Turbulence model reduction by deep learning
Abstract
A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.
- Publication Date:
- Research Org.:
- Univ. of California, San Diego, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES); National Science Foundation (NSF)
- OSTI Identifier:
- 1632122
- Grant/Contract Number:
- FG02-04ER54738; ACI-1548562
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review E
- Additional Journal Information:
- Journal Volume: 101; Journal Issue: 6; Journal ID: ISSN 2470-0045
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; 97 MATHEMATICS AND COMPUTING; drift waves; plasma turbulence; turbulent mixing; weak turbulence; artificial neural networks; machine learning
Citation Formats
Heinonen, R. A., and Diamond, Patrick H. Turbulence model reduction by deep learning. United States: N. p., 2020.
Web. doi:10.1103/PhysRevE.101.061201.
Heinonen, R. A., & Diamond, Patrick H. Turbulence model reduction by deep learning. United States. https://doi.org/10.1103/PhysRevE.101.061201
Heinonen, R. A., and Diamond, Patrick H. Thu .
"Turbulence model reduction by deep learning". United States. https://doi.org/10.1103/PhysRevE.101.061201. https://www.osti.gov/servlets/purl/1632122.
@article{osti_1632122,
title = {Turbulence model reduction by deep learning},
author = {Heinonen, R. A. and Diamond, Patrick H.},
abstractNote = {A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.},
doi = {10.1103/PhysRevE.101.061201},
journal = {Physical Review E},
number = 6,
volume = 101,
place = {United States},
year = {2020},
month = {6}
}
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