@inproceedings{f6595933c796419d83492da56618e3c9,
title = "Data-Driven RANS Turbulence Modeling of Mixed Convection in Reactor Downcomer Geometry",
abstract = "Several advanced nuclear reactor designs consider the use of low-Prandtl fluids (liquid metals) as coolants. However, their Reynolds-averaged Navier-Stokes (RANS) modeling is hampered by the absence of reliable turbulence closure models for Reynolds stresses (RS) and turbulent heat fluxes (THF). Accurate modeling of RS and THF is especially important when buoyancy effects take place in a flow. Since traditional physics-based models exhibit large model form uncertainties, there is a strong interest in data-driven (DD) modeling techniques. In this work, DD models for RS and THF are developed using a high-fidelity direct numerical simulation (DNS) database. The DNS database consists of sodium flows in the reactor downcomer geometry in the presence of buoyancy effects (mixed convection). The buoyancy is considered through the Boussinesq approximation. The DD models are based on invariant tensor basis neural network architectures. Their performance is investigated and discussed for selected flow conditions.",
author = "Iskhakov, {Arsen S.} and Tai, {Cheng Kai} and Bolotnov, {Igor A.} and Dinh, {Nam T.} and Elia Merzari",
note = "Publisher Copyright: {\textcopyright} 2023 Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023. All rights reserved.; 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023 ; Conference date: 20-08-2023 Through 25-08-2023",
year = "2023",
doi = "10.13182/NURETH20-40226",
language = "English (US)",
series = "Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023",
publisher = "American Nuclear Society",
pages = "5004--5017",
booktitle = "Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2023",
address = "United States",
}