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• W3168361878 abstract "A data-driven wall model for turbulent flows over periodic hills is developed using a feedforward neural network and wall-resolved large-eddy simulation data. The developed wall model employs wall-normal distance, near-wall velocities, and pressure gradients as input features, and the wall shear stresses as output labels, respectively. In the $a$ $pphantom{rule{0}{0ex}}rphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}ophantom{rule{0}{0ex}}rphantom{rule{0}{0ex}}i$ test, the accuracy of the trained wall model is examined using periodic hill cases at different Reynolds numbers and with different hill geometries. In the $a$ $pphantom{rule{0}{0ex}}ophantom{rule{0}{0ex}}sphantom{rule{0}{0ex}}tphantom{rule{0}{0ex}}ephantom{rule{0}{0ex}}rphantom{rule{0}{0ex}}iphantom{rule{0}{0ex}}ophantom{rule{0}{0ex}}rphantom{rule{0}{0ex}}i$ test, the trained wall model is applied to the flow over periodic hills and turbulent channel flows." @default.
• W3168361878 created "2021-06-22" @default.
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• W3168361878 date "2021-05-27" @default.
• W3168361878 modified "2023-10-17" @default.
• W3168361878 title "Wall model based on neural networks for LES of turbulent flows over periodic hills" @default.
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• W3168361878 doi "https://doi.org/10.1103/physrevfluids.6.054610" @default.
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