HIGHLIGHTS
- who: Mauricio E. Tano and Pablo Rubiolo from the Department of Nuclear Engineering, Texas AandM University, College Station, TX, USA have published the paper: Development of Explainable Data-Driven Turbulence Models with Application to Liquid Fuel Nuclear Reactors, in the Journal: Energies 2022, 15, 6861. of 20/09/2022
- what: The authors propose a novel method using a modified genetic algorithm to optimize the calculation of the Reynolds Shear Stress Tensor (RST) used for turbulence modeling. The authors demonstrate the applicability of this approach by developing high accuracy Reynolds-Averaged Navier-Stokes (RANS) models (averaged . . .
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