This study addresses the computational challenges in fluid flow simulations arising from demanding computational grids, required to capture the temporal and length scales involved. Our approach focuses on the pressure solver, as this is a resource-intensive component in Computational Fluid Dynamics (CFD) solvers. We achieve this by integrating a Machine Learning (ML) surrogate model with an incompressible fluid flow solver. We created two variants of an ML-enhanced CFD solver which were able to reduce the number of iterations required by the CFD pressure solver during unsteady flow simulations. Consequently, the simulations yielded comparable drag coefficients and Strouhal numbers, accompanied by an eightfold decrease in execution time. The performance enhancements are attributed to reduced computational effort per temporal iteration and early-stage forcing on the simulation dynamical behavior when using the ML-based surrogate models. This research introduces an approach to enhance the computational efficiency of fluid flow analyses by incorporating surrogate models to aid the pressure solver in CFD simulations. We propose a Hybrid CFD solver, ie. a physics-informed solver enhanced with data-driven surrogate models.

中文翻译：

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利用机器学习技术增强 CFD 求解器


这项研究解决了流体流动模拟中因要求计算网格而产生的计算挑战，需要捕获所涉及的时间和长度尺度。我们的方法侧重于压力求解器，因为这是计算流体动力学 (CFD) 求解器中的资源密集型组件。我们通过将机器学习 (ML) 代理模型与不可压缩流体流动求解器集成来实现这一目标。我们创建了 ML 增强型 CFD 求解器的两个变体，它们能够减少非定常流动模拟期间 CFD 压力求解器所需的迭代次数。因此，模拟产生了相当的阻力系数和斯特劳哈尔数，同时执行时间减少了八倍。性能的增强归因于每次时间迭代的计算量的减少以及使用基于机器学习的替代模型时对模拟动态行为的早期强制。本研究介绍了一种通过结合代理模型来帮助 CFD 模拟中的压力求解器来提高流体流动分析的计算效率的方法。我们提出了一种混合 CFD 求解器，即。通过数据驱动的代理模型增强的物理信息求解器。