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.
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