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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-289
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-289
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 10 Nov 2020

Submitted as: development and technical paper | 10 Nov 2020

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This preprint is currently under review for the journal GMD.

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

Robin Stoffer1, Caspar M. van Leeuwen2, Damian Podareanu2, Valeriu Codreanu2, Menno A. Veerman1, Martin Janssens1, Oscar K. Hartogensis1, and Chiel C. van Heerwaarden1 Robin Stoffer et al.
  • 1Meteorology and Air Quality Group, Wageningen University, Wageningen, The Netherlands
  • 2SURFsara, Amsterdam, The Netherlands

Abstract. Atmospheric boundary layers and other wall-bounded flows are often simulated with the large-eddy simulation (LES) technique, which relies on subgrid-scale (SGS) models to parameterize the smallest scales. These SGS models often make strong simplifying assumptions. Also, they tend to interact with the discretization errors introduced by the popular LES approach where a staggered finite-volume grid acts as an implicit filter. We therefore developed an alternative LES SGS model based on artificial neural networks (ANNs) for the computational fluid dynamics code MicroHH (v2.0), which can be run in direct numerical simulation (DNS) and LES mode. We used a turbulent channel flow (with a friction Reynolds number Reτ = 590) as a test case. The developed SGS model has been designed to require fewer simplifying assumptions, and to compensate for the instantaneous discretization errors introduced by the staggered finite-volume grid. We trained the ANNs based on instantaneous flow fields from a direct numerical simulation (DNS) of the selected channel flow. In general, we found excellent agreement between the ANN predicted SGS fluxes and the SGS fluxes derived from DNS for flow fields not used during training (with the correlation coefficient ρ mostly varying between 0.6 and 1.0), showing the potential ANNs may have to construct highly accurate SGS models. However, we observed an artificial build-up of turbulence kinetic energy at high wave modes when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesized that error accumulation and aliasing errrors, were both important contributors to the observed instability. Several obstacles therefore remain before the a priori promise of our ANN LES SGS model, can be successfully leveraged in practical applications.

Robin Stoffer et al.

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Robin Stoffer et al.

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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 incoporated into an actual LES.
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which...
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