RANS turbulence model development using CFD-driven machine learning

DOI10.1016/j.jcp.2020.109413zbMath1436.76011arXiv1902.09075OpenAlexW3013108861MaRDI QIDQ777616

Jack Weatheritt, Yaomin Zhao, Vittorio Michelassi, Harshal D. Akolekar, Richard D. Sandberg

Publication date: 7 July 2020

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.09075

zbMATH Keywords

machine learningturbulence modellingwake mixing

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Statistical turbulence modeling (76F55)

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• Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
• A more general effective-viscosity hypothesis
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• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data

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