Improvement In Unsteady Wake Prediction Through Machine Learning Based Rans Model Training

Abstract

The inability of RANS to correctly capture profile wake dynamics and decay prevents the accurate prediction of unsteady losses and poses additional challenges to the aeromechanic verification of both turbines and compressors. This paper addresses this problem by introducing a novel technique applied to improve wake prediction through a RANS based calculation. In particular, for the first time, a data-driven approach is used to develop bespoke turbulence closures to be used in the context of unsteady RANS (URANS) calculations. The new closure is obtained from an evolutionary machine learning algorithm. The algorithm requires a high-fidelity dataset which, in this study, is provided through a ..