Abstract
We introduce a deep learned subgrid-scale surrogate model for dry, stratified idealized atmospheric flows from high-resolution simulation data. Deep neural networks (NNs) are used to model the full state differences between a coarse resolution simulation and a high-resolution simulation, run simultaneously with the coarse resolution simulation forced by the high-resolution simulation, hence capturing both dissipative and anti-dissipative effects. The NN model is able to accurately capture the state differences in a priori tests outside the training regime. In a posteriori tests intended for production use, the NN coupled coarse simulation is accurate compared to the high-resolution simulation over a finite period in time. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after a while. These surrogate models further pave the way for formulating stable, complex, physics-based NN models which are driven by traditional subgrid-scale turbulence closure models.
Original language | English |
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State | Published - 2022 |
Event | 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022 - Osaka, Virtual, Japan Duration: Jul 19 2022 → Jul 22 2022 |
Conference
Conference | 12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022 |
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Country/Territory | Japan |
City | Osaka, Virtual |
Period | 07/19/22 → 07/22/22 |
Funding
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
Funders | Funder number |
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U.S. Department of Energy | DE-AC05-00OR22725 |
U.S. Department of Energy | |
Office of Science | |
National Nuclear Security Administration |