Data-driven RANS closures for three-dimensional flows around bluff bodies

In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al.(2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based...

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine...