  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 effect 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.
