| Autor: |
Maric, Tomislav, Fadeli, Mohammed Elwardi, Rigazzi, Alessandro, Shao, Andrew, Weiner, Andre |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Meccanica, 2024 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s11012-024-01797-z |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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