TY - JOUR
T1 - Data-driven RANS closures for three-dimensional flows around bluff bodies
AU - Huijing, Jasper P.
AU - Dwight, Richard P.
AU - Schmelzer, Martin
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Data-driven modelling
KW - Incompressible flow
KW - Machine learning
KW - Reynolds averaged Navier-Stokes
KW - Sparse symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=85105574407&partnerID=8YFLogxK
U2 - 10.1016/j.compfluid.2021.104997
DO - 10.1016/j.compfluid.2021.104997
M3 - Article
AN - SCOPUS:85105574407
SN - 0045-7930
VL - 225
JO - Computers and Fluids
JF - Computers and Fluids
M1 - 104997
ER -