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Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges

  • Regular Article – Soft Matter
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Abstract

We examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge discussions with Adrian Lozano-Durán, Tim Flint, Michael Patrick Whitmore and Sanjeeb Bose. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under contract no. DE-NA0003525 with the US Department of Energy (DOE). The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this article or allow others to do so, for US Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan SAND2022-16617 B (https://www.energy.gov/downloads/doe-public-access-plan).

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Authors and Affiliations

  1. Center for Turbulence Research, Stanford University, Stanford, CA, USA

    Mark Benjamin & Gianluca Iaccarino

  2. Sandia National Laboratories, Albuquerque, NM, USA

    Stefan P. Domino

Authors
  1. Mark Benjamin
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  2. Stefan P. Domino
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  3. Gianluca Iaccarino
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Contributions

MB and GI did conceptualization; MB, SPD, GI did the data curation; MB and GI performed formal analysis; GI did the funding acquisition; MB, SPD, GI were involved in investigation and methodology; GI supervised the study; MB did writing—original draft; SPD and GI performed writing—review & editing.

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Correspondence to Mark Benjamin.

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Benjamin, M., Domino, S.P. & Iaccarino, G. Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges. Eur. Phys. J. E 46, 55 (2023). https://doi.org/10.1140/epje/s10189-023-00314-6

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  • DOI: https://doi.org/10.1140/epje/s10189-023-00314-6

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