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

一种并行、高保真耦合机器学习/CFD 求解器方法，用于解决液气界面湍流阻尼的百亿亿次 CFD 问题

• 批准号: 2883937
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2883937
• 关键词: Parallel; Fidelity; Coupled; Machine; Learning

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.

对气体界面湍流阻尼的性质进行建模对于准确预测组件功率损失，润滑剂分布和热传递至关重要。当前，对这种接口湍流阻尼以及如何在计算模型中最好地表示它的理解有限。在EPSRC基石项目期间，证明了一种使用高保真计算流体动力学（CFD）模拟平坦表面上矩形通道中的空气流动的方法。这项研究旨在将此概念扩展到发动机代表性的几何和条件以及其他多相流量。这些高保真性的Exascale测试用例将使用上述CFD概念验证方法模拟，该方法使用高性能计算机（HPC）的平行域特定语言OPS适用于Exascale案例。这些测试用例构成了模拟数据库，可以从中训练机器学习模型。与传统的计算模型相比，将使用和分析机器学习/CFD方法的耦合和CFD方法。