Publication | Open Access
A Large-Eddy-Simulation Model for the Study of Planetary Boundary-Layer Turbulence
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1984
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Numerical AnalysisAeroacousticsEngineeringFluid MechanicsTurbulenceAtmospheric ModelGeophysical FlowBoundary LayerUnsteady FlowAtmospheric ScienceNumerical SimulationLarge-eddy FieldLarge Eddy SimulationMeteorologyPlanetary Boundary-layer TurbulenceAerospace EngineeringTurbulence ModelingAerodynamicsLes ModelClosure Assumptions
Large‑eddy simulation (LES) models explicitly resolve large atmospheric eddies while parameterizing smaller ones, a technique first applied by Deardorff and considered relatively insensitive to small‑eddy parameterization. The authors developed a new LES code employing a mixed pseudospectral finite‑difference method to systematically study boundary‑layer turbulence, aiming to provide 3‑D statistical insights that refine ensemble‑mean turbulence closures. The model combines a mixed pseudospectral finite‑difference scheme and fast Fourier transforms, and was validated against a simple vortex flow and the Wangara day‑33 dataset.
A large-eddy-simulation (LFS) model explicitly calculates the large-eddy field and parameterizes the small eddies. The large eddies in the atmospheric boundary layer are believed to be much more important and more flow-dependent than the small eddies. The LES model results are therefore believed to be relatively insensitive to the parameterization scheme for the small eddies. Deardorff first applied this type of numerical model to boundary-layer turbulence. In order to continue his important work, and to take advantage of the fast Fourier transformation algorithm, a new LES model code which uses a mixed pseudospectral finite-difference method was developed. This LES model is described here and tested with a simple vortex flow and with the Wangara day-33 data. This model will be used to systematically investigate fundamental problems in the area of boundary-layer turbulence. It is hoped that three-dimensional simulations will give useful statistical information about turbulence structural and improve the closure assumptions in ensemble-mean turbulence modeling.