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

A neural-networks predictor library has been developed to deploy machine learning (ML) models into computational fluid dynamics (CFD) codes. The pointer-to-implementation strategy is adopted to isolate the implementation details in order to simplify the implementation to CFD solvers. The library provides simplified model-managing functions by encapsulating the TensorFlow C library, and it maintains self-belonging data containers to deal with data type casting and memory layouts in the input / output (I / O) functions interfacing with CFD solvers. On the language level, the library provides application programming interfaces (APIs) for C ++ and Fortran, the two commonly used programming languages in the CFD community. High-level customized modules are developed for two open-source CFD codes, OpenFOAM and CFL3D, written with C ++ and Fortran, respectively. The basic usage of the predictor is demonstrated in a simple data-driven heat transfer problem as the first tutorial case. Another tutorial case of modeling the e ff ect of turbulence in channel flow using the library is implemented in both OpenFOAM and CFL3D codes. The developed ML predictor library provides a powerful tool for the deployment of ML models in CFD solvers.