用机器学习预测流体压力场，加速计算流体力学求解。
Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations
- 用神经网络学习局部流场特征，预测压力变化。
- 压力初值精度提升94%，求解速度平均快3倍。
- 适合需长期高精度模拟的工程流体问题。
数据驱动方法在加速计算流体力学（CFD）求解方面展现出巨大潜力。然而，纯机器学习代理模型在保证物理一致性及扩展至真实场景方面存在挑战。本文提出一种通用且可扩展的混合方法，结合CFD与机器学习，加速长时间不可压缩流体流动模拟，同时保持高精度。利用多种二维瞬态浮力羽流的模拟数据离线训练神经网络，目标是通过局部特征预测压力场的时间演变。由于采用单元级预测，该方法无需重新训练即可应用于不同几何结构。将预测的压力估计值作为初始值，用于加速压力-速度耦合过程。结果表明，求解泊松方程的初始猜测平均改进94%；第一轮压力校正加速达均值3倍，具体取决于所用迭代求解器。研究表明，细胞级别的机器学习估计可在不牺牲精度的前提下提升CFD迭代线性求解器效率。尽管该方法在更复杂场景中的可扩展性尚待验证，但本研究凸显了领域特定混合求解器在CFD中的前景价值。
原文摘要 · Abstract (English)
Data-driven methods demonstrate considerable potential for accelerating the inherently expensive computational fluid dynamics (CFD) solvers. Nevertheless, pure machine-learning surrogate models face challenges in ensuring physical consistency and scaling up to address real-world problems. This study presents a versatile and scalable hybrid methodology, combining CFD and machine learning, to accelerate long-term incompressible fluid flow simulations without compromising accuracy. A neural network was trained offline using simulated data of various two-dimensional transient buoyant plume flows. The objective was to leverage local features to predict the temporal changes in the pressure field in comparable scenarios. Due to cell-level predictions, the methodology was successfully applied to diverse geometries without additional training. Pressure estimates were employed as initial values to accelerate the pressure-velocity coupling procedure. The results demonstrated an average improvement of 94% in the initial guess for solving the Poisson equation. The first pressure corrector acceleration reached a mean factor of 3, depending on the iterative solver employed. Our work reveals that machine learning estimates at the cell level can enhance the efficiency of CFD iterative linear solvers while maintaining accuracy. Although the scalability of the methodology to more complex cases has yet to be demonstrated, this study underscores the prospective value of domain-specific hybrid solvers for CFD.
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