Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
Abstract
Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and calculation on heterogeneous hardware, making their implementation for large-scale problems exceptionally challenging. We provide an effective and scalable solution to developing CFD+ML algorithms using open source software OpenFOAM and SmartSim. SmartSim provides an Orchestrator that significantly simplifies the programming of CFD+ML algorithms and a Redis database that ensures highly scalable data exchange between ML and CFD clients. We show how to leverage SmartSim to effectively couple different segments of OpenFOAM with ML, including pre/post-processing applications, solvers, function objects, and mesh motion solvers. We additionally provide an OpenFOAM sub-module with examples that can be used as starting points for real-world applications in CFD+ML.
Cite
@article{arxiv.2402.16196,
title = {Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim},
author = {Tomislav Maric and Mohammed Elwardi Fadeli and Alessandro Rigazzi and Andrew Shao and Andre Weiner},
journal= {arXiv preprint arXiv:2402.16196},
year = {2024}
}