RANS predictions of trailing-edge slot flows using heat-flux closures developed with CFD-driven machine learning

Abstract

Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, gene-expression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent h..