Deploying deep learning in OpenFOAM with TensorFlow

Abstract

We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is constructed with the TensorFlow C API and is integrated into OpenFOAM as an application that may be linked at run time. Notably, our formulation precludes any restrictions related to the type of neural network architecture (i.e., convolutional, fully-connected, etc.). This allows for potential studies of complicated neural architectures for practical CFD problems. In addition, the proposed module outlines a path towards an opensource, unified and transparent framework for computational fluid dynamics and machine learning. I. Nomenclature𝐶 𝑠 = Dynamic Smagorinsky coefficient ℎ = Backward facing step-height 𝜈 𝑡 = Turbulent eddy-viscosity 𝑅𝑒 𝜏 = Friction Reynolds number 𝑅 𝑖 𝑗 = Reynolds stress tensor * AIAA Member.