Large eddy simulations of wall jets with coflow for the study of turbulent Prandtl number variations and data-driven modeling

Abstract

There is a continuing effort by turbulence researchers to provide improved turbulent heat flux predictions for Reynolds-averaged Navier-Stokes (RANS) calculations of heat transfer applications. In this paper, data-driven models are developed for the turbulent heat flux prediction in wall jets with coflow using a gene expression programming (GEP)–based machine-learning technique. The training data used as input to the optimization algorithm are obtained by performing highly resolved large eddy simulations (LES) of nine cases covering various flow and geometry conditions. The study examines whether predictive RANS-based heat transfer closures can be trained that are robust to these physically ..