Evolutionary Algorithm applied to Differential Reynolds Stress Model for Turbulent Boundary Layer subjected to an Adverse Pressure Gradient

Abstract

In this paper, an evolutionary algorithm is implemented for the purpose of performing symbolic regression in an attempt to improve Reynolds Averaged-Navier-Stokes models predictions. In contrast to most machine learning algorithms, Gene Expression Programming generates a mathematical expression that can be directly interpreted and implemented into the Computational Fluid Dynamics solver. In this paper, the latter feature is exploited based on high-fidelity data to ascertain novel correlations for the pressure-strain correlation within a particular Differential Reynolds Stress Model, the Speziale-Sarkar-Gatski (SSG) model. The CFD-driven Gene Expression Programming is considered for the curved backward-facing step. Two models are obtained regarding the industrially relevant phenomenon of a turbulent boundary layer under adverse pressure gradient. The models are tested on a range of validation cases.