Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications. However, the practical use is often limited by the high computational cost, and the accurate resolution of near-wall flow is a significant contributor to this cost. Machine learning (ML) and other data-driven methods can complement existing wall models. Nevertheless, training these models is bottlenecked by the large computational effort and memory footprint demanded by back-propagation. Recent work has presented alternatives for computing gradients of neural networks where a separate forward and backward sweep is not needed and storage of intermediate results between sweeps is not required because an unbiased estimator for the gradient is computed in a single forward sweep. In this paper, we discuss the application of this approach for training a subgrid wall model that could potentially be used as a surrogate in wall-bounded flow CFD simulations to reduce the computational overhead while preserving predictive accuracy.

翻译：计算流体动力学（CFD）被广泛应用于燃气轮机及众多工业/科学领域的设计与优化中。然而，高昂的计算成本往往限制了其实际应用，其中近壁面流动的精确求解是造成该成本的主要因素之一。机器学习（ML）及其他数据驱动方法可对现有壁面模型进行补充，但此类模型的训练受制于反向传播所需的大量计算资源与内存占用。近期研究提出了替代方案——在无需独立前向/反向传播过程、无需存储层间中间结果的情况下，通过单次前向传播计算梯度的无偏估计量。本文探讨了该方法在训练亚网格壁面模型中的应用潜力，该模型可作为壁面约束流CFD仿真中的替代模型，在保持预测精度的同时降低计算开销。