INTEGRATION OF MACHINE LEARNING AND COMPUTATIONAL FLUID DYNAMICS TO DEVELOP TURBULENCE MODELS FOR IMPROVED TURBINE WAKE MIXING PREDICTION

Abstract

This paper presents development of accurate turbulence closures for wake mixing prediction by integrating a machine-learning approach with Reynolds Averaged Navier-Stokes (RANS)-based computational fluid dynamics (CFD). The data-driven modelling framework is based on the gene expression programming (GEP) approach previously shown to generate non-linear RANS models with good accuracy. To further improve the performance and robustness of the data-driven closures, here we exploit that GEP produces tangible models to integrate RANS in the closure development process. Specifically, rather than using as cost function a comparison of the GEP-based closure terms with a frozen high fidelity dataset, ..