Numerical dissipation vs. subgrid-scale modelling for large eddy simulation

被引:80
作者
Dairay, Thibault [1 ]
Lamballais, Eric [1 ]
Laizet, Sylvain [2 ]
Vassilicos, John Christos [2 ]
机构
[1] Univ Poitiers, CNRS, ISAE ENSMA, Inst Pprime, F-86962 Futuroscope, France
[2] Imperial Coll London, Dept Aeronaut, London SW7 2AZ, England
关键词
Large eddy simulation; Subgrid-scale modelling; Numerical dissipation; Spectral vanishing viscosity; Pao's spectrum; 3D Taylor-Green flow; SPECTRAL VANISHING VISCOSITY; TURBULENT FLOWS; ERRORS; LES; JET; VELOCITY; SCHEMES; DECAY;
D O I
10.1016/j.jcp.2017.02.035
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This study presents an alternative way to perform large eddy simulation based on a targeted numerical dissipation introduced by the discretization of the viscous term. It is shown that this regularisation technique is equivalent to the use of spectral vanishing viscosity. The flexibility of the method ensures high-order accuracy while controlling the level and spectral features of this purely numerical viscosity. A Pao-like spectral closure based on physical arguments is used to scale this numerical viscosity a priori. It is shown that this way of approaching large eddy simulation is more efficient and accurate than the use of the very popular Smagorinsky model in standard as well as in dynamic version. The main strength of being able to correctly calibrate numerical dissipation is the possibility to regularise the solution at the mesh scale. Thanks to this property, it is shown that the solution can be seen as numerically converged. Conversely, the two versions of the Smagorinsky model are found unable to ensure regularisation while showing a strong sensitivity to numerical errors. The originality of the present approach is that it can be viewed as implicit large eddy simulation, in the sense that the numerical error is the source of artificial dissipation, but also as explicit subgrid-scale modelling, because of the equivalence with spectral viscosity prescribed on a physical basis. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:252 / 274
页数:23
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