ALEX17 Diurnal Cycles Benchmark: A Large Domain in Complex Terrain

Javier Sanz Rodrigo, Roberto Chávez, Pedro Alvim De Azevedo Santos

Background

This repository holds model evaluation scripts for the ALEX17 Diurnal Cycles benchmark to test mesoscale-to-microscale flow models in the assessment of wind conditions during several diurnal cycles happening during the NEWA ALEX17 experiment.

The project is integrated in the Phase 3 of the IEA Task 31 Wakebench international framework for wind farm modeling and evaluation.

Scope and Objectives

This benchmark is intended for flow models that can reproduce wind conditions at microscale, relevant for wind turbine siting and energy yield assessment, with realistic large-scale forcing characterized by mesoscale modelling. Therefore, the benchmark is designed with transient meso-to-micro modelling in mind. Nevertheless, steady-state modellers are also welcomed to participate and identify situations where the flow is quasi-steady at different levels of thermal stratification.

The ALEX17 intensive campaign, where all the instruments were operational, lasted 4 months. During this period numerous interesting phenomena were captured like gravity waves, hydraulic jumps, etc., but we would like to focus first on testing models in a simpler environment to make sure models can deal with more fundamental challenges, such as:

• How to set up an efficient meshing strategy for a large microscale domain while capturing all relevant topographic features;
• How to deal with surface boundary conditions in the presence of thermal heating/cooling, roughness changes and heterogeneous forest canopies;
• How to parameterize turbulence models;
• How to couple mesoscale and microscale.

Benchmark Guides

The following blog posts were used to guide benchmark participants:

• Benchmark guide
• ALEX17 background

Data

Benchmark input data and simulation data is published open-access in the following data repository: [zenodo dataset]

Installation

We use Jupyter notebooks based on Python 3. We recomend the Anaconda distribution to install python. You can find a list of library dependencies in requirements.txt to build your local environment.

Output data converter

A script is provided to make facilitate the output data conversion process and comply with the requested data formatting and variable name conventions. The standard way of using the scripts consists of the following steps:

1. Update the config/Marinet.yaml file.

2. Edit the runALEX17.py file, making sure to provide it with functions that can extract your data for a given point and column.

   wrf_inp = lib.WrfReader.WrfReader(variables_to_write)
   f_get_column = wrf_inp.get_column  # (lat, lon) -> (time, height, variables)
   f_get_point = wrf_inp.get_point  # (lat, lon, height) -> (time, variables)

   The expected output from the functions are labeled, xarray tables. An example of how to define those functions can be found here get_point and get_column. If you don't feel confortable with xarrays, you can try hacking the script and copy the numbers directly to the generated output files file1, file2, file3. This approach is not advised as it will be prone to errors and most likelly it will be more time consuming than understanding the suggested approach).

3. Finally, edit your [simID] representing your simulation identifier (alex17_*, where * is a number of your choice).

Citation

You can cite the github repo in the following way:

[zenodo github release]

License

Licensed under the GNU General Public License v3.0

Acknowledgements

The authors would like to thank the benchmark participants for their simulations and in-kind support in fine-tuning the benchmark set-up and evaluation methodology. The benchmark is run under the umbrella of IEA-Wind Task 31 with support from the H2020-MARINET2 project, where it was used as a pilot of the virtual research environment.