A Parallel, High-Fidelity Coupled Machine Learning/ CFD Solver Method for Solving Exascale CFD Problems with Turbulence Damping at the Liquid-Gas Inte

模拟气液界面湍流阻尼的性质在准确预测部件功率损失、润滑剂分布和传热方面起着至关重要的作用。目前，有一个有限的了解，这种界面湍流阻尼，以及如何最好地代表它在计算模型。在EPSRC Cornerstone项目期间，展示了一种概念验证方法，该方法使用高保真计算流体动力学（CFD）来模拟平面上矩形通道中的气液流动。这项研究旨在将这一概念扩展到发动机代表性的几何形状和条件，以及其他多相流的情况。这些高保真度，exascale测试用例将使用上述CFD概念验证方法进行模拟，适用于exascale用例，使用高性能计算机（HPC）上的并行域特定语言OPS。这些测试用例形成了一个模拟数据库，从中可以训练机器学习模型。将使用耦合机器学习/CFD方法，并分析其与传统计算模型相比的效率，可扩展性和准确性。

Modelling the nature of gas-liquid interface turbulence damping plays a crucial role in making accurate predictions of component power losses, lubricant distribution and heat transfer. Currently, there is a limited understanding of this interface turbulence damping and how to best represent it in computational models. During the EPSRC Cornerstone project, a proof-of-concept approach was demonstrated that uses high-fidelity computational fluid dynamics (CFD) to simulate air-liquid flow in a rectangular channel on a flat surface. This study seeks to expand this concept to engine-representative geometries and conditions, as well as other multi-phase flow cases. These high-fidelity, exascale test cases will be simulated using the aforementioned CFD proof-of-concept approach, adapted for exascale cases using the parallel domain specific language OPS on high performance computers (HPCs). These test cases form a database of simulations, from which a machine learning model can be trained. A coupled machine learning/CFD approach will be used and analysed for its efficiency, scalability, and accuracy compared to traditional computational models.