We perform a direct numerical simulation (DNS) of forced homogeneous isotropic turbulence with a passive scalar that is forced by mean gradient. The DNS data are used to study the properties of subgrid-scale flux of a passive scalar in the framework of large eddy simulation (LES), such as alignment trends between the flux, resolved, and subgrid-scale flow structures. It is shown that the direction of the flux is strongly coupled with the subgrid-scale stress axes rather than the resolved flow quantities such as strain, vorticity, or scalar gradient. We derive an approximate transport equation for the subgrid-scale flux of a scalar and look at the relative importance of the terms in the transport equation. A particular form of LES tensor-viscosity model for the scalar flux is investigated, which includes the subgrid-scale stress. Effect of different models for the subgrid-scale stress on the model for the subgrid-scale flux is studied.
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http://dx.doi.org/10.1103/PhysRevE.78.036313 | DOI Listing |
To capture the effects of mesoscale turbulent eddies, coarse-resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models.
View Article and Find Full Text PDFSci Total Environ
July 2022
School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China. Electronic address:
Face shield is a common personal protection equipment for pandemic. In the present work, three-dimensional computational fluid dynamic (CFD) method is used to simulate a cough jet from an emitter who wears a face shield. A realistic manikin model with a simplified mouth cavity is employed.
View Article and Find Full Text PDFPhys Rev E
May 2021
Institut für Thermo- und Fluiddynamik, Technische Universität Ilmenau, Postfach 100565, D-98684 Ilmenau, Germany.
Recurrent neural networks are machine learning algorithms that are well suited to predict time series. Echo state networks are one specific implementation of such neural networks that can describe the evolution of dynamical systems by supervised machine learning without solving the underlying nonlinear mathematical equations. In this work, we apply an echo state network to approximate the evolution of two-dimensional moist Rayleigh-Bénard convection and the resulting low-order turbulence statistics.
View Article and Find Full Text PDFWe demonstrate that an extended eddy-diffusivity mass-flux (EDMF) scheme can be used as a unified parameterization of subgrid-scale turbulence and convection across a range of dynamical regimes, from dry convective boundary layers, through shallow convection, to deep convection. Central to achieving this unified representation of subgrid-scale motions are entrainment and detrainment closures. We model entrainment and detrainment rates as a combination of turbulent and dynamical processes.
View Article and Find Full Text PDFJ Geophys Res Atmos
October 2019
National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.
The representation of land use (LU) in meteorological modeling strongly influences the simulation of fluxes of heat, moisture, and momentum; affecting the accuracy of 2-m temperature and precipitation. Here, the Weather Research and Forecasting (WRF) model is used with the Noah land surface model to compare a mosaic approach, which accounts for subgrid scale variability of LU types, to the default option which only considers the dominant category in each grid cell. Three-year historical dynamically downscaled WRF simulations are generated using a 12-km domain over the contiguous U.
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