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A neural-networks predictor library has been developed to deploy machinelearning (ML) models into computational fluid dynamics (CFD) codes. Thepointer-to-implementation strategy is adopted to isolate the implementationdetails in order to simplify the implementation to CFD solvers. The libraryprovides simplified model-managing functions by encapsulating the TensorFlow Clibrary, and it maintains self-belonging data containers to deal with data typecasting and memory layouts in the input/output (I/O) functions interfacing withCFD solvers. On the language level, the library provides applicationprogramming interfaces (APIs) for C++ and Fortran, the two commonly usedprogramming languages in the CFD community. High-level customized modules aredeveloped for two open-source CFD codes, OpenFOAM and CFL3D, written with C++and Fortran, respectively. The basic usage of the predictor is demonstrated ina simple data-driven heat transfer problem as the first tutorial case. Anothertutorial case of modeling the effect of turbulence in channel flow using thelibrary is implemented in both OpenFOAM and CFL3D codes. The developed MLpredictor library provides a powerful tool for the deployment of ML models inCFD solvers.

NNPred: A Predictor Library to Deploy Neural Networks in Computational Fluid Dynamics software