-03-05 11:03:50 +$*********************************************************************** +$ +$ TITLE= DC-3 +$ +$ +$----------------------------------------------------------------------- +$ +GRID 33290201 21.2600 0.000+0 1.86699 +GRID 33290101 17.4160 0.000+0 1.86699 +RBE2 3329010133290001123456 3329010133290201 +GRID 33290202 21.1914 0.000+0 2.22425 +GRID 33290102 17.6219 0.000+0 2.22425 +RBE2 3329010233290002123456 3329010233290202 +GRID 33290203 21.1229 0.000+0 2.58150 +GRID 33290103 17.8279 0.000+0 2.58150 +RBE2 3329010333290003123456 3329010333290203 +GRID 33290204 21.0545 0.000+0 2.93875 +GRID 33290104 18.0340 0.000+0 2.93875 +RBE2 3329010433290004123456 3329010433290204 +GRID 33290205 20.9860 0.000+0 3.29600 +GRID 33290105 18.2399 0.000+0 3.29600 +RBE2 3329010533290005123456 3329010533290205 +GRID 33290206 20.9175 0.000+0 3.65323 +GRID 33290106 18.4459 0.000+0 3.65323 +RBE2 3329010633290006123456 3329010633290206 +GRID 33290207 20.8490 0.000+0 4.01048 +GRID 33290107 18.6519 0.000+0 4.01048 +RBE2 3329010733290007123456 3329010733290207 +GRID 33290208 20.7805 0.000+0 4.36775 +GRID 33290108 18.8579 0.000+0 4.36775 +RBE2 3329010833290008123456 3329010833290208 +GRID 33290209 20.7120 0.000+0 4.72498 +GRID 33290109 19.0639 0.000+0 4.72498 +RBE2 3329010933290009123456 3329010933290209 +$ +$ +$*********************************************************************** +$ ModGen is a program to set up Nastran structural, aerodynamic, and +$ optimization models for aircraft structures like wing and fuselage +$ including optional pre-sizing. +$ +$ ModGen is developed by: +$ +$ DLR German Aerospace Center +$ Institute of Aeroelasticity +$ 37073 Goettingen +$ Internet www.dlr.de/ae +$ Contact: thomas.klimmek@dlr.de +$ +$*********************************************************************** +$ +$ diff --git a/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge b/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge new file mode 100644 index 00000000..ef3bebe6 --- /dev/null +++ b/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge @@ -0,0 +1,301 @@ +$ +$******************************************************************************* +$ File created by the program mrw2gj +$ Date & Time 2024-03-19 13:18:14 +$ Contact: thomas.klimmek@dlr.de +$******************************************************************************* +$ +$ FILE: vt/vt.DMI_W2GJb OPENED +$ +$ DMI MATRIX W2GJ FOUND: 60 ROWS, 1 COLS +$ +$ FILE: eft-ht.DMI_W2GJb OPENED +$ +$ DMI MATRIX W2GJ FOUND: 74 ROWS, 1 COLS +$ +$ FILE: right-ht.DMI_W2GJb OPENED +$ +$ DMI MATRIX W2GJ FOUND: 74 ROWS, 1 COLS +$ +$ FILE: left-wing.DMI_W2GJb OPENED +$ +$ 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pathlib.Path(__file__).parent.parent.parent.resolve() +path = os.path.join(path_root, 'DC3_results', 'response_jcl_dc3_trim.hdf5') +filename = path + +# open HDF5 file +responses = h5py.File(filename, 'r') + +# get response of first trim case +response = responses['0'] +response2 = responses['1'] +response3 = responses['2'] +Ug_f = response['Ug_f'][:] +Ug_f2 = response2['Ug_f'][:] +Ug_f3 = response3['Ug_f'][:] + +# extract ID, offset and set matrices +filename = path = os.path.join(path_root, 'DC3_results', 'model_jcl_dc3_trim.hdf5') +model = h5py.File(filename, 'r') +strcgrid = model['strcgrid'] +ID = strcgrid['ID'][:] +set = strcgrid['set'][:] +offset = strcgrid['offset'][:] + +# IDs of the wing's LRA +ID_wings = np.array([54090001, 54090002, 54090003, 54090004, 54090005, 54090006, 54090007, 54090008, 54090009, + 54090010, 54090011, 54090012, 54090013, 54090014, 54090015, 54090016, 54090017, 54090018, + 54090019, 54090020, 54090021, 54090022, 54090023, 54090024, 54090025, 54090026, 54090027, + 54090028, 54090029, 54090030, 54090031, 64090001, 64090002, 64090003, 64090004, 64090005, + 64090006, 64090007, 64090008, 64090009, 64090010, 64090011, 64090012, 64090013, 64090014, + 64090015, 64090016, 64090017, 64090018, 64090019, 64090020, 64090021, 64090022, 64090023, + 64090024, 64090025, 64090026, 64090027, 64090028, 64090029, 64090030, 64090031]) + +y = [] +dz_pos = [] +ry_pos = [] +for i in range(len(ID_wings)): + x = np.where(strcgrid['ID'][:] == ID_wings[i]) + dz_pos.append(strcgrid['set'][x[0], 2]) + ry_pos.append(strcgrid['set'][x[0], 4]) + y.append(strcgrid['offset'][x[0], 1][0]) + +dz = [] +ry = [] +for i in range(len(dz_pos)): + dz.append(Ug_f[0, dz_pos[i]]) + ry.append(Ug_f[0, ry_pos[i]]) + +dz2 = [] +ry2 = [] +for i in range(len(dz_pos)): + dz2.append(Ug_f2[0, dz_pos[i]]) + ry2.append(Ug_f2[0, ry_pos[i]]) + +dz3 = [] +ry3 = [] +for i in range(len(dz_pos)): + dz3.append(Ug_f3[0, dz_pos[i]]) + ry3.append(Ug_f3[0, ry_pos[i]]) + +y_sorted_indices = np.argsort(y) +y_sorted = np.sort(y) +dz_sorted = [dz[i] for i in y_sorted_indices] +dz_sorted_sym = [-dz_sorted[i] for i in range(len(dz_sorted))] +ry_sorted = [ry[i] for i in y_sorted_indices] +dz2_sorted = [dz2[i] for i in y_sorted_indices] +dz2_sorted_sym = [-dz2_sorted[i] for i in range(len(dz2_sorted))] +ry2_sorted = [ry2[i] for i in y_sorted_indices] +dz3_sorted = [dz3[i] for i in y_sorted_indices] +dz3_sorted_sym = [-dz3_sorted[i] for i in range(len(dz3_sorted))] +ry3_sorted = [ry3[i] for i in y_sorted_indices] + +ry_sorted = np.degrees(ry_sorted) +ry2_sorted = np.degrees(ry2_sorted) +ry3_sorted = np.degrees(ry3_sorted) + +# Plots +fig, axs = plt.subplots(2, 1, figsize=(12, 6)) +axs[0].plot(y_sorted, dz_sorted_sym, marker='o', linestyle='-', color='blue', label='Horizontal Level Flight (n$_Z$=1)') +axs[0].plot(y_sorted, dz2_sorted_sym, marker='o', linestyle='-', color='red', label='Push Down Maneuver (n$_Z$=-1)') +axs[0].plot(y_sorted, dz3_sorted_sym, marker='o', linestyle='-', color='green', label='Pull Up Maneuver (n$_Z$=2.5)') +axs[0].set_xlabel('Wing span [m]', fontsize='x-large') +axs[0].set_ylabel('U$_{flex,z}$ [m]', fontsize='x-large') +axs[0].grid(True) +axs[0].legend(fontsize='x-large') + +axs[1].plot(y_sorted, ry_sorted, marker='o', linestyle='-', color='blue') +axs[1].plot(y_sorted, ry2_sorted, marker='o', linestyle='-', color='red') +axs[1].plot(y_sorted, ry3_sorted, marker='o', linestyle='-', color='green') +axs[1].set_xlabel('Wing span [m]', fontsize='x-large') +axs[1].set_ylabel('U$_{flex,ry}$ [deg]', fontsize='x-large') +axs[1].grid(True) +plt.tight_layout() +plt.show() diff --git a/doc/tutorials/_config.yml b/doc/tutorials/_config.yml index 0f296182..f9e73640 100644 --- a/doc/tutorials/_config.yml +++ b/doc/tutorials/_config.yml @@ -2,7 +2,7 @@ # Learn more at https://jupyterbook.org/customize/config.html title: Loads Kernel Tutorials -author: Arne Voß +author: Arne Voß and Francisco Carvalho logo: ../../graphics/LK_logo2.png # Force re-execution of notebooks on each build. @@ -36,4 +36,19 @@ html: # differently by Jupyter. sphinx: config: - nb_merge_streams: true \ No newline at end of file + nb_merge_streams: true + +# Parse and render settings +parse: + myst_enable_extensions: # default extensions to enable in the myst parser. See https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html + # - amsmath + - colon_fence + # - deflist + - dollarmath + # - html_admonition + - html_image + - linkify + # - replacements + # - smartquotes + - substitution + - tasklist diff --git a/doc/tutorials/_toc.yml b/doc/tutorials/_toc.yml index f71861c9..a1e23e42 100644 --- a/doc/tutorials/_toc.yml +++ b/doc/tutorials/_toc.yml @@ -5,3 +5,9 @@ format: jb-book root: intro chapters: - file: first_steps +- file: simulation_set_up +- file: aeroelastic_modeling +- file: trim +- file: maneuver_loads +- file: gust_time_domain +- file: flutter diff --git a/doc/tutorials/aeroelastic_modeling.md b/doc/tutorials/aeroelastic_modeling.md new file mode 100644 index 00000000..45002013 --- /dev/null +++ b/doc/tutorials/aeroelastic_modeling.md @@ -0,0 +1,144 @@ +# Aeroelastic Model of the DC3 + +In this Jupyter notebook the aeroelastic modelling of the Douglas DC-3 is going to be exposed and explained. This model will include the finite element model, aerodynamic model, mass model, and fuel model. Additionally, the different mass configurations and various modified models will be detailed. The model was created with the aim of getting as close as possible to reality. Nonetheless, the model needs to fullfill some requirements, that are descriminated in front, and that is why modified models of the original models were created. Finally, the eigenmodes of the final modified model will be analysed. The aim of exposing the aeroelastic model is to show the different parts that the model should include and the level of detail. In the following figure, the aircraft in question can be seen: + +DC3 + +*A DC-3 operated by Scandinavian Airlines.* + + + +To ensure the generality, requisite complexity, and representativeness of the developed aeroelastic model, a deliberate choice was made regarding the optimal aircraft. To comprehend the rationale behind this selection, it is imperative to first grasp the specific requirements that the chosen aircraft needed to fulfill. The decision was made to start with an existing aircraft and, if needed, introduce modifications to accentuate certain aeroelastic phenomena. The specific requirements and specifications targeted were: + +- Aircraft Configuration - Simple and conventional. +- Wing - Features a simple planform (non-rectangular), with a significant leading edge (LE) sweep angle (ΛLE), washin/washout angle (Φ), dihedral angle (Γ), and an airfoil with camber. The aspect ratio (AR) should exceed 8 to facilitate the application of beam theory. +- Empennage - Adheres to a conventional configuration or includes a T-tail. +- Control Surfaces - Includes ailerons, elevators, and a rudder. Flaps, though optional, are excluded from the model. +- Engines - Limited to a maximum of 2 engines. +- Subsonic Regime - Mach number is below 0.6 (M<0.6) to avoid the formation of shock waves. +- Representativeness - The aircraft must be easily recognizable, as well as representative; for instance, a glider is not deemed suitable. +- Information Availability - Extensive data about the aircraft must be publicly accessible. This encompasses general geometry dimensions, airfoil data, internal structure, center of gravity (Cg), mass distribution, flight envelope, and performance. +- Aeroelastic Effects: - The aircraft should exhibit notable wing bending and torsion during level flight, with an evident flutter mechanism where bending and torsion modes are coupled. It is noteworthy that the properties of the aircraft can be manipulated to accentuate these aeroelastic phenomena. + + +## Modelling Overview + +Before starting with the more detailed model description, a modelling overview will be provided. This overview aims to explain better the purpose of the aeroelastic modelling. It is important that before using Loads Kernel some steps need to be performed before hand. Firstly, the aeroelastic model must be done by hand (using Patran), or a third party program (such as the in-house DLR tool Modgen {cite}`modgen2023`). Secondly, MSC Nastran is used to the role of a pre-processor that only delivers mass and stiffness matrices. After exporting the mass and stiffness matrices from MSC Nastran to Loads Kernel, Loads Kernel reads the finite element grid points and aeromodel as MSC Nastran cards. + +## Finite Element Model + +

To construct the finite element model for the Douglas DC-3, ModGen was employed, an in-house tool developed by DLR. ModGen was used to establish the finite element model of the wing-like components: the right and left wings, right and left horizontal tails, and the vertical tail. For that, the following steps were followed:

+ +- Planform definition - not only the general parameters of the wing and empenage, but the dihedral angle, incidence angle and control surfaces (ailerons, elevators and rudder). +- Airfoils - assigning airfoils to different surface sections. +- Spars and Ribs - determining the number and precise positions of the spars and ribs for the several surfaces. The stringers were not explicitly modeled, but their impact on stiffness properties was incorporated afterward. + +DC3 + +*Douglas DC-3 model geometry.* + +- Materials - assign a material to the geometry. In this instance, certain assumptions were made to streamline the modelling process. Instead of employing different materials for the wing skin, spars, and ribs, a single material was utilized. +- Structure model - the wing-like components were modeled as beams along their Load Reference Axis (LRA). Beam elements were +chosen primarily due to their simplicity and ease of modifying wing properties to observe various aeroelastic phenomena. For example, consider the right wing with 31 ribs, resulting in 31 grid points along the LRA. Stiffness properties were delineated in the regions between these grid points, giving rise to 30 sections requiring the definition of properties such as cross-sectional area (A), area moment of inertia about the chordwise axis (I1), area moment of inertia about the axis perpendicular to the chordwise direction in the airfoil plane (I2), and torsional constant (J). The computation of these cross-sectional properties involved considering the contributions of spars, skin, and stringers (in the case of the wing). All dimensions used in the property calculations were parametrically dependent on the chord, allowing for straightforward and logical generation of properties along the surfaces’ span. On the contrary, the fuselage and the attached engines were conceptualized as a single rigid body featuring independent degrees of freedom specified at a central grid point, alongside dependent degrees of freedom pinpointed at various grid points. The decision to model the fuselage as a rigid body, rather than a flexible beam, aimed to maintain model simplicity. Other aircraft components, not integral to the structure, such as systems and payload masses, were not incorporated into the structural model. + +DC3 + +*Douglas DC-3 structural discretization.* + +## Mass Model + +Besides the finite element, is imperative to develop the mass model. The mass model consists in distributing the mass of the aircarft across the different aircarft components nodes. In addition to the masses, the input of mass moments of inertia (Iji) for these masses is essential. The mass model of the DC-3 looks like this: + +DC3 + +*Douglas DC-3 mass modelling including structural, systems, crew and standard equipment masses.* + +The modelling of fuel masses is more complex than the rest of the components. Due to the significant mass and inertia properties of the fuel, precise modelling is essential. Additionally, fuel levels can vary, necessitating a different approach for accurate representation. For that, the fuel masses need to be discretized per fuel bay, and modelled as volume elements to obtain inertia properties and filling levels. The fuel model was developed using the DLR in-house tool Modgen, necessitating the definition of fuel tank geometry. Additionally, the fuel density needs to be specified, as well as the utilization factor, which refers to the percentage of the tank’s volume that is effectively filled with fuel, excluding the space occupied by fuel system components like pumps and lines. Finally, Z levels were defined based on the mass configuration. The illustration of the full fuel tanks model for the right wing is shown next: + +DC3 + +*Douglas DC-3 right wing full fuel tanks.* + +## Aerodynamics Model + +To conduct a comprehensive aeroelastic analysis, the Doublet-Lattice Method (DLM) is often coupled with a linear finite element structural model. This integration entails an interpolation method to establish a relationship between the structural deformation field and the motion of aerodynamic surfaces. The DLM exhibits efficiency when the aerodynamic configuration can be represented with a small number of quadrilateral panels. + +Panel size in the chordwise direction must adhere to the condition Δ < 0.08V/f ( 0.02V /f is needed for converged stability derivatives), where V is the minimum velocity, and f, in Hertz, is the maximum frequency to be analyzed. Another condition for panel geometry is that AR<4 to ensure mesh quality, and an aspect ratio of AR=2 was targeted. Additionally, it is essential to align the wing and horizontal tail panels so that each wing panel corresponds to only one horizontal tail panel. Next figure shows the panel alignment and the macro panels highlighted in yellow. + +DC3 + +*Aerodynamic mesh panels aligned between wing and horizontal tail.* + +The next figure displays the final aerodynamic mesh for the Douglas DC-3 model: + +DC3 + +*Douglas DC-3 model aerodynamic mesh.* + + +## Mass Configurations + +Four representative mass configurations for the Douglas DC-3 have been outlined. The first configuration refers to the operational empty mass (OEM), covering only the weight of the structure, crew, and standard equipment. The second configuration denotes the maximum range, obtained by adding the maximum fuel capacity to the OEM. The third configuration represents the maximum take-off mass (MTOM), incorporating the maximum payload and the fuel capacity that constitutes 49% of the total fuel capacity. The fourth and final configuration encompasses the OEM along with the maximum capacity, excluding any fuel. The next table provides a summary of these four mass configurations. + +DC3 + +*Mass configurations.* + +The next figure illustrates the relationship between the mass of various configurations and their respective center of gravity positions, along with the established limits for center of gravity and mass. It is evident that all four configurations are well within the specified limits. The center of gravity positions are measured from the aircraft’s nose. + +DC3 + +*Mass against center of gravity position for the different mass configurations.* + +## Modified Models + +To analyse the impact of aeroelasticity in the original model, a trim was performed. The trim was performed at horizontal level flight, a Load Factor of nZ=1, with a True Airspeed (TAS) of 70 m/s, and Flight Level FL000. The M3 mass configuration was selected because it represents the scenario in which the aircraft undergoes the most significant structural deformations, thereby embodying a critical test case. Plotting the elastic deformation of the wing and the looking into the elastic deformation contribution on the lift force of the original model, it can be concluded that the impact of aeroelasticity is negligible. This outcome was expected, considering the era of the aircraft’s original development and production in the 1930s, when understanding of aeroelasticity was limited. Consequently, aircraft designs leaned towards stiffness to prevent potential complexities and analysis challenges. That can be seen by the use of a big number of spars and ribs, compared with more modern aircraft in the same category. + +Given that one of the primary objectives of this aeroelastic model is to underscore the significance of aeroelasticity in aircraft design, stability, flight control, and performance, modified versions of the original Douglas DC-3 aeroelastic model have been developed. + +In the initial modified model (More Flex), alterations were made to the wing’s stiffness and inertial properties. Regarding stiffness properties, the area moment of inertia about the chordwise axis (I1) was reduced to a values five times smaller, the moment of inertia about the axis perpendicular to the chordwise direction in the airfoil plane (I2), and torsional constant (J) were reduced by one order of magnitude. Additionally, the mass moments of inertia (Iji) of the wing masses were increased by one order of magnitude. These modifications were applied in order to obtain higher deflections in terms of bending and torsion. Analysis of the trim results reveals that the contribution of elastic deformation to the lift coefficient has risen. Furthermore, elastic deformations are significantly more pronounced. + +DC3 + +*Trim results for different aeroelastic models for M3.* + +Subsequently, to make sure that the variation in stiffness properties indeed accounts for the observed differences, a second modified aeroelastic model (More Flex 2) was crafted. This model retained the same stiffness modifications as the first modified one but preserved the original inertia/mass properties. Examination of the trim results and the elastic deformation revealed striking similarities, corroborating the statement that stiffness properties are the pivotal factor. + +In order to explore the impact of the LRA position (thus the location of the elastic axis) on both trim results and elastic deformation, two additional modified aeroelastic models were constructed. In both cases, the wing stiffness properties remained consistent with those of the first and second modified models, while the inertial properties reverted to those of the original model, given their negligible influence. In the third modified model, the wing LRA was shifted forward from a mid-position between the front and rear spars to a position 30% along the distance between them. Basically the elastic axis was positioned in front of the center of mass of the wing. Conversely, in the fourth modified model, the wing LRA was relocated aft, positioned at 70% along the distance between the front and rear spars. The elastic axis was positioned aft the center of mass. In both instances, the distribution of wing masses remained unchanged to ensure that the aircraft’s center of gravity remained unaltered. + +Upon reviewing the results, it becomes evident that the elastic deformation contributes 4.7% to the lift coefficient in the third modified model and 2.6% in the fourth modified model. While the vertical displacements appear similar across both models, disparities emerge in the twist deformation. Notably, the third modified model exhibits greater elastic deformations and a heightened contribution to the lift force, indicating that shifting the LRA forward amplifies aeroelastic effects. Therefore, the third modified model, incorporating alterations in both stiffness properties and LRA position compared to the original aeroelastic model of the Douglas DC-3, effectively demonstrates the influence of aeroelasticity in trim level flight. + +It’s important to note that with the decrease in stiffness, the eigenfrequencies of these modified aeroelastic models have diminished, as anticipated. For example, the first flexible eigenfrequency has decreased to approximately 2-3 Hz, which is deemed acceptable as there is no interaction between the flexible modes and the rigid body modes. + +DC3 + +*Elastic deformation of the main wing at Load Factor of nZ=1, with a True Airspeed (TAS) of 70 m/s, Flight Level FL000, and M3 mass configuration for the original and modified aeroelastic models.* + +A fundamental requirement of this aeroelastic model is the presence of a distinct flutter mechanism, where bending and torsion modes are coupled, occurring at velocities beyond the flight envelope but within the subsonic regime (M<0.6). Upon examining the flutter mechanism and velocity using both MSC Nastran and Loads Kernel, it was noted that the flutter speed exceeded by a substantial margin M=0.6, placing it within the transonic regime. + +As a result, adjustments were necessary for this third modified model. Regarding stiffness properties, the area moment of inertia about the chordwise axis (I1) remained unchanged, while the moment of inertia about the axis perpendicular to the chordwise direction in the airfoil plane (I2) and the torsional constant (J) were halved compared to the previous modified model. This adjustment aimed to enhance twist deformations, thereby enhancing the coupling between bending and torsion modes. Additionally, the distribution of wing masses was changed. Previously concentrated in the LRA, the masses were relocated towards the trailing edge, while the LRA itself was shifted forward from its original position, which was 30% between the front and rear spars, to a new position, 20% along the distance between them. The wing masses were then positioned 45% of the wing section chord aft of the LRA. Furthermore, a redistribution of wing mass was carried out, with mass being transferred from nodes in sections near the wingtip to nodes in sections closer to the wing root. + +These modifications led to a notable reduction in flutter speed, achieving a flutter phenomenon within the subsonic regime, featuring a distinct coupling between bending and torsion modes as intended. The rationale behind these adjustments to mass distribution are: first, to facilitate the observation of the flutter mechanism with coupling between bending and torsion modes by shifting masses aft and the LRA forward; second, by moving some wing mass closer to the root, the weight distribution towards the wingtip decreases, so the aerodynamic forces needed to counteract the weight force at the tip decreases, necessitating a lower dynamic pressure, and consequently, a lower velocity to induce flutter. The mechanisms described above allowed the flutter speed to drop significantly while staying outside the flight envelope. The flutter check is present and explained in detail in a notebook further on. + +## Eigenmodes + +Having obtained the final aeroelastic model, eigenmodes and eigenfrequencies can be computed. For that, Loads Kernel was used. The same calculations can be performed in MSC Nastran with the help of solution sequence SOL103. For the visualization of the eigenmodes Model Viewer was utilized. + +DC3 + +*First four flexible mode shapes, M3.* + +Looking at the results from this solution sequence for the final modified aeroelastic model, the first six eigenvalues, as expected, are approximately 0 Hz. These eigenvalues correspond to the rigid body modes. In the following figure the first four flexible mode shapes for the unconstrained aircraft in vacuum for the M3 mass configuration are exposed. It can be seen, that the first eigenmode has a eigenfrequency of 3.17 Hz which does not fall in the range expected from an aircraft of the size and weight of the Douglas DC-3, as well as the design and manufacture techniques of that time. This was expected since the model was tuned significantly. One thing that is noticeable is that there is a mode correspondent to a pure wing torsion at f=9.60 Hz, which is the seventh eigenmode. This was something that was aimed to have. In several modes the wing bending is coupled with the wing torsion as expected. + + + + + + + + + + + + + \ No newline at end of file diff --git a/doc/tutorials/first_steps.ipynb b/doc/tutorials/first_steps.ipynb index 42debfea..266bb117 100644 --- a/doc/tutorials/first_steps.ipynb +++ b/doc/tutorials/first_steps.ipynb @@ -6,21 +6,21 @@ "metadata": {}, "source": [ "# First Steps\n", - "This Tutorial will give a short introduction on how to use Loads Kernel. For more information on the theoretical background, please consult the User Guide. It is also assumed that you sucessfully installed the software as a Python package as described in the [README](https://github.com/DLR-AE/LoadsKernel#installation--use).\n", "\n", - "To Do / Notes to myself\n", - "- Jupyter Notebooks can be tested using the nbmake plugin for pytest, see https://pypi.org/project/nbmake/.\n", - "To figure out if the notebooks are still working, \"pytest --nbmake \\*.ipynb\" will run a test of all notebook.\n", - "- The Discus2c examples are not published, so switch to public example when available.\n", + "This tutorial will give a short introduction on how to use Loads Kernel. For more information on the theoretical background, please consult the User Guide. It is also assumed that you sucessfully installed the software as a Python package as described in the [README](https://github.com/DLR-AE/LoadsKernel#installation--use).\n", "\n", "Let's see if we can import the software, which is a good indictor for a sucessful installation. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a4a55c00", - "metadata": {}, + "metadata": { + "tags": [ + "skip-execution" + ] + }, "outputs": [], "source": [ "from loadskernel import program_flow" @@ -44,14 +44,172 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "744e8fab", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_trim\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: False\n", + "INFO: post: False\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 3 empty dicts.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 2 Mach number(s)...\n", + "INFO: done in 0.92 [sec].\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 76 modes...\n", + "INFO: Found 76 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433851e+01 2.81886245e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01 3.71484020e+01 3.74387461e+01\n", + " 4.65087795e+01 4.66555395e+01 5.19704569e+01 5.31897897e+01\n", + " 5.59827047e+01 5.61588818e+01 5.79161872e+01 5.83859187e+01\n", + " 6.49254027e+01 6.49636994e+01 6.52953993e+01 6.55893563e+01\n", + " 6.76977070e+01 7.39272939e+01 7.40020584e+01 7.92493678e+01\n", + " 7.98448917e+01 8.44686963e+01 8.44977700e+01 8.89696420e+01\n", + " 8.90380160e+01 9.17266784e+01 9.27516791e+01 1.00718906e+02\n", + " 1.00742526e+02 1.05800840e+02 1.06520421e+02 1.09898735e+02\n", + " 1.10071029e+02 1.12603325e+02 1.12677121e+02 1.21628942e+02\n", + " 1.21633308e+02 1.25797288e+02 1.25826624e+02 1.30369009e+02\n", + " 1.30516978e+02 1.37566261e+02 1.37608584e+02 1.41676350e+02\n", + " 1.44303157e+02 1.44811563e+02 1.47038007e+02 1.47151873e+02\n", + " 1.55547154e+02 1.56081100e+02 1.56183006e+02 1.57181628e+02]\n", + "INFO: From these 76 modes, the following 70 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n", + " 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", + " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:04 [h:mm:ss].\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], "source": [ - "k = program_flow.Kernel(job_name='jcl_Discus2c_B2000', pre=True, main=False, post=False,\n", - " path_input='../../loads-kernel-examples/Discus2c/JCLs',\n", - " path_output='../../loads-kernel-examples/Discus2c/output')\n", + "k = program_flow.Kernel('jcl_dc3_trim', pre=True, main=False, post=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", "k.run()" ] }, @@ -70,9 +228,9 @@ "metadata": {}, "source": [ "## Model Viewer\n", - "As the preprocessing is finished, you should find a file \"model_\\*.hdf5\" in the output folder. You can explore the file with tools such as \"hdfview\" or use the Model Viewer to visualize the model. Note that HDF5 files have a locking mechnism, so always remember to close the file again.\n", + "As the preprocessing is finished, you should find a file \"model_\\*.hdf5\" in the output folder. You can explore the file with tools such as \"hdfview\" or use the Model Viewer to visualize the model. Note that HDF5 files have a locking mechnism, so always remember to close the file again.\n", "\n", - "During the installation, a link should have been added to your system PATH, so that you can launch the the Model Viewer from the command line with \"model-viewer\". For this tutorial, we initialze the Model Viewer interactively from python. The Model Viewer is based on Mayavi, which is powerful for 3D visualizaions but needs a few seconds to be loaded.\n", + "During the installation, a link should have been added to your system PATH, so that you can launch the the Model Viewer from the command line with \"model-viewer\". For this tutorial, we initialze the Model Viewer interactively from python. The Model Viewer is based on Mayavi, which is powerful for 3D visualizaions but needs a few seconds to be loaded.\n", "\n", "After a few seconds, new window should open, possibly in the background. Load the model via File -> Load model and start exploring! Remember that this GUI is not as robust and tested as you might be used form your daily software and it can break easily (e.g. random clicking, clicking buttons twice, etc.), so be careful and you will get some nice visualizations.\n", "\n", @@ -87,14 +245,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "43566f76", + "execution_count": 3, + "id": "f5987cf8-628d-406b-aaed-9af22e008bd1", "metadata": { "tags": [ "skip-execution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Qt: Session management error: None of the authentication protocols specified are supported\n" + ] + } + ], "source": [ "from modelviewer import view\n", "m = view.Modelviewer()\n", @@ -120,14 +286,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "3c6bf524", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_trim\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: False\n", + "INFO: main: True\n", + "INFO: post: False\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 3 empty dicts.\n", + "INFO: --> Starting Main in sequential mode for 3 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pushdown\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pullup\n", + "INFO: subcase: 3\n", + "INFO: (case 3 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:02 [h:mm:ss].\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], "source": [ - "k = program_flow.Kernel(job_name='jcl_Discus2c_B2000', pre=False, main=True, post=False,\n", - " path_input='../../loads-kernel-examples/Discus2c/JCLs',\n", - " path_output='../../loads-kernel-examples/Discus2c/output')\n", + "k = program_flow.Kernel(job_name='jcl_dc3_trim', pre=False, main=True, post=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", "k.run()" ] }, @@ -137,21 +390,29 @@ "metadata": {}, "source": [ "## Loads Compare\n", - "To visualize the section loads interactively, the tool Loads Compare can be used. Similar to the Model Viewer, the Loads Compare tool can be launched from the command line with \"loads-compare\". You can try that now or, alternatively, execute the following two lines.\n", + "To visualize the section loads interactively, the tool Loads Compare can be used. Similar to the Model Viewer, the Loads Compare tool can be launched from the command line with \"loads-compare\". You can try that now or, alternatively, execute the following two lines.\n", "\n", "Load the data via File -> Load Monstations. The GUI allows you to show the section loads in 2D envelopes, for example the wing root torsional moments My plotted over the bending moments Mx. Each dot in the plot corresponds to one load case or one time slice. You can also rename the dataset, change the color and load multiple datasets, which is handy to compare for example maneuver loads with gust loads or to identify differences in loads bewteen two sizing loops. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "74791581", "metadata": { "tags": [ "skip-execution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Qt: Session management error: None of the authentication protocols specified are supported\n" + ] + } + ], "source": [ "from loadscompare import compare\n", "c = compare.Compare()\n", @@ -171,21 +432,66 @@ "- The section loads are plotted at the monitoring stations and saved in a PDF document (if specified in the JCL).\n", "- The trim results are exported as a CSV file.\n", "- Three more CSV files simply list the failed, the sucessful and the critical trim cases. \n", - "- The nodal forces acting on the structure are exported in MSC.Nastran format using FORCE and MOMENT cards.\n", + "- The nodal forces acting on the structure are exported in MSC Nastran format using FORCE and MOMENT cards.\n", "\n", - "For further analyses or custom plots you can open the response file in Python, Matlab or your favorite tool that understands HDF5." + "For further analyses or custom plots you can open the response file in Python, Matlab or your favorite tool that understands HDF5." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1151d9ec", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_trim\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: False\n", + "INFO: main: False\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 3 empty dicts.\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_trim.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing trim results to: ./DC3_results/trim_results_jcl_dc3_trim.csv\n", + "INFO: writing successful trimcases cases to: ./DC3_results/successful_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing failed trimcases cases to: ./DC3_results/failed_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_trim.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], "source": [ - "k = program_flow.Kernel(job_name='jcl_Discus2c_B2000', pre=False, main=False, post=True,\n", - " path_input='../../loads-kernel-examples/Discus2c/JCLs',\n", - " path_output='../../loads-kernel-examples/Discus2c/output')\n", + "k = program_flow.Kernel(job_name='jcl_dc3_trim', pre=False, main=False, post=True,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", "k.run()" ] } @@ -207,7 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/doc/tutorials/flutter.ipynb b/doc/tutorials/flutter.ipynb new file mode 100644 index 00000000..390c1105 --- /dev/null +++ b/doc/tutorials/flutter.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f1382d15-d5e6-4da9-b16d-b50319092e1a", + "metadata": {}, + "source": [ + "# Flutter Check\n", + "\n", + "In this section a flutter check is going to be studied. The simulation set-up in Loads Kernel was already shown in detail in a previous notebook. Hence, the focus will be more in the results and post processing. At the end, the Loads Kernel are compared with the MSC Nastran results." + ] + }, + { + "cell_type": "markdown", + "id": "20bbe2cd-fc03-402d-b42c-06bf9a653f02", + "metadata": {}, + "source": [ + "## Simulation Set-up\n", + "\n", + "It is assumed that the notebook concerning the simulation set-up was studied before. The only comments regarding the code script are focus on the differences with the other simulations. \n", + "\n", + "To analyze flutter, horizontal level flight (nZ=1) FL000 at trim conditions was selected. Again the mass configuration M3 was chosen. The same conditions were written two times because two different methods were used to analyse flutter. For every self.trimcase, a corresponding self.simcase is required. A time simulation is triggered if the simcase contains at least 'dt' and 't_final'. In the DC-3 case, for the flutter check the KE-method and the PK-method were used. In order to perform those two simulations, the 'flutter' field was set to True and the method chosen." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fad596d3-8be5-4d7b-bfd5-385aac49c956", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + " \n", + " self.trimcase = [{'desc': 'CC.M3.OVCFL000.KE-Method', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 1,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0 ,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.PK-Method', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 2,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0 ,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " }]\n", + " \n", + " self.simcase = [{# True or False, enables 1-cosine gust according to CS-25\n", + " 'gust': False,\n", + " # True or False, enables continuous turbulence excitation\n", + " 'turbulence': False,\n", + " # True or False, calculates limit turbulence according to CS-25\n", + " 'limit_turbulence': False,\n", + " # True or False, enables playback of control surface signals via efcs\n", + " 'cs_signal': False,\n", + " # True or False, enables a generic controller e.g. to maintain p, q and r\n", + " 'controller': False,\n", + " # True or False, enables a generic landing gear\n", + " 'landinggear': False,\n", + " # True or False, enables calculation of rigid and elastic derivatives\n", + " 'derivatives': False,\n", + " # True or False, enables flutter check with k, ke or pk method\n", + " 'flutter': True,\n", + " # flutter parameters for k and ke method\n", + " 'flutter_para': {'method': 'ke', 'k_red': np.linspace(3.0, 0.001, 100)},\n", + " },\n", + " {# True or False, enables 1-cosine gust according to CS-25\n", + " 'gust': False,\n", + " # True or False, enables continuous turbulence excitation\n", + " 'turbulence': False,\n", + " # True or False, calculates limit turbulence according to CS-25\n", + " 'limit_turbulence': False,\n", + " # True or False, enables playback of control surface signals via efcs\n", + " 'cs_signal': False,\n", + " # True or False, enables a generic controller e.g. to maintain p, q and r\n", + " 'controller': False,\n", + " # True or False, enables a generic landing gear\n", + " 'landinggear': False,\n", + " # True or False, enables calculation of rigid and elastic derivatives\n", + " 'derivatives': False,\n", + " # True or False, enables flutter check with k, ke or pk method\n", + " 'flutter': True,\n", + " # flutter parameters for pk method\n", + " 'flutter_para': {'method': 'pk', 'Vtas': np.linspace(20.0, 300.0, 20)},\n", + " },\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "a31187b2-471c-4e97-a253-b900cf820b3f", + "metadata": {}, + "source": [ + "In order to obtain the results the following 'launch.py' file is run. The results can be found in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2aab232-5e9d-4260-b07e-8ec160b863c3", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_flutter\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "WARNING: No Monitoring Stations are created!\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 1 Mach number(s)...\n", + "INFO: done in 0.54 [sec].\n", + "INFO: Calculating unsteady AIC matrices (1056 panels, k=[0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0] (Nastran Definition!)) for 1 Mach number(s)...\n", + "INFO: done in 29.14 [sec].\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 27 modes...\n", + "INFO: Found 27 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433851e+01 2.81886245e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01 3.71484020e+01]\n", + "INFO: From these 27 modes, the following 21 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:33 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 2 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.KE-Method\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 2)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 27 variables...\n", + "INFO: The solution converged.\n", + "INFO: Init model equations of type \"KEMethod\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: building systems\n", + "INFO: calculating eigenvalues\n", + "INFO: Flutter analysis finished.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 1 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.PK-Method\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 2)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 27 variables...\n", + "INFO: The solution converged.\n", + "INFO: Init model equations of type \"PKMethod\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: building systems\n", + "INFO: starting p-k iterations to match k_red with Vtas and omega\n", + "INFO: Flutter analysis finished.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 1 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:15 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: jcl.loadplots not specified in the JCL - no automatic plotting of load envelopes possible.\n", + "INFO: start plotting flutter curves...\n", + "INFO: plots saved as ./DC3_results/fluttercurves_jcl_dc3_flutter.pdf\n", + "INFO: start plotting eigenvalues and -vectors...\n", + "INFO: skip plotting of eigenvalues and -vectors for CC.M3.OVCFL000.KE-Method\n", + "INFO: plots saved as ./DC3_results/eigenvalues_jcl_dc3_flutter.pdf\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_flutter', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + }, + { + "cell_type": "markdown", + "id": "1067ecaf-a1a9-41d8-af0d-aebd9d8ca28c", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "To conduct the flutter check, both the Loads Kernel or MSC Nastran was used. The flutter calculation was executed at FL000, where Equivalent Airspeed (EAS) is equal to True Airspeed (TAS), and the M3 mass configuration was adopted. The following figure illustrates the results of the flutter check conducted using Loads Kernel and the pk-method. The figure comprises two plots: one displaying the frequency of the modes as a function of velocity, and the other depicting the damping of the modes relative to velocity.\n", + "\n", + "In the damping plot, the evolution of each eigenmode’s damping with velocity is portrayed. Initially, at low velocities, all eigenmodes exhibit negative damping, indicating stability. However, at a velocity of 195 m/s, the eigenmode with a frequency of 9.60 Hz becomes unstable, marking the flutter speed Vflutter=195 m/s, equivalent to Ma=0.569. This mode represents the symmetrical pure torsion mode, maintaining a constant frequency across velocities. Subsequently, at 262 m/s, the mode with a frequency of 36.00 Hz also becomes unstable. This mode corresponds to a high-order symmetrical bending and torsion coupling mode,\n", + "\n", + "\"DC3\"\n", + "\n", + "*Flutter results Loads Kernel.*\n", + "\n", + "Examining the MSC Nastran results, it is evident that at lower velocities, all eigenmodes exhibit negative damping, indicating stability. However, at a velocity of 203 m/s, the eigenmode with a frequency of 9.60 Hz becomes unstable, marking the flutter speed Vflutter=203 m/s, equivalent to Ma=0.592. Comparing these findings with those from the Loads Kernel results, the first eigenmode to become unstable is the same, although at a slightly higher velocity in the case of MSC Nastran. Nevertheless, the results are remarkably consistent. \n", + "\n", + "At 256 m/s, another mode with a frequency of 26.19 Hz becomes unstable. Unlike the mode that became unstable around this velocity in the Loads Kernel analysis, this mode corresponds to a different high-order symmetrical bending and torsion coupling mode. In this instance, the frequency of the mode decreases from 26.19 Hz to 23 Hz by the time instability occurs. Thus, both Loads Kernel and MSC Nastran cases exhibit eigenmodes with a frequency of 23 Hz at the onset of instability, despite their differing initial frequencies.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Flutter results MSC Nastran.*\n", + "\n", + "It’s worth noting that both flutter speeds obtained lie outside the flight envelope (VNE=119.72 m/s), yet they remain within the subsonic regime (Ma<0.6), as intended.\n", + "\n", + "For additional information about the meanuver load results please refer to {cite}`Carvalho2024`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/gust_time_domain.ipynb b/doc/tutorials/gust_time_domain.ipynb new file mode 100644 index 00000000..7ff838fa --- /dev/null +++ b/doc/tutorials/gust_time_domain.ipynb @@ -0,0 +1,869 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19f7262c-8624-40ff-bcac-db66f3848262", + "metadata": {}, + "source": [ + "# Gust Loads\n", + "\n", + "In this section a gust loads analyses is going to be conducted. The simulation set-up in Loads Kernel was already shown in detail in a previous notebook. Hence, the focus will be more in the results and post processing. In order to showcase the gust load analyses capabilities of Loads Kernel only one gust gradeint was simulated in the first example. This is justified with the large amount of time necessary to perform the simulation with all the different gust gradients. Nevertheless, the simulation with all the different gust gradients is shown and it is explained under the name 'Advanced Simulation' below." + ] + }, + { + "cell_type": "markdown", + "id": "42e97fe9-1460-4f0e-9034-60d9ad3a32c9", + "metadata": {}, + "source": [ + "## Simulation Set-up\n", + "\n", + "The simulation set-up is similar to the one done for the trim and maneuver load analyses with the only exception being the last part of the set-up regarding the self.trimcase and the self.simcase. To analyze gust loads, horizontal level flight (nZ=1) FL000 at trim conditions was selected. Again the mass configuration M3 was chosen. The gust gradient selected for this example is the gust gradient of 23 meters, which corresponds to the limit case. In the simulation a time step of 0.01 seconds and a simulation time of 2 seconds were utilized. The gust parameters were obtained in accordance with CS-25 {cite}`cs25`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "40b57f40-c219-4f7d-9541-b7a093456f4b", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + " \n", + " self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 1,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0 ,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " }]\n", + " \n", + " self.simcase = [{'dt': 0.01, # Time step size of the output in [s]\n", + " # Final simulation time in [s]\n", + " 't_final': 2.0,\n", + " # True or False, enables 1-cosine gust according to CS-25\n", + " 'gust': True,\n", + " # Gust gradient H (half gust length) in [m]\n", + " 'gust_gradient': 23.0,\n", + " # Orientation of the gust in [deg], 0/360 = gust from bottom, 180 = gust from top,\n", + " # 90 = gust from the right, 270 = gust from the left, arbitrary values possible\n", + " # (rotation of gust direction vector about Nastran's x-axis pointing backwards)\n", + " 'gust_orientation': 0,\n", + " # Gust parameters according to CS-25 to calculate the gust velocity\n", + " # MD is the dive speed in Mach\n", + " 'gust_para': {'Z_mo': 8046.72, 'MLW': 11793.40, 'MTOW': 11883.98, 'MZFW': 10594.47, 'MD': 0.334,\n", + " 'T1': 0.0},\n", + " # Alternatively, give gust velocity / Vtas directly\n", + " # 'WG_TAS': 0.1,\n", + " # True or False, enables continuous turbulence excitation\n", + " 'turbulence': False,\n", + " # True or False, calculates limit turbulence according to CS-25\n", + " 'limit_turbulence': False,\n", + " # True or False, enables playback of control surface signals via efcs\n", + " 'cs_signal': False,\n", + " # True or False, enables a generic controller e.g. to maintain p, q and r\n", + " 'controller': False,\n", + " # True or False, enables a generic landing gear\n", + " 'landinggear': False,\n", + " # True or False, enables calculation of rigid and elastic derivatives\n", + " 'derivatives': False,\n", + " # True or False, enables flutter check with k, ke or pk method\n", + " 'flutter': False,\n", + " }]" + ] + }, + { + "cell_type": "markdown", + "id": "5f9cd38e-c690-4513-9654-4e7bc1424eea", + "metadata": {}, + "source": [ + "In order to obtain the results the following 'launch.py' file is run. The results can be found in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "29800512-fa3e-408d-b698-2c37a048fa55", + "metadata": { + "scrolled": true, + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_gust_H23\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 1 Mach number(s)...\n", + "INFO: done in 0.38 [sec].\n", + "INFO: Calculating unsteady AIC matrices (1056 panels, k=[0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0] (Nastran Definition!)) for 1 Mach number(s)...\n", + "INFO: done in 29.33 [sec].\n", + "INFO: Performing rational function approximation (RFA) on AIC matrices with 4 poles...\n", + "INFO: - solving B = A*x with least-squares method\n", + "INFO: - root-mean-square error(s): \n", + "INFO: k = 0.001 , RMSE_real = 0.0005720125389303132, RMSE_imag = 8.148603817532326e-06\n", + "INFO: k = 0.1 , RMSE_real = 0.00041807481116542486, RMSE_imag = 0.00048220449848666787\n", + "INFO: k = 0.3 , RMSE_real = 0.0006588508335750756, RMSE_imag = 0.0003448677217333894\n", + "INFO: k = 0.6 , RMSE_real = 0.0004535145823103877, RMSE_imag = 0.0012483389774543253\n", + "INFO: k = 1.0 , RMSE_real = 0.0017925260984709345, RMSE_imag = 0.0007525162422984316\n", + "INFO: k = 1.5 , RMSE_real = 0.0016356640792206374, RMSE_imag = 0.0011109219821269012\n", + "INFO: k = 2.0 , RMSE_real = 0.0008890505196004791, RMSE_imag = 0.0027241199582818056\n", + "INFO: k = 3.0 , RMSE_real = 0.000227376445707805, RMSE_imag = 0.001601657886637573\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 26 modes...\n", + "INFO: Found 26 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433853e+01 2.81886244e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01]\n", + "INFO: From these 26 modes, the following 20 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:34 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 1 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 1)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1730\n", + "INFO: Running time simulation for 2.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:37 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_gust_H23.pdf\n", + "INFO: start plotting cutting forces over time ...\n", + "INFO: plots saved as ./DC3_results/monstations_time_jcl_dc3_gust_H23.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_gust_H23.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_gust_H23', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + }, + { + "cell_type": "markdown", + "id": "3b8ae030-a789-4788-8781-5209a62b799f", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "As it was done for the analyses of the maneuver loads, two-dimensional load envelopes were constructed. Again, the Mx/My load envelope is going to be interpreted. In the following figure the torsional and bending moments envelope is shown for the left wing root. First, one can see that the envelope shape resembles a certain direct correlation between the bending and torsional moments. Second, one can see that the highest negative bending moments Mx and highest negative torsional moments My are reached by the gust load at the time instants between 0.36 and 0.48 seconds. The highest positive bending moments Mx and the highest positive torsional moments My are reached by the gust load at the time instants close to 1 second.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx and torsional moment My at the wing root.*\n", + "\n", + "Once again, one-dimensional envelopes along the wing span were studied to provide insights into the evolution of section loads across the wing. The next figure illustrates the envelope of the bending moment Mx along the wingspan. The highest positive bending moment arise from the gust load at the time instants between 0.50 and 0.54 seconds. On the other hand, the highest negative bending moment arises from the gust load at the time instants between 0.87 and 0.88 seconds.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx along the wing span.*\n", + "\n", + "Furthermore, in order to have a deeper understanding of the gust loads, the forces and moments of the different gust profiles were plotted over time. Observing, the bending moments Mx over time for the gust gradients H=23m at the wing root (figure below), it can be seen that the magnitude of the positive peak is very close to the negative peak, which was unexpected. The explanation for this comes further on in the Advanced Simulation section.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx over time at the wing root.*" + ] + }, + { + "cell_type": "markdown", + "id": "25ab2e5a-1041-41e1-aad8-a5fb5e20f005", + "metadata": {}, + "source": [ + "## Advanced Simulation\n", + "\n", + "In this section a more complete gust load analyses is presented. In the DC-3 case, for the gust load analyses, a gust gradient of 9, 16, 23, 30, 37, 51, 65, 79, 93 and 107 meters were selected in accordance with CS-25.341 {cite}`cs25`. In every simulation a time step of 0.01 seconds and a simulation time of 3 seconds were utilized. The gust parameters were obtained in accordance with CS-25 too.\n", + "\n", + "To run the simulation the 'launch.py' file is run. The results can be fouund in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "21457773-07c0-4c6a-9ff2-c521d26d4769", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_gust\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 1 Mach number(s)...\n", + "INFO: done in 0.30 [sec].\n", + "INFO: Calculating unsteady AIC matrices (1056 panels, k=[0.001, 0.1, 0.3, 0.6, 1.0, 1.5, 2.0, 3.0] (Nastran Definition!)) for 1 Mach number(s)...\n", + "INFO: done in 29.92 [sec].\n", + "INFO: Performing rational function approximation (RFA) on AIC matrices with 4 poles...\n", + "INFO: - solving B = A*x with least-squares method\n", + "INFO: - root-mean-square error(s): \n", + "INFO: k = 0.001 , RMSE_real = 0.0005720125389303132, RMSE_imag = 8.148603817532326e-06\n", + "INFO: k = 0.1 , RMSE_real = 0.00041807481116542486, RMSE_imag = 0.00048220449848666787\n", + "INFO: k = 0.3 , RMSE_real = 0.0006588508335750756, RMSE_imag = 0.0003448677217333894\n", + "INFO: k = 0.6 , RMSE_real = 0.0004535145823103877, RMSE_imag = 0.0012483389774543253\n", + "INFO: k = 1.0 , RMSE_real = 0.0017925260984709345, RMSE_imag = 0.0007525162422984316\n", + "INFO: k = 1.5 , RMSE_real = 0.0016356640792206374, RMSE_imag = 0.0011109219821269012\n", + "INFO: k = 2.0 , RMSE_real = 0.0008890505196004791, RMSE_imag = 0.0027241199582818056\n", + "INFO: k = 3.0 , RMSE_real = 0.000227376445707805, RMSE_imag = 0.001601657886637573\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 26 modes...\n", + "INFO: Found 26 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224905e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529788e+01\n", + " 2.68433851e+01 2.81886246e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01]\n", + "INFO: From these 26 modes, the following 20 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:34 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 10 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1479\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1628\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 3\n", + "INFO: (case 3 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1730\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 4\n", + "INFO: (case 4 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1808\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 5\n", + "INFO: (case 5 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1872\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 6\n", + "INFO: (case 6 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.1975\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 7\n", + "INFO: (case 7 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.2057\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 8\n", + "INFO: (case 8 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.2125\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 9\n", + "INFO: (case 9 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.2183\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 10\n", + "INFO: (case 10 of 10)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 26 variables...\n", + "INFO: The solution converged.\n", + "INFO: Adding 1056 x 4 unsteady lag states to the system\n", + "INFO: Init model equations of type \"Unsteady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: CS25_Uds is set up with flight profile alleviation factor Fg = 0.9164764669699965\n", + "INFO: Gust set up with initial Vtas = 70.0000, t1 = 0.0, WG_tas = 0.2235\n", + "INFO: Running time simulation for 3.0 sec...\n", + "INFO: Simulation finished. Running (again) with full outputs at selected time steps...\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: reducing dyn2stat data...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:09:13 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_gust.pdf\n", + "INFO: start plotting cutting forces over time ...\n", + "INFO: plots saved as ./DC3_results/monstations_time_jcl_dc3_gust.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_gust.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_gust', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + }, + { + "cell_type": "markdown", + "id": "e17d6274-c0ef-4d3a-b164-23075284f454", + "metadata": {}, + "source": [ + "## Results Advanced Simulation\n", + "\n", + "To study the gust loads the (1-cos) Discrete Gust Model was implemented in Loads Kernel. The gust analyses was performed in the same conditions as the maneuvers at True Airspeed (TAS) of 70 m/s and Flight Level FL000 at trim conditions. In accordance to CS-25.341 {cite}`cs25` the gust profiles at sea level were obtained, and are exposed in the next figure.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Gust shapes at sea-level according to CS-25.*\n", + "\n", + "To have a more detailed understanding of the gust shapes, the gust gradients and their corresponding peak velocities are listed in the following table. Concerning the orientation of the gust, only vertical gusts are going to be studied.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Gust gradients and corresponding peak velocities.*\n", + "\n", + "As it was done for the analyses of the maneuver loads, two-dimensional load envelopes were constructed. Again, the Mx/My load envelope is going to be interpreted. In the figure below the torsional and bending moments envelope is shown for the left wing root. First, one can see that the envelope shape resembles a certain direct correlation between the bending and torsional moments. Second, one can see that the highest negative bending moments Mx are reached by the gust loads with gust gradients H=16, 23, 30 and 37 m, while the highest negative torsional moments My are reached by the gust loads with gust gradient H=9 m. The highest positive bending moments Mx are reached by the gust load with gust gradient H=51 m, this gust load corresponds to the smallest negative torsional moments My. Third, both maneuver and gust envelopes are very close to each other, which is an indicator for well-selected load cases which harmonize with each other in the sense that there are no extreme loads cases which dominate the design. That can be concluded comparing the figure below with the analogous figure obtained in the manuever load analyses.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx and torsional moment My at the wing root.*\n", + "\n", + "Once again, one-dimensional envelopes along the wing span were studied to provide insights into the evolution of section loads across the wing. The following figure illustrates the envelope of the bending moment Mx along the wing span. Comparing the results between the maneuver and gust loads, one can see that magnitude of the values is the same. Due to the fact that the results are symmetric, the conclusions drawn from the right wing (y>0) are valid for the left wing. In the inboard part of the right wing, the highest positive bending moment arise from the gust load with gust gradient H=23 m. For the outboard section the highest values correspond to the gust load with gust gradient H=16 m, except for the last three monitoring stations closer to the tip. For the monitoring station located at the tip the\n", + "highest value is for the gust load with gust gradient H=51 m and for the other two is for the gust load with gust gradient H=9 m. On the other hand, the highest negative bending moment arises from the gust load with gust gradient H=51 m for the entire wing span, except for the tip for which the highest value is for the gust load with gust gradient H=37 m.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx along the wing span.*\n", + "\n", + "Furthermore, in order to have a deeper understanding of the gust loads, the forces and moments of the different gust profiles were plotted over time. The time step chosen was 0.01 seconds which is a step size with a good balance between accuracy and computational efficiency. It maintains acceptable results and at the same time is large enough to not be very computationally demanding. The interval time selected\n", + "for the gust analyses was 3 seconds, which is more than enough to capture the several peaks for each gust profile. Observing, the bending moments Mx over time for the different gust gradients at the wing root (figure below), it can be seen that the magnitude of the positive peaks is very close to the negative peaks, which was unexpected. Moreover, the highest positive peak is reached for a gust gradient of H=23 m. Empirical data collected in the 1940s shows that the highest gust excitations on legacy aircraft occur at a gradient corresponding to 12.5 spatial chord lengths on average. Being the mean aerodynamic chord c=3.508 m, the highest positive peak should have been reached around a gust gradient of H=44 m, almost the value obtained.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx along the wing span.*\n", + "\n", + "It was tought that the source was possible a coupling between the gusts and one of the longitudinal flight mechanics modes, namely the short period. One way to eliminate this coupling is to decreased the distance between the aerodynamic center and the center of gravity, so the static margin. The aircraft model has a very high static margin in the M3 mass configuration, which was reduced to approximately 10%. This resulted in the magnitude of the positive and negative peaks of the moments/forces over time plots to change so that the magnitude of these peaks was then much different. On top of that, the highest peak was then arising from a gust with a gust gradient between H=37 m\n", + "and H=51 m. This means the empirical predicted gust gradient value of H=44 m falls in this range. This goes to show that the unexpected gust load results obtained are justified by this gust and short period coupling that happens for gust profiles with small gust gradients.\n", + "\n", + "For additional information about the meanuver load results please refer to {cite}`Carvalho2024`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/images/MxMy.png b/doc/tutorials/images/MxMy.png new file mode 100644 index 00000000..fcc7872a Binary files /dev/null and b/doc/tutorials/images/MxMy.png differ diff --git a/doc/tutorials/images/MxMy_gust_adv.png 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b/doc/tutorials/images/trim_deflections.png new file mode 100644 index 00000000..2e64f0ca Binary files /dev/null and b/doc/tutorials/images/trim_deflections.png differ diff --git a/doc/tutorials/images/trim_results.png b/doc/tutorials/images/trim_results.png new file mode 100644 index 00000000..865b8a3a Binary files /dev/null and b/doc/tutorials/images/trim_results.png differ diff --git a/doc/tutorials/intro.md b/doc/tutorials/intro.md index 66343e9a..e1a8650b 100644 --- a/doc/tutorials/intro.md +++ b/doc/tutorials/intro.md @@ -1,11 +1,14 @@ # Loads Kernel Tutorials -While the User Guide {cite}`vos_loads_2020` mainly describes the theoretical background, this collection of tutorials focuses on how to use the software. +While the User Guide {cite}`vos_loads_2020` mainly describes the theoretical background, this collection of tutorials focuses on how to use the software. This collection of tutorials include a short introduction on how to use Loads Kernel, a detailed simulation set-up, and the simulations and results of the trim, maneuvers and gust loads, and the flutter check simulations. +The aeroelastic model of the Douglas DC3 as well as most parts of this tutorial are created by Francisco Carvalho during his master thesis {cite}`Carvalho2024`. + +## Overview ```{tableofcontents} ``` - +## Bibliography ```{bibliography} :style: unsrt ``` \ No newline at end of file diff --git a/doc/tutorials/maneuver_loads.ipynb b/doc/tutorials/maneuver_loads.ipynb new file mode 100644 index 00000000..4c50e8c4 --- /dev/null +++ b/doc/tutorials/maneuver_loads.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c5274f36-3b63-4ab2-ab36-037d9232c388", + "metadata": {}, + "source": [ + "# Maneuver Loads\n", + "\n", + "In this section a maneuver loads simulation is going to be studied. The simulation set-up in Loads Kernel was already shown in detail in a previous notebook. Hence, the focus will be more in the results and post processing." + ] + }, + { + "cell_type": "markdown", + "id": "e3a91932-9728-47cd-9c83-ebc245383f36", + "metadata": {}, + "source": [ + "## Simulation Set-up\n", + "\n", + "The simulation set-up for the maneuver loads simulation is very similar to the set-up done for the trim analyses. The only difference is in the self.trimcase set up. In this case two extra maneuvers are included. The three maneuvers used for the trim cases were used: the horizontal level flight (nZ=1), the push-down maneuver (nZ=-1) and the pull-up maneuver (nZ=2.5). Additionally, two roll maneuvers were added. From CS-25 Amendment 28 {cite}`cs25`, the aircraft at trim with a 30◦ bank angle has to be able to roll to a 30◦ bank angle in the other direction in not more than 11 seconds. That corresponds to a roll velocity of p=5.45◦/s. A roll velocity of p=5.45◦/s is actually quiet slow and it does not produce very interesting results in the maneuver loads. For that reason, this roll maneuver is going to be performed in 3 seconds rather than 11. Hence, a roll velocity of p=20◦/s. All these maneuvers were performed at True Airspeed (TAS) of 70m/s and Flight Level FL000. This trim velocity was chosen again because is roughly the middle point between the maneuvering speed VA=113KEAS=58.13m/s and the design speed for maximum gust intensity VB=170KEAS=87.46m/s." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "375cd0f5-e922-4dca-829d-3dd411f4513e", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 1,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0 ,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.pushdown', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 2,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': -1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.pullup', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 3,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 2.5,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.roll_right', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 4,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.349,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.roll_left', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 5,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': -0.349,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " }]\n", + " \n", + " self.simcase = jcl_helper.generate_empty_listofdicts(self.trimcase)\n", + " # End\n" + ] + }, + { + "cell_type": "markdown", + "id": "f95369ce-148b-427a-a6e9-034715011bbb", + "metadata": {}, + "source": [ + "In order to obtain the results the following 'launch.py' file is run. The results can be found in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38c976bf-f470-4c0f-b41e-c0c07d739e85", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_maneuvers\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 5 empty dicts.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 2 Mach number(s)...\n", + "INFO: done in 1.04 [sec].\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 76 modes...\n", + "INFO: Found 76 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433851e+01 2.81886245e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01 3.71484020e+01 3.74387461e+01\n", + " 4.65087795e+01 4.66555395e+01 5.19704569e+01 5.31897897e+01\n", + " 5.59827047e+01 5.61588818e+01 5.79161872e+01 5.83859187e+01\n", + " 6.49254027e+01 6.49636994e+01 6.52953993e+01 6.55893563e+01\n", + " 6.76977070e+01 7.39272939e+01 7.40020584e+01 7.92493678e+01\n", + " 7.98448917e+01 8.44686963e+01 8.44977700e+01 8.89696420e+01\n", + " 8.90380160e+01 9.17266784e+01 9.27516791e+01 1.00718906e+02\n", + " 1.00742526e+02 1.05800840e+02 1.06520421e+02 1.09898735e+02\n", + " 1.10071029e+02 1.12603325e+02 1.12677121e+02 1.21628942e+02\n", + " 1.21633308e+02 1.25797288e+02 1.25826624e+02 1.30369009e+02\n", + " 1.30516978e+02 1.37566261e+02 1.37608584e+02 1.41676350e+02\n", + " 1.44303157e+02 1.44811563e+02 1.47038007e+02 1.47151873e+02\n", + " 1.55547154e+02 1.56081100e+02 1.56183006e+02 1.57181628e+02]\n", + "INFO: From these 76 modes, the following 70 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n", + " 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", + " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:04 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 5 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 5)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pushdown\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 5)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pullup\n", + "INFO: subcase: 3\n", + "INFO: (case 3 of 5)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.roll_right\n", + "INFO: subcase: 4\n", + "INFO: (case 4 of 5)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.roll_left\n", + "INFO: subcase: 5\n", + "INFO: (case 5 of 5)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:04 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_maneuvers.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing trim results to: ./DC3_results/trim_results_jcl_dc3_maneuvers.csv\n", + "INFO: writing successful trimcases cases to: ./DC3_results/successful_trimcases_jcl_dc3_maneuvers.csv\n", + "INFO: writing failed trimcases cases to: ./DC3_results/failed_trimcases_jcl_dc3_maneuvers.csv\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_maneuvers.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_maneuvers', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + }, + { + "cell_type": "markdown", + "id": "db6a4dc7-b1ed-4f33-8b06-a49ad44aca80", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "The resulting nodal loads were integrated at designated monitoring stations along the wing span to derive section loads, utilizing the Loads Kernel. In this section only the Loads Kernel results are analysed, since in the previous section the verisimilitude between MCS Nastran and Loads Kernel was proved. A total of 16 monitoring stations were strategically placed on each wing. Key quantities of interest for the wing include the bending moment Mx, torsional moment My, and shear force Fz.\n", + "\n", + "To identify the critical load cases, two-dimensional load envelopes were constructed, illustrating combinations of two significant section loads. For the wing, the pertinent load envelopes are Fz/Mx and Mx/My . But only the Mx/My load envelope is going to be analysed. Given the wing’s configuration with an outboard section featuring sweep and a rectangular inboard section, section loads were computed in the global coordinate system (x-y-z directions: aft-right-up) for the inboard section, and in a local coordinate system for the outboard section.\n", + "\n", + "For instance, next figure illustrates the load envelope detailing the torsional moment My and bending moment Mx at the wing root. It is evident that the most significant negative bending moment Mx aligns with the pull-up maneuver, while the highest positive bending moment Mx corresponds to the push-down maneuver. Similarly, regarding the torsional moment My , the peak negative torsional moment My coincides with the pull-up maneuver, and the maximum positive torsional moment My aligns with the push-down maneuver.\n", + "\n", + "Furthermore, it’s noticeable that the roll maneuvers exhibit identical bending moments Mx to horizontal level flight since the load factor remains constant at nZ=1 for all three maneuvers. However, in terms of the torsional moment My , the roll maneuvers display an offset (one for each side) compared to horizontal level flight. This outcome was anticipated as the roll maneuver involves aileron deflection, contributing to an increase in elastic wing twist.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx and torsional moment My at the wing root.*\n", + "\n", + "Moreover, one-dimensional envelopes along the wing span provide insights into the evolution of section loads across the wing. Next figure illustrates the envelope of the bending moment Mx along the wing span, with each red dot denoting the highest positive and negative bending moment at each monitoring station. A comparison between the results for the right and left wings reveals symmetrical bending moments with reversed signs, attributable to the rearward orientation of the x-axis in the coordinate system for both sides. In the left wing, the most significant negative bending moment arises from the pull-up maneuver with a load factor of nZ=2.5. Conversely, the largest positive bending moment stems from the push-down maneuver with a load factor of nZ=-1. Hence, a negative sign denotes an upward bending moment, while a positive sign indicates a downward bending moment. Conversely, for the right wing, the opposite occurs due to the definition of the x-axis. This outcome aligns with expectations, as the pull-up maneuver induces upward wing bending, while the push-down maneuver leads to downward bending. The highest bending moments Mx are at the root as expected.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Bending moment Mx along the wing span.*\n", + "\n", + "For additional information about the meanuver load results please refer to {cite}`Carvalho2024`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/references.bib b/doc/tutorials/references.bib index 62f454fc..3d86ec60 100644 --- a/doc/tutorials/references.bib +++ b/doc/tutorials/references.bib @@ -22,3 +22,38 @@ @techreport{vos_implementation_2020 year = {2020}, file = {Voß - 2020 - An Implementation of the Vortex Lattice and the Do.pdf:/work/voss_ar/Literatur/Paper/Voß - 2020 - An Implementation of the Vortex Lattice and the Do.pdf:application/pdf}, } + +@mastersthesis{Carvalho2024, + author = {Francisco Carvalho}, + title = {Set-up of a generic aeroelastic simulation model for demonstration and software testing purposes}, + school = {KTH Royal Institute of Technology}, + year = {2024}, + month = {July}, + type = {Master's Thesis}, + institution = {DLR - Deutsches Zentrum für Luft- und Raumfahrt, Institute of Aeroelasticity}, +} + + + +@manual{Nastran, + title = {{MSC Nastran 2023.1 Quick Reference Guide}}, + year = {2023}, + organization = {MSC.Software Corporation}, +} + + + +@manual{modgen2023, + author = {Thomas Klimmek}, + title = {ModGen User's Manual}, + year = {2023}, + note = {Version 2023-01-17}, +} + + + +@manual{cs25, + title = {Certification Specifications and Acceptable Means of Compliance for Large Aeroplanes (CS-25)}, + organization = {European Union Aviation Safety Agency (EASA)}, + year = {2007}, +} diff --git a/doc/tutorials/simulation_set_up.ipynb b/doc/tutorials/simulation_set_up.ipynb new file mode 100644 index 00000000..c507356d --- /dev/null +++ b/doc/tutorials/simulation_set_up.ipynb @@ -0,0 +1,716 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "20bbe2cd-fc03-402d-b42c-06bf9a653f02", + "metadata": {}, + "source": [ + "# Simulation Set-up\n", + "\n", + "In this section a simulation set-up is going to be conducted. The simulation set-up in Loads Kernel is going to be shown in detail step by step. The Phyton file used for this notebook is named 'jcl_dc3_trim.py' and can be found in the folder tutorials -> DC3_model -> JCLs. To start the libraries needed are imported. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "110d88e0-4304-49cd-b545-597b2198b450", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import platform\n", + "import os\n", + "from loadskernel.units import ft2m, tas2Ma\n", + "from loadskernel import jcl_helper\n", + "import pathlib" + ] + }, + { + "cell_type": "markdown", + "id": "30e69626-3527-471e-9eea-bf45e383f93f", + "metadata": {}, + "source": [ + "In the second step of the set-up simulation process, a name for the aircarft and some general parameters need to be assigned. This parameters are essential to then compute force and moment coefficients, as well as the stability derivatives." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c2e8223-fa46-4a29-8ce7-518e13d871d4", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " model_root = pathlib.Path(__file__).parent.parent.resolve()\n", + "\n", + " # Give your aircraft a name and set some general parameters\n", + " self.general = {'aircraft': 'DC3',\n", + " # Reference span width (from tip to tip)\n", + " 'b_ref': 29.0,\n", + " # Reference chord length\n", + " 'c_ref': 3.508,\n", + " # Reference area\n", + " 'A_ref': 91.7,\n", + " # Mean aerodynamic center, also used as moments reference point\n", + " 'MAC_ref': [8.566, 0.0, 0.0],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "4348eb8c-4f9b-4f2a-8f50-8deeb1cdf173", + "metadata": {}, + "source": [ + "Afterwards, the eletronic flight control system is set-up. The electronic flight control system (EFCS) provides the \"wireing\" of the pilot \n", + "commands xi, eta and zeta with the control surface deflections. This is aircraft specific and needs to be implemented as a python module." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "60f76302-fd1c-47bb-aeab-2aa83e952b5f", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + " \n", + " self.efcs = {'version': 'efcs_dc3', # Name of the corresponding python module\n", + " # Path where to find the EFCS module\n", + " 'path': os.path.join(model_root, 'efcs'),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "a39cfaed-f838-4549-b6ac-0666f7a8d510", + "metadata": {}, + "source": [ + "The next step is to read the structural geometry. This consists in reading the bdf file(s) with GRIDs, RBE2s, CBARs, PBARs, CONM2s, MATs and CORDs (CORD1R and CORD2R) cards; the file(s) that contains the monitoring stations; and, to finalize, it reads the mass and stiffness matrices, obtained using MSC Nastran. Important to refer that all these files correpond to the structure only configuration, hence no payload, fuel or system masses. For more information about the MSC Nastran cards please refer to {cite}`Nastran`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c6613729-8d49-4f14-a4f8-200b922f7c40", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.geom = {'method': 'mona', # ModGen and/or Nastran (mona) BDFs\n", + " # bdf file(s) with GRIDs and CORDs (CORD1R and CORD2R)\n", + " 'filename_grid': [os.path.join(model_root, 'fem', 'structure_only.bdf')],\n", + " # bdf file(s) with CQUADs and CTRIAs, for visualization only, e.g. outer skin on the aircraft\n", + " # 'filename_shell': [],\n", + " # bdf file(s) with MONPNT-cards\n", + " 'filename_monpnt': os.path.join(model_root, 'fem', 'export_monitoring-stations.csv'),\n", + " # The following matrices are required for some mass methods. However, the stiffness is geometry\n", + " # and not mass dependent. Overview:\n", + " # KGG via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan' or 'B2000'\n", + " # USET via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan'\n", + " # matrix GM via DMAP Alter und OP4 - required for mass method = 'modalanalysis', 'guyan'\n", + " # bdf file(s) with ASET1-card - required for mass method = 'guyan'\n", + " # matrix R_trans frum B2000 - required for mass method = 'B2000'\n", + " 'filename_h5': os.path.join(model_root, 'fem', 'SOL103_structure_only.mtx.h5'),\n", + " 'filename_uset': os.path.join(model_root, 'fem', 'uset.op2'),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "ef4aaf95-a834-4294-a787-2eca1174925e", + "metadata": {}, + "source": [ + "Following the structure model, the aerodynamic model needs to be set-up. The set-up starts with the selection of simulation aerodynamic method. In this specific simulation case the 'mona_steady' method was used. Nevertheless, five more methods are available (for more information refer to {cite}`vos_loads_2020`). Then, true or false is chosen for the field 'flex'. Activating this 'flex' mode means activating the aerodynamic feedback of elastic structure on aerodynamics. In the case of desactivation of this 'flex' mode deformation are still visible, but there is no coupling between elastic structure deformation and aerodynamic forces. Furthermore, the aerodynamic grid is read. This process is done reading the file(s) containing the CAERO1s (or CAERO7s, or CQUAD4s) , AESURFs and AELISTs cards. Also, the DMI matrix for camber and twist corrrection is read. Moreover, the hingeline of the control surfaces is defined, followed by the selection of the aerodynamic model. In the case of the flutter analyses the Doublet-Lattice Method (DLM) is chosen. After selecting the aerodynamic model, a reference velocity(ies) and a serie of reduced frequencies are descriminated for the DLM calculations. At the end, the number of poles for Rational Function Approximation (RFA) is specified." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c132bace-23e0-440a-992b-11aa77196b27", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.aero = {'method': 'mona_steady',\n", + " # 'mona_steady' - steady trim and quasi-steady time domain simulations\n", + " # 'mona_unsteady' - unsteady time domain simulation based on the RFA, e.g. for gust\n", + " # 'freq_dom' - frequency domain simulations, e.g. gust, continuous turbulence, flutter, etc\n", + " # 'nonlin_steady' - steady trim and quasi-steady time domain simulations with some non-linearities\n", + " # 'cfd_steady' - steady trim\n", + " # 'cfd_unsteady' - unsteady time domain simulation, e.g. for gust\n", + " #\n", + " # True or False, aerodynamic feedback of elastic structure on aerodynamics can be deactivated.\n", + " # You will still see deformations, but there is no coupling.\n", + " 'flex': True,\n", + " # aerogrid is given by CAERO1, CAERO7 or by CQUAD4 cards\n", + " 'method_caero': 'CAERO1',\n", + " # bdf file(s) with CAERO1 or CQUAD4-cards for aerogrid. IDs in ascending order.\n", + " 'filename_caero_bdf': [os.path.join(model_root, 'aero', 'vt', 'vt.CAERO1'),\n", + " os.path.join(model_root, 'aero', 'left-ht', 'left-ht.CAERO1'),\n", + " os.path.join(model_root, 'aero', 'right-ht', 'right-ht.CAERO1'),\n", + " os.path.join(model_root, 'aero', 'left-wing', 'left-wing.CAERO1'),\n", + " os.path.join(model_root, 'aero', 'right-wing', 'right-wing.CAERO1'),\n", + " ],\n", + " # DMI Matrix for camber and twist correction. Same order as the aerogrid.\n", + " 'filename_DMI_W2GJ': [os.path.join(model_root, 'fem', 'w2gj_list.DMI_merge')],\n", + " # bdf file(s) with AESURF-cards\n", + " 'filename_aesurf': [os.path.join(model_root, 'aero','vt', 'vt.AESURF'),\n", + " os.path.join(model_root, 'aero', 'left-ht','left-ht.AESURF'),\n", + " os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AESURF'),\n", + " os.path.join(model_root, 'aero', 'left-wing','left-wing.AESURF'),\n", + " os.path.join(model_root, 'aero', 'right-wing','right-wing.AESURF'),\n", + " ],\n", + " # bdf file(s) with AELIST-cards\n", + " 'filename_aelist': [os.path.join(model_root, 'aero', 'vt', 'vt.AELIST'),\n", + " os.path.join(model_root, 'aero', 'left-ht', 'left-ht.AELIST'),\n", + " os.path.join(model_root, 'aero', 'right-ht', 'right-ht.AELIST'),\n", + " os.path.join(model_root, 'aero', 'left-wing','left-wing.AELIST'),\n", + " os.path.join(model_root, 'aero', 'right-wing','right-wing.AELIST'),\n", + " ],\n", + " # The hingeline of a CS is given by a CORD. Either the y- or the z-axis is taken as hingeline. 'y', 'z'\n", + " 'hingeline': 'y',\n", + " # 'vlm' (panel-aero), 'dlm' (panel-aero) or 'nastran' (external form matrices)\n", + " 'method_AIC': 'vlm',\n", + " 'key': ['VC', 'VD'],\n", + " 'Ma': [0.27, 0.34],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "b8908798-5abf-4627-81d1-5c1620c8ee76", + "metadata": {}, + "source": [ + "Splining in aeroelastic analyses is a crucial technique for accurately tranferring data between structural and aerodynamic model, ensuring precise and stable simulations of the interactions between aerodynamic forces and structural deformations. To set the way in which the aerodynamic forces are applied to the structure the following code lines are used. For that, the method needs to be chosen (for more information about the different methods please refer to {cite}`vos_loads_2020`); then, there is the possibility to use only a subset of the structural grid for splinning; lastly, the bdf file(s) with GRIDs to be used as the subset is called." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "675e8c90-6e86-4e72-bc2c-a5e65534272c", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.spline = {'method': 'nearest_neighbour', # Options: 'nearest_neighbour', 'rbf' or 'nastran'\n", + " # Possibility to use only a subset of the structural grid for splining. True or False\n", + " 'splinegrid': False,\n", + " # bdf file(s) with GRIDs to ne used\n", + " 'filename_splinegrid': ['splinegrid.bdf']\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "234f3365-0435-4176-9580-7ba1fa31e3f8", + "metadata": {}, + "source": [ + "The next step is to specify the settings for the structural dynamics. In the case of the DC-3 model the method chosen was 'modalanalyses' (for more information about the methods please refer to {cite}`vos_loads_2020`). Afterwards, the file .h5 from MSC Nastran SOL103 is called. To finalize there is an option to omit the first six modes (the rigid body modes), and the list(s) of modes to use." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3e5d5af6-fc6c-43d8-af87-144890972dd1", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.mass = {'method': 'modalanalysis', # Inplemented interfaces: 'f06', 'modalanalysis', 'guyan', 'CoFE', 'B2000'\n", + " 'key': ['M3'],\n", + " # MGG via DMAP Alter and OP4 - always required\n", + " 'filename_h5': [\n", + " os.path.join(model_root, 'fem', 'SOL103_M3.mtx.h5'),\n", + " ],\n", + " # True or False, omits first six modes\n", + " 'omit_rb_modes': True,\n", + " # list(s) of modes to use\n", + " 'modes': [np.arange(1, 71), np.arange(1, 71), np.arange(1, 71), np.arange(1, 71)],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "df904e08-46cc-48e8-be0a-90e250c194d7", + "metadata": {}, + "source": [ + "Thereafter, the modal damping can be applied as a factor of the stiffness matrix. As well, the list of altitudes is discriminated and the method for the rigid body equations of motion is selected." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c437d35e-3702-4732-ac70-5c5903a586a4", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.damping = {'method': 'modal',\n", + " 'damping': 0.02,\n", + " }\n", + " self.atmo = {'method': 'ISA',\n", + " 'key': ['FL000', 'FL055', 'FL075', 'FL210'],\n", + " # Altitude in meters\n", + " 'h': ft2m([0, 5500, 7500, 21000,]),\n", + " }\n", + " self.eom = {'version': 'waszak'} # 'linear' or 'waszak'" + ] + }, + { + "cell_type": "markdown", + "id": "bdfd4939-467c-4b9b-a4a6-da6ae85f46b1", + "metadata": {}, + "source": [ + "This section controls the automatic plotting and selection of dimensioning load cases. Simply put a list of names of the monitoring stations into the dictionary of possible load plots listed below. This will generate a pdf document and MSC Nastran force and moment cards for the dimensioning load cases. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1da8cee2-3d1d-47e6-8e01-84856c93f504", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.loadplots = {'potatos_fz_mx': [],\n", + " 'potatos_mx_my': ['WL01','WL03','WL05','WL07','WL09','WL11','WL13','WL15','WL17','WL19','WL21','WL23','WL25','WL27','WL29','WL31','WR31','WR29','WR27','WR25','WR23','WR21','WR19','WR17','WR15','WR13','WR11','WR09','WR07','WR05','WR03','WR01'],\n", + " 'potatos_fz_my': [],\n", + " 'potatos_fy_mx': [],\n", + " 'potatos_mx_mz': [],\n", + " 'potatos_my_mz': [],\n", + " 'cuttingforces_wing': ['WL01','WL03','WL05','WL07','WL09','WL11','WL13','WL15','WL17','WL19','WL21','WL23','WL25','WL27','WL29','WL31','WR31','WR29','WR27','WR25','WR23','WR21','WR19','WR17','WR15','WR13','WR11','WR09','WR07','WR05','WR03','WR01'],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "8e6a552c-4b54-4c5b-a527-1a028a6c7623", + "metadata": {}, + "source": [ + "The trimcase defines the maneuver load case, one dictionary per load case. There may be hundreds or thousands of load cases, so at some point it might be beneficial to script this section or import an excel sheet. Here, for the specific case of this simulation three different trim cases were chosen. The first one correspond to horizontal level flight (nZ=1) at 70 m/s TAS and FL000. The second case concerns a push-down maneuver (nZ=-1) at the same velocity and altitude conditions. At last, a pull-up manuever (nZ=2.5) at the same velocity and altitude conditions is set. For the three cases the mass configuration M3 was chosen." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "adc94672-0e88-4eba-9e85-4c3faed56dd0", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.trimcase = [{'desc': 'CC.M3.OVCFL000.level', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 1,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0 ,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.pushdown', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 2,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': -1.0,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0 / 180.0 * np.pi,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0 / 180.0 * np.pi,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " },\n", + " {'desc': 'CC.M3.OVCFL000.pullup', # Descriptive string of the maneuver case\n", + " # Kind of trim condition, blank for trim about all three axes, for more trim conditions see\n", + " # trim_conditions.py\n", + " 'maneuver': '',\n", + " # Subcase ID number, for Nastran in acending order\n", + " 'subcase': 3,\n", + " # Setting of the operational point\n", + " # The flight speed is given by the Mach number\n", + " 'Ma': tas2Ma(70.0, 0.0),\n", + " # Aero key\n", + " 'aero': 'VC',\n", + " # Atmo key\n", + " 'altitude': 'FL000',\n", + " # Mass key\n", + " 'mass': 'M3',\n", + " # Load factor Nz\n", + " 'Nz': 2.5,\n", + " # Velocities and accelerations given in ISO 9300 coordinate system (right-handed, forward-right-down)\n", + " # Roll rate in rad/s\n", + " 'p': 0.0,\n", + " # Pitch rate in rad/s\n", + " 'q': 0.0,\n", + " # Yaw rate in rad/s\n", + " 'r': 0.0,\n", + " # Roll acceleration in rad/s^2\n", + " 'pdot': 0.0,\n", + " # Pitch acceleration in rad/s^2\n", + " 'qdot': 0.0,\n", + " # Yaw acceleration in rad/s^2\n", + " 'rdot': 0.0,\n", + " }]" + ] + }, + { + "cell_type": "markdown", + "id": "17aceb3a-138c-4958-8a2a-393ec6d10bc3", + "metadata": {}, + "source": [ + "For every trimcase, a corresponding simcase is required. For maneuvers, it may be empty self.simcase = [{}]." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "957e3b74-7d52-4720-899c-85030d8453cf", + "metadata": {}, + "outputs": [], + "source": [ + "class jcl:\n", + "\n", + " def __init__(self):\n", + "\n", + " self.simcase = jcl_helper.generate_empty_listofdicts(self.trimcase)" + ] + }, + { + "cell_type": "markdown", + "id": "5e31f5d9-6185-4c3f-b316-bdda8f244083", + "metadata": {}, + "source": [ + "After completing the set-up, the 'launch.py' file (found in tutorials -> DC3_model -> JCLs) is run. The name of the file, as well as the input and output file locations are specified. The results can be found in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6b761e81-72b9-4260-872d-1727fbe39099", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_trim\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 3 empty dicts.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 2 Mach number(s)...\n", + "INFO: done in 1.03 [sec].\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 76 modes...\n", + "INFO: Found 76 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433851e+01 2.81886245e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01 3.71484020e+01 3.74387461e+01\n", + " 4.65087795e+01 4.66555395e+01 5.19704569e+01 5.31897897e+01\n", + " 5.59827047e+01 5.61588818e+01 5.79161872e+01 5.83859187e+01\n", + " 6.49254027e+01 6.49636994e+01 6.52953993e+01 6.55893563e+01\n", + " 6.76977070e+01 7.39272939e+01 7.40020584e+01 7.92493678e+01\n", + " 7.98448917e+01 8.44686963e+01 8.44977700e+01 8.89696420e+01\n", + " 8.90380160e+01 9.17266784e+01 9.27516791e+01 1.00718906e+02\n", + " 1.00742526e+02 1.05800840e+02 1.06520421e+02 1.09898735e+02\n", + " 1.10071029e+02 1.12603325e+02 1.12677121e+02 1.21628942e+02\n", + " 1.21633308e+02 1.25797288e+02 1.25826624e+02 1.30369009e+02\n", + " 1.30516978e+02 1.37566261e+02 1.37608584e+02 1.41676350e+02\n", + " 1.44303157e+02 1.44811563e+02 1.47038007e+02 1.47151873e+02\n", + " 1.55547154e+02 1.56081100e+02 1.56183006e+02 1.57181628e+02]\n", + "INFO: From these 76 modes, the following 70 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n", + " 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", + " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:04 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 3 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pushdown\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pullup\n", + "INFO: subcase: 3\n", + "INFO: (case 3 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:02 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_trim.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing trim results to: ./DC3_results/trim_results_jcl_dc3_trim.csv\n", + "INFO: writing successful trimcases cases to: ./DC3_results/successful_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing failed trimcases cases to: ./DC3_results/failed_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_trim.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_trim', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/trim.ipynb b/doc/tutorials/trim.ipynb new file mode 100644 index 00000000..b6103697 --- /dev/null +++ b/doc/tutorials/trim.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "080a4124-eda9-4ce0-a539-516bc4a13ae7", + "metadata": {}, + "source": [ + "# Simple Trimcases\n", + "\n", + "In this section a trim simulation is going to be present. The simulation set-up in Loads Kernel was already shown in detail in a previous notebook. Hence, the focus will be more in the results and post processing. The results obtained in Loads Kernel are discussed and compared with the MSC Nastran results." + ] + }, + { + "cell_type": "markdown", + "id": "2a353d1f-638b-495b-893c-e602562d1ecf", + "metadata": {}, + "source": [ + "## Simulation Set-up\n", + "\n", + "As disucssed previously, three trim cases were analysed. The three trim cases were performed at True Airspeed (TAS) of 70 m/s and Flight Level FL000. The first case corresponds to a horizontal level flight, a Load Factor of nZ=1; the second one to a push-down maneuver, a Load Factor of nZ=-1; and the third one to a pull-up maneuver, a Load Factor of nZ=2.5. These load cases where chosen having in consideration the flight envelope that was obtained using the CS-25 {cite}`cs25`.\n", + "\n", + "In order to obtain the results the following 'launch.py' file is run. The results can be found in the folder 'DC-3_results'." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e593fcac-7661-451c-ab04-2e9ed077362c", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: This is the log for process 0.\n", + "INFO: Starting Loads Kernel with job: jcl_dc3_trim\n", + "INFO: User carn_fr on schwalbe (Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28)\n", + "INFO: pre: True\n", + "INFO: main: True\n", + "INFO: post: True\n", + "INFO: test: False\n", + "INFO: --> Reading parameters from JCL.\n", + "INFO: Generated list of 3 empty dicts.\n", + "INFO: --> Starting preprocessing.\n", + "INFO: Building structural model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/structure_only.bdf\n", + "INFO: Found include(s):\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_FUS.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.GRID_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.RBE2_LREFAX_5400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/left-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-wing/export_left-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.GRID_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.RBE2_LREFAX_6400001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing/right-wing.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-wing//export_right-wing.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.GRID_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.RBE2_LREFAX_3330001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/left-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/left-ht/export_left-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.GRID_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.RBE2_LREFAX_3340001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/right-ht.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/right-ht/export_right-ht.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.GRID_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.RBE2_LREFAX_3320001\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.CORD2R_LREFAX\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/vt.MAT_ZR\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/vt/export_vt.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_left-nacell.csv\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/../fem/export_right-nacell.csv\n", + "INFO: The structural model consists of 278 grid points (1668 DoFs) and 7 coordinate systems.\n", + "INFO: Reading Monitoring Stations from MONPNTs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/export_monitoring-stations.csv\n", + "INFO: Splining (rigid body) of 1668 DOFs to 192 DOFs...\n", + "INFO: Building atmo model...\n", + "INFO: Building aero model...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.CAERO1\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.CAERO1\n", + "INFO: Constructing aero panels from CAERO cards\n", + "INFO: - from corner points and aero panels, constructing aerogrid\n", + "INFO: Read W2GJ data from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/w2gj_list.DMI_merge\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6336 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AESURF\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/vt/vt.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-ht/left-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-ht/right-ht.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/left-wing/left-wing.AELIST\n", + "INFO: Read from file: /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/aero/right-wing/right-wing.AELIST\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 6 DOFs...\n", + "INFO: Calculating steady AIC matrices (1056 panels, k=0.0) for 2 Mach number(s)...\n", + "INFO: done in 0.81 [sec].\n", + "INFO: The aerodynamic model consists of 1056 panels and 5 control surfaces.\n", + "INFO: Coupling aerogrid directly. Doing cleanup/thin out of strcgrid to avoid singularities (safety first!)\n", + "INFO: The spline model consists of 272 grid points.\n", + "INFO: Searching nearest neighbour of 1056 dependent nodes in 272 independent nodes...\n", + "INFO: Splining (rigid body) of 6336 DOFs to 1668 DOFs...\n", + "INFO: Building stiffness and mass model...\n", + "INFO: Reading matrix KGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Reading matrix GM from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_structure_only.mtx.h5\n", + "INFO: Read USET from OP2-file /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/uset.op2 ...\n", + "Table USET , bytes = 6935 [ 0 to 6936]\n", + "INFO: Extracting bit positions from USET to determine DoFs\n", + "INFO: Prepare stiffness matrices for independent and free DoFs (f-set)\n", + "INFO: Mass configuration 1 of 1: M3 \n", + "INFO: Reading matrix MGG from /data/carn_fr/LoadsKernel/doc/tutorials/DC3_model/fem/SOL103_M3.mtx.h5\n", + "INFO: Calculate center of gravity, mass and inertia (GRDPNT)...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Mass: 11883.983000000002\n", + "INFO: CG at: [8.62280419e+00 5.43474355e-08 3.11704131e-01]\n", + "INFO: Inertia: \n", + "[[ 6.93201323e+04 -3.80956656e-04 1.17729353e+04]\n", + " [-3.80956654e-04 1.40925492e+05 -1.74794555e-04]\n", + " [ 1.17729353e+04 -1.74794555e-04 1.97104534e+05]]\n", + "INFO: Prepare mass matrices for independent and free DoFs (f-set)\n", + "INFO: Modal analysis for first 76 modes...\n", + "INFO: Found 76 modes with the following frequencies [Hz]:\n", + "INFO: [ nan nan 1.05457802e-06 4.52538930e-06\n", + " 1.45457362e-05 3.39333114e-05 3.13716140e+00 4.68251648e+00\n", + " 7.20798842e+00 7.88159198e+00 8.33703334e+00 8.49130434e+00\n", + " 9.88499185e+00 1.25695152e+01 1.53520000e+01 1.70224904e+01\n", + " 1.71353125e+01 1.84415883e+01 2.53323405e+01 2.53529787e+01\n", + " 2.68433851e+01 2.81886245e+01 3.20724571e+01 3.24562324e+01\n", + " 3.51081211e+01 3.52877859e+01 3.71484020e+01 3.74387461e+01\n", + " 4.65087795e+01 4.66555395e+01 5.19704569e+01 5.31897897e+01\n", + " 5.59827047e+01 5.61588818e+01 5.79161872e+01 5.83859187e+01\n", + " 6.49254027e+01 6.49636994e+01 6.52953993e+01 6.55893563e+01\n", + " 6.76977070e+01 7.39272939e+01 7.40020584e+01 7.92493678e+01\n", + " 7.98448917e+01 8.44686963e+01 8.44977700e+01 8.89696420e+01\n", + " 8.90380160e+01 9.17266784e+01 9.27516791e+01 1.00718906e+02\n", + " 1.00742526e+02 1.05800840e+02 1.06520421e+02 1.09898735e+02\n", + " 1.10071029e+02 1.12603325e+02 1.12677121e+02 1.21628942e+02\n", + " 1.21633308e+02 1.25797288e+02 1.25826624e+02 1.30369009e+02\n", + " 1.30516978e+02 1.37566261e+02 1.37608584e+02 1.41676350e+02\n", + " 1.44303157e+02 1.44811563e+02 1.47038007e+02 1.47151873e+02\n", + " 1.55547154e+02 1.56081100e+02 1.56183006e+02 1.57181628e+02]\n", + "INFO: From these 76 modes, the following 70 modes are selected: [ 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n", + " 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54\n", + " 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76]\n", + "INFO: Working on f-set\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Working on h-set\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Damping: modal damping of 0.02\n", + "INFO: Splining (rigid body) of 1668 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: Splining (rigid body) of 6 DOFs to 6 DOFs...\n", + "INFO: --> Saving model data.\n", + "INFO: --> Done in 0:00:03 [h:mm:ss].\n", + "INFO: --> Starting Main in sequential mode for 3 trimcase(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.level\n", + "INFO: subcase: 1\n", + "INFO: (case 1 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pushdown\n", + "INFO: subcase: 2\n", + "INFO: (case 2 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: \n", + "INFO: ========================================\n", + "INFO: trimcase: CC.M3.OVCFL000.pullup\n", + "INFO: subcase: 3\n", + "INFO: (case 3 of 3)\n", + "INFO: ========================================\n", + "INFO: Setting trim conditions to \"default\"\n", + "INFO: Init model equations of type \"Steady\"\n", + "INFO: Init EFCS \"efcs_dc3\"\n", + "INFO: Running trim for 76 variables...\n", + "INFO: The solution converged.\n", + "INFO: calculating forces & moments on structural set (force summation method)...\n", + "INFO: apply euler angles...\n", + "INFO: calculating cutting forces & moments...\n", + "INFO: gathering information on monitoring stations from response(s)...\n", + "INFO: searching min/max in time data at 32 monitoring stations and gathering loads (dyn2stat)...\n", + "INFO: --> Saving response(s).\n", + "INFO: --> Saving monstation(s).\n", + "INFO: --> Saving dyn2stat.\n", + "INFO: --> Done in 0:00:02 [h:mm:ss].\n", + "INFO: --> Opening response(s).\n", + "INFO: --> Loading monstations(s).\n", + "INFO: --> Loading dyn2stat.\n", + "INFO: --> Drawing some standard plots.\n", + "INFO: start potato-plotting...\n", + "INFO: start plotting cutting forces along wing...\n", + "INFO: plots saved as ./DC3_results/monstations_jcl_dc3_trim.pdf\n", + "INFO: --> Saving auxiliary output data.\n", + "INFO: writing trim results to: ./DC3_results/trim_results_jcl_dc3_trim.csv\n", + "INFO: writing successful trimcases cases to: ./DC3_results/successful_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing failed trimcases cases to: ./DC3_results/failed_trimcases_jcl_dc3_trim.csv\n", + "INFO: writing critical trimcases cases to: ./DC3_results/crit_trimcases_jcl_dc3_trim.csv\n", + "INFO: saving critical nodal loads as Nastarn cards...\n", + "INFO: Loads Kernel finished.\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: ( )\n", + "INFO: ( )\n", + "INFO: \n", + "INFO: _|_\n", + "INFO: ---------O---------\n", + "INFO: \n", + "INFO: \n" + ] + } + ], + "source": [ + "from loadskernel import program_flow\n", + "\n", + "# Here you launch the Loads Kernel with your job\n", + "k = program_flow.Kernel('jcl_dc3_trim', pre=True, main=True, post=True, test=False,\n", + " path_input='./DC3_model/JCLs',\n", + " path_output='./DC3_results')\n", + "k.run()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ac84c1cf-c381-47cf-8295-3400add7623f", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "In this section, the results of Loads Kernel are presented, for both the rigid and flexible versions. Moreover, these results are compared to the MSC Nastran results, obtained with SOL144. The purpose of this comparison is to prove the similarities of the outcome of both softwares.\n", + "\n", + "One of the most interesting results from this simulation is the 'trim_results_jcl_dc3_trim.csv' file, where all the trim conditions for the different trim cases are discriminated. Upon examining the trim results, several notable conclusions emerge. the next table presents the αtrim outcomes for the final modified model, revealing values that comfortably align within the acceptable range for both the rigid and flexible versions across both software programs. Comparing the results between both software it can be seen that they match quiet well, having just very small differences. To understand the sources of these small differences is important to discuss the differences between both software. While the simulation performed in Loads Kernel is a modal analyses, the SOL144 performed in MSC Nastran it is not. Hence, for the Loads Kernel case a convergence study had to be performed. For that, the number of eigenmodes truncated were progressively increased until the trim results converged. At the end, it was concluded that a total number of 71 eigenmodes is enough to obtain convergence. On top of that, MSC Nastran considers the elastic deformation on the wing root to be null, while Loads Kernel allows the wing root to have elastic deformation. This makes the comparison not so direct in the flexible case. For instance, in order to compare the αtrim flexible results, in the Loads Kernel results for αtrim the ”elastic incidence angle” (basically, the wing root elastic twist deformation) was subtracted. Comparative analysis between the rigid and flexible aircraft configurations reveals evident disparities. Notably, when comparing the Loads Kernel αtrim results for horizontal level flight, a significant 26.6% difference emerges, underscoring the pronounced impact of aeroelasticity in aircraft design. This underscores the imperative of considering aeroelasticity into flight performance, stability, and control analyses.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Trim cases αtrim results for the final modified model, for M3.*\n", + "\n", + "Further elucidating the significance of aeroelasticity, the next table shows the CLf/CL (the percentage of the contribution of flexibility on the lift force) results obtained with Loads Kernel, highlighting the contribution of aircraft flexibility. For instance, in horizontal level flight, flexibility contributes a detrimental -5.22%. This negative contribution is explained analysing the elastic deformations of the main wing at these trim cases, as is going to be shown further on.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Trim cases αtrim results for the final modified model, for M3.*\n", + "\n", + "Additional information about these results can be consulted in {cite}`Carvalho2024`. To understand the influence of elasticity on aircraft performance, stability, and control, the main wing’s elastic deformations along the span is illustrated in the following figure across the different trim cases are analyzed.\n", + "\n", + "\"DC3\"\n", + "\n", + "*Loads Kernel elastic deformation of the main wing at certain speed, M3 for the final modified aeroelastic model.*\n", + "\n", + "Elastic vertical displacement Uflex,Z along the wing span reveals upward bending during horizontal level flight and pull-up maneuvers, and downward bending during push-down maneuvers. This alignment is expected, as lift force in the former cases lifts the wing, while in the latter, it pushes it downward.\n", + "\n", + "Notably, during pull-up maneuvers, the most pronounced vertical displacements occur, particularly at the wing tip, reaching Uflex,Z=1.55 m, equivalent to 10.7% of the half wing span. This displacement remains within the accepted range for linear elasticity assumptions applied in these analyses, typically valid until 12-15% of the tip’s vertical displacement relative to half the wing span.\n", + "\n", + "Evaluating elastic twist Uflex,ry along the wing span, a nose-down torsional twist is observed during horizontal level flight and pull-up maneuvers in the outer wing section, indicating negative twist values. At the inner section a nose-up torsional twist is observed, but with very small values compared with the outer section. On the opposite, during push-down maneuvers, the opposite trend is evident. Notably, the most significant elastic twist occurs at the wing tip during pull-up maneuvers, with a value of Uflex,ry=−1.98 degrees. This substantial twist significantly influences lift distribution along the wing, hence affecting overall wing performance. To obtain this elastic deformation plots the file 'plot_wing_defo_trim-cases.py' can be found in the 'scripts' folder and it is presented below.\n", + "\n", + "For additional information about the meanuver load results please refer to {cite}`Carvalho2024`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8c7fbb9c-47f5-4316-9d06-e19bfb5af671", + "metadata": { + "tags": [ + "skip-execution" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import h5py\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pathlib\n", + "import os\n", + "import IPython\n", + "\n", + "notebook_path = IPython.get_ipython().run_line_magic('pwd','')\n", + "notebook_dir = pathlib.Path(notebook_path).resolve()\n", + "filename = path = os.path.join(notebook_dir, 'DC3_results', 'response_jcl_dc3_trim.hdf5')\n", + "\n", + "# open HDF5 file\n", + "responses = h5py.File(filename, 'r')\n", + "\n", + "# get response of first trim case\n", + "response = responses['0']\n", + "response2 = responses['1']\n", + "response3 = responses['2']\n", + "Ug_f = response['Ug_f'][:]\n", + "Ug_f2 = response2['Ug_f'][:]\n", + "Ug_f3 = response3['Ug_f'][:]\n", + "\n", + "# extract ID, offset and set matrices\n", + "filename = path = os.path.join(notebook_dir, 'DC3_results', 'model_jcl_dc3_trim.hdf5')\n", + "model = h5py.File(filename, 'r')\n", + "strcgrid = model['strcgrid']\n", + "ID = strcgrid['ID'][:]\n", + "set = strcgrid['set'][:]\n", + "offset = strcgrid['offset'][:]\n", + "\n", + "# IDs of the wing's LRA\n", + "ID_wings = np.array([54090001,54090002,54090003,54090004,54090005,54090006,54090007,54090008,54090009,54090010,54090011,54090012,54090013,54090014,54090015,54090016,54090017,54090018,54090019,54090020,54090021,54090022,54090023,54090024,54090025,54090026,54090027,54090028,54090029,54090030,54090031,64090001,64090002,64090003,64090004,64090005,64090006,64090007,64090008,64090009,64090010,64090011,64090012,64090013,64090014,64090015,64090016,64090017,64090018,64090019,64090020,64090021,64090022,64090023,64090024,64090025,64090026,64090027,64090028,64090029,64090030,64090031])\n", + "\n", + "y = []\n", + "dz_pos = []\n", + "ry_pos = []\n", + "for i in range(len(ID_wings)):\n", + " x = np.where(strcgrid['ID'][:]==ID_wings[i])\n", + " dz_pos.append(strcgrid['set'][x[0], 2])\n", + " ry_pos.append(strcgrid['set'][x[0], 4])\n", + " y.append(strcgrid['offset'][x[0], 1][0])\n", + "\n", + "dz= []\n", + "ry = []\n", + "for i in range(len(dz_pos)):\n", + " dz.append(Ug_f[0, dz_pos[i]])\n", + " ry.append(Ug_f[0, ry_pos[i]])\n", + "\n", + "dz2= []\n", + "ry2 = []\n", + "for i in range(len(dz_pos)):\n", + " dz2.append(Ug_f2[0, dz_pos[i]])\n", + " ry2.append(Ug_f2[0, ry_pos[i]])\n", + "\n", + "dz3= []\n", + "ry3 = []\n", + "for i in range(len(dz_pos)):\n", + " dz3.append(Ug_f3[0, dz_pos[i]])\n", + " ry3.append(Ug_f3[0, ry_pos[i]])\n", + "\n", + "y_sorted_indices = np.argsort(y)\n", + "y_sorted = np.sort(y)\n", + "dz_sorted = [dz[i] for i in y_sorted_indices]\n", + "dz_sorted_sym = [-dz_sorted[i] for i in range(len(dz_sorted))]\n", + "ry_sorted = [ry[i] for i in y_sorted_indices]\n", + "dz2_sorted = [dz2[i] for i in y_sorted_indices]\n", + "dz2_sorted_sym = [-dz2_sorted[i] for i in range(len(dz2_sorted))]\n", + "ry2_sorted = [ry2[i] for i in y_sorted_indices]\n", + "dz3_sorted = [dz3[i] for i in y_sorted_indices]\n", + "dz3_sorted_sym = [-dz3_sorted[i] for i in range(len(dz3_sorted))]\n", + "ry3_sorted = [ry3[i] for i in y_sorted_indices]\n", + "\n", + "ry_sorted = np.degrees(ry_sorted)\n", + "ry2_sorted = np.degrees(ry2_sorted)\n", + "ry3_sorted = np.degrees(ry3_sorted)\n", + "\n", + "# Plots\n", + "fig, axs = plt.subplots(2, 1, figsize=(12, 6))\n", + "axs[0].plot(y_sorted, dz_sorted_sym, marker='o', linestyle='-', color='blue', label='Horizontal Level Flight (n$_Z$=1)')\n", + "axs[0].plot(y_sorted, dz2_sorted_sym, marker='o', linestyle='-', color='red', label='Push Down Maneuver (n$_Z$=-1)')\n", + "axs[0].plot(y_sorted, dz3_sorted_sym, marker='o', linestyle='-', color='green', label='Pull Up Maneuver (n$_Z$=2.5)')\n", + "axs[0].set_xlabel('Wing span [m]', fontsize='x-large')\n", + "axs[0].set_ylabel('U$_{flex,z}$ [m]', fontsize='x-large')\n", + "axs[0].grid(True)\n", + "axs[0].legend( fontsize='x-large')\n", + "\n", + "axs[1].plot(y_sorted, ry_sorted, marker='o', linestyle='-', color='blue')\n", + "axs[1].plot(y_sorted, ry2_sorted, marker='o', linestyle='-', color='red')\n", + "axs[1].plot(y_sorted, ry3_sorted, marker='o', linestyle='-', color='green')\n", + "axs[1].set_xlabel('Wing span [m]', fontsize='x-large')\n", + "axs[1].set_ylabel('U$_{flex,ry}$ [deg]', fontsize='x-large')\n", + "axs[1].grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}