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

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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.