Gene Expression Programming for Differential Reynolds Stress Models

Kurzfassung

Gene Expression Programming is an evolutionary algorithm that has been successfully applied to Explicit Algebraic Reynolds Stress Models as a part of the fast-growing field of Datadriven turbulence modeling. However, its performance for Differential Reynolds Stress Models has not yet been assessed. In this paper, two different approaches of Gene Expression Programming - frozen and data-driven - are investigated for the Speziale Sarkar-Gatski (SSG) Model and applied to the curved-backward facing step. The aim is to improve the SSG Model prediction of the Turbulent Boundary Layer subjected to an Adverse Pressure Gradient.