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）广泛应用于燃气轮机及众多工业/科学应用的设计与优化中。然而，高昂的计算成本常限制其实用性，其中近壁流动的精确求解是成本的主要来源。机器学习及数据驱动方法可有效补充现有壁面模型，但反向传播所需的大计算量及内存开销成为训练此类模型的瓶颈。最新研究提出了神经网络的梯度计算替代方案：无需分离正向与反向传播过程，亦无需存储传播中间结果，即可通过单次正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——该模型可替代壁面约束流域CFD模拟中的全阶模型，在保持预测精度的同时降低计算开销。