https://doi.org/10.1140/epje/s10189-023-00302-w
Regular Article - Flowing Matter
Toward accelerated data-driven Rayleigh–Bénard convection simulations
1
Google Research, 94043, Mountain View, CA, USA
2
Institute for Computational and Mathematical Engineering, Stanford University, 94305, Stanford, CA, USA
3
School of Engineering and Applied Sciences, Harvard University, 02138, Cambridge, MA, USA
Received:
8
February
2023
Accepted:
16
May
2023
Published online:
28
July
2023
A hybrid data-driven/finite volume method for 2D and 3D thermal convective flows is introduced. The approach relies on a single-step loss, convolutional neural network that is active only in the near-wall region of the flow. We demonstrate that the method significantly reduces errors in the prediction of the heat flux over the long-time horizon and increases pointwise accuracy in coarse simulations, when compared to direct computations on the same grids with and without a traditional subgrid model. We trace the success of our machine learning model to the choice of the training procedure, incorporating both the temporal flow development and distributional bias.
Stephan Hoyer, Michae Brenner, Gianluca Iaccarino and Peter Norgaard have contributed equally to this work.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
