Update paper.md

doc/paper.md

@@ -45,20 +45,20 @@ The first category contains models which seek to resolve all relevant physics wi
 
 - FLOW-3D [@FLOW-3D] is a commercial CFD software known for its capabilities in simulating complex free-surface problems, and has several specialized models for AM. However, the Flow-3D is proprietary, meaning its source code is not available for public inspection or modification and users must purchase a license to use the software. 
 
-The second category contains models that simplify melt pool physics to rapidly simulate heat transport, residual stress, and distortion across an entire part. Commercial finite element thermomechanics software solutions built on Abaqus [@abaqus] and Ansys [@ansys] are available; however, these software packages are not open and free to use and develop upon. Adamantine [@adamantine] is a thermomechanics simulation tool built on an open-source software stack designed for high performance computing across computing architectures, including the deal.II finite element software package [@deal.ii], providing an alternative to the proprietary software solutions.
+The second category contains models that simplify melt pool physics to rapidly simulate heat transport, residual stress, and distortion across an entire part. Commercial finite element thermomechanics software solutions built on Abaqus [@abaqus] and Ansys [@ansys] are available; however, these software packages are not open and free to use and develop upon. Alternatively, Adamantine [@adamantine] is a thermomechanics simulation tool built on an open-source software stack designed for high-performance computing across various architectures, including the deal.II finite element software package [@deal.ii], providing an alternative to proprietary software solutions.
 
-There is an established need for intermediate frameworks that are capable of accurately predicting melt pool shape, thermal gradients, and other meso-scale quantities across an entire part. Such models can be used to inform process design decisions through heuristic estimations of anomalous printing features (e.g., keyhole formation and lack-of-fusion), as well as the prediction of the final microstructure and material properties of AM components. AdditiveFOAM was created to address these challenges and is written in an extensible manner that is capable of considering a large number of physical representations of the target problem. This includes volumetric source terms in the energy equation that support any number of independently moving sources with distinct energy profiles, optional Marangoni-driven fluid flow, and a generalized scheme for implementing tabulated thermodynamic pathways for metal alloys.
+There is an established need for intermediate frameworks that accurately predict melt pool shape, thermal gradients, and other meso-scale quantities across an entire part. These models can inform process design decisions through heuristic estimations of anomalous printing features (e.g., keyhole formation and lack-of-fusion) and predict the final microstructure and material properties of AM components. AdditiveFOAM addresses these challenges, featuring a volumetric source term in the energy equation for multiple heat sources, optional Marangoni-driven fluid flow, and a scheme for implementing tabulated thermodynamic pathways for metal alloys.
 
 # Software Features
-AdditiveFOAM features a volumetric heat source library that supports any number of independently moving sources with distinct energy profiles that have been validated for a number of AM processes. The volumetric source term is used in the solution of the unsteady, nonlinear heat equation which considers three distinct phases (solid, liquid, and powder) as a continuum. A novel thermodynamic algorithm enables variable-order time integration for any thermodynamic pathway supported through tabulated solid fraction-temperature lookup tables. The available time integration schemes are explicit forward Euler, implicit backward Euler, Backward differentiation formula (BDF-2), and Crank-Nicolson. An adaptive time integration and time stepping method automatically switches between implicit and explicit schemes to balances the computational cost and solution accuracy depending on the problem state and user defined tolerances. Additionally, AdditiveFOAM features boundary conditions for Marangoni driven fluid flow in the melt pool as well as convective and radiative heat transfer. Finally, AdditiveFOAM leverages existing OpenFOAM libraries to sample thermal data needed for microstructure predictions with a specific library for coupling to the grain structure prediction software ExaCA [@exaca]. Future releases of AdditiveFOAM will focus on resource-optimized adaptive mesh refinement (AMR) as well as dynamic load-balancing using the Zoltan [@zoltan] library.
+The main feature of AdditiveFOAM is a transient, multiphysics application which solves conservation equations for mass, momentum, and energy during AM processing, built upon on the OpenFOAM finite volume software package [@openfoam]. This solver includes a novel thermodynamic algorithm enabling variable-order time integration for tabulated thermodynamic pathways. The available time integration schemes are explicit forward Euler, implicit backward Euler, backward differentiation formula (BDF-2), and Crank-Nicolson. An adaptive time integration and time-stepping method automatically switches between implicit and explicit schemes to balance the computational cost and solution accuracy depending on the problem state and user-defined tolerances. Additionally, AdditiveFOAM features boundary conditions for Marangoni-driven fluid flow in the melt pool as well as convective and radiative heat transfer. Another feature of AdditiveFOAM is the volumetric heat source library that supports any number of independently moving sources with distinct energy profiles that have been validated for several AM processes including laser powder bed fusion (LPBF), directed energy deposition (DED), and electron beam melting (EBM). Finally, AdditiveFOAM leverages existing OpenFOAM libraries to sample thermal data needed for microstructure predictions with a specific library for coupling to the grain structure prediction software ExaCA [@exaca]. Future releases of AdditiveFOAM will focus on resource-optimized adaptive mesh refinement (AMR), dynamic load-balancing using the Zoltan [@zoltan] library, and the integration of next-generation laser profiles (e.g., nLight AFX series) into the volumetric heat source library.
 
 AdditiveFOAM was designed to be used openly by researchers, industry, and academics. It has already been used in a number of scientific publications primarily focused on studying how AM process planning influences the development of local microstructural features in a part. Select publications that demonstrates AdditiveFOAM’s usage towards understanding the connection between process planning and microstructure evolution in metal AM include: 
 
 - Knapp et al. [@knapp] developed an automated framework for statistical calibration of the gaussian heat source parameters available in AdditiveFOAM to accurately predict the melt pool shape, solidification conditions, and solidification grain structure for single track welds on IN625 plate. 
 
-- Rolchigo and co-workers [@exaca; @texture-regimes] used AdditiveFOAM to generate thermal data for solidification grain structure predictions is laser powder bed fusion, validated against the NIST 2018 AM-Bench dataset [@amb2018]. 
+- Rolchigo et al. [@exaca; @texture-regimes] used AdditiveFOAM to generate thermal data for solidification grain structure predictions is laser powder bed fusion, validated against the NIST 2018 AM-Bench dataset [@amb2018]. 
 
-- Haines et al. [@haines-1] used AdditiveFOAM to confirm experimentally observed phase transformation pathways in 17–4 PH stainless steel during the laser powder bed fusion (L-PBF) process, and [@haines-2] 
+- Haines et al. used AdditiveFOAM to confirm experimentally observed phase transformation pathways in 17–4 PH stainless steel during the laser powder bed fusion (L-PBF) process [@haines-1], and investigated the role of scan strategy on recrystallization and grain growth in Fe-Si steel made by L-PBF with a pulsed laser [@haines-2].
 
 - Plotkowski et al. [@plotkowski] used AdditiveFOAM to confirm experimentally observed grain growth in Fe-Si steel during the Laser Engineered Net Shaping (LENS) process.