A framework to develop data-driven turbulence models for flows with organised unsteadiness
DOI10.1016/J.JCP.2019.01.022zbMath1451.76060OpenAlexW2911525369WikidataQ128459693 ScholiaQ128459693MaRDI QIDQ2214628
Jimmy Philip, Chitrarth Lav, Richard D. Sandberg
Publication date: 10 December 2020
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11343/241909
machine learningDNSturbulence closurepartially averaged Navier-Stokesscale resolving simulationunsteady Reynolds-averaged Navier-Stokes
Artificial neural networks and deep learning (68T07) Navier-Stokes equations for incompressible viscous fluids (76D05) Statistical turbulence modeling (76F55) Direct numerical and large eddy simulation of turbulence (76F65)
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Cites Work
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