Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3769–3788, 2021
https://doi.org/10.5194/gmd-14-3769-2021
Geosci. Model Dev., 14, 3769–3788, 2021
https://doi.org/10.5194/gmd-14-3769-2021

Development and technical paper 24 Jun 2021

Development and technical paper | 24 Jun 2021

Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow

Robin Stoffer et al.

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Cited articles

Bardina, J., Ferziger, J., and Reynolds, W.: Improved subgrid-scale models for large-eddy simulation, in: 13th fluid and plasmadynamics conference, 1357, https://doi.org/10.2514/6.1980-1357, 1980. a
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Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, 025105, https://doi.org/10.1063/1.1839152, 2005. a
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Short summary
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which requires subgrid models to account for the smallest scales. Current subgrid models often require strong simplifying assumptions. We therefore developed a subgrid model based on artificial neural networks, which requires fewer assumptions. Our data-driven SGS model showed high potential in accurately representing the smallest scales but still introduced instability when incorporated into an actual LES.