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

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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.

Original languageEnglish
Article number104997
Number of pages6
JournalComputers and Fluids
Publication statusPublished - 2021


  • Data-driven modelling
  • Incompressible flow
  • Machine learning
  • Reynolds averaged Navier-Stokes
  • Sparse symbolic regression


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