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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Robin Stoffer on behalf of the Authors (14 Apr 2021)  Author's response    Manuscript
ED: Publish as is (08 May 2021) by Richard Mills
AR by Robin Stoffer on behalf of the Authors (18 May 2021)  Author's response    Manuscript
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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.