Application of gene expression programming to a-posteriori LES modeling of a Taylor Green vortex

DOI10.1016/j.jcp.2020.109859OpenAlexW3088916089MaRDI QIDQ2123916

Richard D. Sandberg, Maximilian Reissmann, J. Hasslberger, Markus Klein

Publication date: 14 April 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2020.109859

zbMATH Keywords

evolutionary algorithmgene expression programminga-posteriori LESin-the-loop optimizationTaylor Green vortex

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Computer science (68-XX)

Related Items (2)

Invariant data-driven subgrid stress modeling in the strain-rate eigenframe for large eddy simulation ⋮ Invariant data-driven subgrid stress modeling on anisotropic grids for large eddy simulation

Cites Work

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• Statistics of turbulence subgrid-scale stresses: Necessary conditions and experimental tests
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data
• A rational model for the turbulent scalar fluxes

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