Subgrid-scale modeling of reacting scalar fluxes in large-eddy simulations of atmospheric boundary layers
Jean-François Vinuesa, Fernando Porté-Agel, Sukanta Basu, Rob Stoll
Abstract
In large-eddy simulations of atmospheric boundary layer turbulence, the lumped coefficient in the eddy-diffusion subgrid-scale (SGS) model is known to depend on scale for the case of inert scalars. This scale dependence is predominant near the surface. In this paper, a scale-dependent dynamic SGS model for the turbulent transport of reacting scalars is implemented in large-eddy simulations of a neutral boundary layer. Since the model coefficient is computed dynamically from the dynamics of the resolved scales, the simulations are free from any parameter tuning. A set of chemical cases representative of various turbulent reacting flow regimes is examined. The reactants are involved in a first-order reaction and are injected in the atmospheric boundary layer with a constant and uniform surface flux. Emphasis is placed on studying the combined effects of resolution and chemical regime on the performance of the SGS model. Simulations with the scale-dependent dynamic model yield the expected trends of the coefficients as function of resolution, position in the flow and chemical regime, leading to resolution-independent turbulent reactant fluxes.
Create a lesson
Related papers
Morphology-based retrieval of local wave parameters and phase shifts in satellite-observed atmospheric disturbances
Yuriy Klymenko, Alla Fedorenko, Evgen Kryuchkov et al.
Parametric wave spectrum models for prediction of Stokes drift profiles
W. Erick Rogers, Yalin Fan, Luis Zamudio et al.
Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX
Iris Linck, John Dennis, John Schreck et al.
legoESM: a modular, differentiable, multiscale, AI-ready Earth system model built with AI agents
Pierre Gentine, Dhruv Balwada, Aytaç Paçal et al.
A Physics-Constrained Implicit Profile Network for Continuous Reconstruction of Tropical Cyclone Near-Surface Wind Profiles
Jian Ma, Yilin Yang, Robert Rogers et al.
A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times
Li Zhang, Jun Wang, Isidora Jankov et al.