Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer
Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (...
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
American Geophysical Union (AGU)
2022-05-01
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| Collection: | Journal of Advances in Modeling Earth Systems |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1029/2021MS002847 |
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