Machine learning from RANS and LES to inform coarse grid simulations

Iskhakov, Arsen S.; Dinh, Nam T.; Leite, Victor Coppo; Merzari, Elia

doi:10.1016/j.pnucene.2023.104809

Machine learning from RANS and LES to inform coarse grid simulations

Journal Article · Tue Jul 18 00:00:00 EDT 2023 · Progress in Nuclear Energy

DOI:https://doi.org/10.1016/j.pnucene.2023.104809· OSTI ID:2424804

^[1]; ^[1]; ^[2]; Merzari, Elia ^[2]

North Carolina State University, Raleigh, NC (United States)
Pennsylvania State University, University Park, PA (United States)

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

View Accepted Manuscript (DOE)

Research Organization:: North Carolina State University, Raleigh, NC (United States)

Sponsoring Organization:: USDOE

OSTI ID:: 2424804

Alternate ID(s):: OSTI ID: 1994575

Journal Information:: Progress in Nuclear Energy, Journal Name: Progress in Nuclear Energy Vol. 163; ISSN 0149-1970

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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