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 on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline k−ω SST model in predictions of mean-velocity in complete flow domain.
- Data-driven modelling
- Incompressible flow
- Machine learning
- Reynolds averaged Navier-Stokes
- Sparse symbolic regression