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

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-tau 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.

Automated summary

In large-eddy simulations, subgrid-scale processes are parameterized as a function of filtered grid-scale variables. This paper applies supervised deep neural networks (DNNs) to learn subgrid stresses and achieves higher correlation compared to traditional models, with applicability to different resolutions and stability conditions.