Turbulent Closure for Sediment Transport Using Symbolic Regression Based on DNS Data

  • Stöcker, Yvonne (TU Dresden)
  • Golla, Christian (TU Dresden)
  • Jain, Ramandeep (TU Dresden)
  • Fröhlich, Jochen (TU Dresden)
  • Cinnella, Paola (Sorbonne Université)

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This work aims to improve the turbulence modelling in RANS simulations for particle-laden flows. Using DNS data as reference, the error of the model assumptions for the turbulence transport equation is extracted and serves as target data for a machine learning process called SpaRTA. The resulting corrective algebraic expressions are implemented in the RANS solver SedFoam-2.0 for cross-validation.