Title: Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Journal Article · · Journal of Advances in Modeling Earth Systems
ORCiD logo [1];  [2];  [3];  [3];  [3];  [3];  [1]; ORCiD logo [4];  [1]; ORCiD logo [5]; ORCiD logo [6]
  1. Department of Earth and Environmental Engineering Columbia University New York NY USA
  2. Department of Civil Engineering and Engineering Mechanics Columbia University New York NY USA
  3. Capstone Project at Data Science Institute Columbia University New York NY USA
  4. School of Civil and Environmental Engineering Cornell University Ithaca NY USA
  5. Department of Earth and Environmental Sciences Columbia University Palisades NY USA
  6. Department of Earth and Environmental Engineering Columbia University New York NY USA, Department of Earth and Environmental Sciences Columbia University Palisades NY USA
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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 (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

Research Organization:
Columbia University, New York, NY (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0014203
OSTI ID:
1869503
Journal Information:
Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 5 Vol. 14; ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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