基于OpenFOAM与SmartSim的机器学习与计算流体动力学结合方法
Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
AI总结：
本研究针对CFD+ML算法在大规模问题中实现困难的痛点，基于开源软件OpenFOAM与SmartSim提出高效可扩展的耦合方案，简化编程并保障数据交换，覆盖多模块耦合，还提供带示例的子模块供实际应用开发参考。
摘要：
机器学习（ML）与计算流体动力学（CFD）的结合为改进技术系统与自然系统的模拟提供了诸多可能。然而，CFD+ML算法需要在异构硬件上进行数据交换、同步与计算，这使得其在大规模问题中的实现极具挑战性。\n我们利用开源软件OpenFOAM与SmartSim，为CFD+ML算法的开发提供了一种高效且可扩展的解决方案。SmartSim提供的编排器（Orchestrator）大幅简化了CFD+ML算法的编程，其内置的Redis数据库则确保了ML与CFD客户端之间高度可扩展的数据交换。我们展示了如何利用SmartSim有效耦合OpenFOAM的不同模块与ML，包括前/后处理应用、求解器、函数对象以及网格运动求解器。此外，我们还提供了一个附带示例的OpenFOAM子模块，可作为CFD+ML领域实际应用的开发起点。
英文摘要：
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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