OpenFOAM: A C++ Library for Complex Physics Simulations

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POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations (2016, methodology): “present in the extended version of OpenFOAM [64], some utilities have been updated or created in order to implement the offline phase of POD-FV-ROM in the library.”
Physics-informed neural networks (PINNs) for fluid mechanics: a review (2021, methodology): “Finally, computer programs such as OpenFOAM [6] have more than 100,000 lines of code, making it almost impossible to maintain and update them from one generation to the next.”
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019, methodology): “To validate the prediction performance of the trained DNN surrogates, corresponding CFD simulations are also conducted using an open-source FV-based CFD solver, OpenFOAM [69].”
Geometry-Informed Neural Operator for Large-Scale 3D PDEs (2023, methodology): “We generate our own large-scale industry-standard Ahmed’s body geometries using GPU-based OpenFOAM [15], composed of 500+ car geometries with O (10 5 ) mesh points on the surface and O (10 7 ) mesh points in space.”
Partitioned solver for strongly coupled fluid–structure interaction (2013, methodology): “Several solvers for the mesh motion can be found in the open literature [33,39,41,44,45] such as Laplace smoothing [47], solid body rotation stress (SBR stress) [48], pseudo-solid equations [49] and radial basis function [33,50,51].”