Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim

• URL: http://arxiv.org/abs/2402.16196v2
• Date: Tue, 23 Apr 2024 18:22:08 GMT
• Title: Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
• Authors: Tomislav Maric, Mohammed Elwardi Fadeli, Alessandro Rigazzi, Andrew Shao, Andre Weiner,
• Abstract summary: We provide an effective and scalable solution to developing CFD+ML algorithms using OpenFOAM and SmartSim. SmartSim provides an Orchestrator that significantly simplifies the programming of CFD+ML algorithms and a Redis database. 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.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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