Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3769–3788, 2021
Geosci. Model Dev., 14, 3769–3788, 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,, 1980. a
Beck, A., Flad, D., and Munz, C.: Deep neural networks for data-driven LES closure models, J. Comput. Phys., 398, 108910,, 2019. a, b, c, d, e, f
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399,, 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,, 2005. a
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.