TY - GEN
T1 - Machine Learning from LES Data to Improve Coarse Grid RANS Simulations
AU - Iskhakov, Arsen S.
AU - Grubbs, Taylor
AU - Dinh, Nam T.
AU - Leite, Victor Coppo
AU - Merzari, Elia
N1 - Publisher Copyright:
© 2023 Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Reynolds-averaged Navier-Stokes (RANS) simulations remain the workhorse for engineering computational fluid dynamics (CFD). However, they are still prohibitively expensive for system thermal hydraulic (TH) analysis. One of the ways to reduce the computational cost is to perform simulations on a coarse grid (CG). Unfortunately, this will introduce larger discretization errors in addition to the uncertainties of the turbulence models. Therefore, further advances are needed in CG RANS modeling techniques. In this work, two high-to-low data-driven (DD) approaches are investigated to reduce grid- and turbulence model-induced errors. The approaches are based on: (1) a turbulence model to predict eddy viscosity; (2) correction of errors in velocity. Neural networks (NNs) are trained using large eddy simulation (LES) data for upper plenum of a gas-cooled reactor facility. For approach (1) an inverse optimization problem is solved to extract the eddy viscosity from the LES data.
AB - Reynolds-averaged Navier-Stokes (RANS) simulations remain the workhorse for engineering computational fluid dynamics (CFD). However, they are still prohibitively expensive for system thermal hydraulic (TH) analysis. One of the ways to reduce the computational cost is to perform simulations on a coarse grid (CG). Unfortunately, this will introduce larger discretization errors in addition to the uncertainties of the turbulence models. Therefore, further advances are needed in CG RANS modeling techniques. In this work, two high-to-low data-driven (DD) approaches are investigated to reduce grid- and turbulence model-induced errors. The approaches are based on: (1) a turbulence model to predict eddy viscosity; (2) correction of errors in velocity. Neural networks (NNs) are trained using large eddy simulation (LES) data for upper plenum of a gas-cooled reactor facility. For approach (1) an inverse optimization problem is solved to extract the eddy viscosity from the LES data.
KW - Coarse grid CFD
KW - Machine learning
KW - Mixing in upper plenum
KW - Turbulence modeling
UR - https://www.scopus.com/pages/publications/85202948453
UR - https://www.mendeley.com/catalogue/3827e4e0-150d-361d-b6a0-0d7e08990ef6/
U2 - 10.13182/NURETH20-40227
DO - 10.13182/NURETH20-40227
M3 - Conference contribution
AN - SCOPUS:85202948453
T3 - Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023
SP - 4544
EP - 4557
BT - Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023
PB - American Nuclear Society
T2 - 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023
Y2 - 20 August 2023 through 25 August 2023
ER -