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Langmuir turbulence consists of Langmuir circulation (LC) generated at the surface of rivers, lakes, bays, and oceans by the interaction between the wind-driven shear and surface gravity waves. In homogeneous shallow water, LC can extend to the bottom of the water column and interact with the bottom boundary layer. Large-eddy simulation (LES) of LC in shallow water was performed with the finite volume method and various forms of subgrid-scale (SGS) model characterized by different near-wall treatments of the SGS eddy viscosity. The wave forcing relative to wind forcing in the LES was set following the field measurements of full-depth LC during the presence of LC engulfing a water column 15 m in depth in the coastal ocean, reported in the literature. It is found that the SGS model can greatly impact the structure of LC in the lower half of the water column. Results are evaluated in terms of (1) the Langmuir turbulence velocity statistics and (2) the lateral (crosswind) length scale and overall cell structure of LC. LES with an eddy viscosity with velocity scale in terms of S and Ω (where S is the norm of the strain rate tensor and Ω is the norm of the vorticity tensor) and a Van Driest wall damping function (referred to as the S-Omega model) is found to provide best agreement with pseudo-spectral LES in terms of the lateral length scale and overall cell structure of LC. Two other SGS models, namely the dynamic Smagorinsky model and the wall-adapting local-eddy viscosity model are found to provide less agreement with pseudo- spectral LES, for example, as they lead to less coherent bottom convergence of the cells and weaker associ ated upward transport of slow downwind moving fluid. Finally, LES with the S-Omega SGS model is also found to lead to good agreement with physical measurements of LC in the coastal ocean in terms of Langmuir turbulence decay during periods of surface heating
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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 (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 numbersReτup 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 ina posteriori(online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.
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- PAR ID:
- 10368452
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 14
- Issue:
- 5
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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