4.5 Article

Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques

期刊

COMPUTERS & FLUIDS
卷 185, 期 -, 页码 34-58

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compfluid.2019.03.025

关键词

Large eddy simulation; Uncertainty quantification; Wall modeling; OpenFOAM

资金

  1. Swedish Research Council [621-2012-3721]

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The predictive accuracy of wall-modeled large eddy simulation is studied by systematic simulation campaigns of turbulent channel flow. The effect of wall model, grid resolution and anisotropy, numerical convective scheme and subgrid-scale modeling is investigated. All of these factors affect the resulting accuracy, and their action is to a large extent intertwined. The wall model is of the wall-stress type, and its sensitivity to location of velocity sampling, as well as law of the wall's parameters is assessed. For efficient exploration of the model parameter space (anisotropic grid resolution and wall model parameter values), generalized polynomial chaos expansions are used to construct metamodels for the responses which are taken to be measures of the predictive error in quantities of interest (Qols). The Qols include the mean wall shear stress and profiles of the mean velocity, the turbulent kinetic energy, and the Reynolds shear stress. DNS data is used as reference. Within the tested framework, a particular second-order accurate CFD code (OpenFOAM), the results provide ample support for grid and method parameters recommendations which are proposed in the present paper, and which provide good results for the Qols. Notably, good results are obtained with a grid with isotropic (cubic) hexahedral cells, with 15 000 cells per delta(3), where delta is the channel half-height (or thickness of the turbulent boundary layer). The importance of providing enough numerical dissipation to obtain accurate Qols is demonstrated. The main channel flow case investigated is Re-tau = 5200, but extension to a wide range of Re-numbers is considered. Use of other numerical methods and software would likely modify these recommendations, at least slightly, but the proposed framework is fully applicable to investigate this as well. (C) 2019 Elsevier Ltd. All rights reserved.

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