Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty

Pranav Chandramouli 1 Dominique Heitz 2 Sylvain Laizet 3 Etienne Mémin 1
1 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRMAR - Institut de Recherche Mathématique de Rennes, IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Inria Rennes – Bretagne Atlantique
Abstract : The focus of this paper is to perform coarse-grid large eddy simulation (LES) using recently developed sub-grid scale (SGS) models of cylinder wake flow at Reynolds number (Re) of 3900. As we approach coarser resolutions, a drop in accuracy is noted for all LES models but more importantly, the numerical stability of classical models is called into question. The objective is to identify a statistically accurate, stable sub-grid scale (SGS) model for this transitional flow at a coarse resolution. The proposed new models under location uncertainty (MULU) are applied in a deterministic coarse LES context and the statistical results are compared with variants of the Smagorinsky model and various reference data-sets (both experimental and Direct Numerical Simulation (DNS)). MULU are shown to better estimate statistics for coarse resolution (at 0.46% the cost of a DNS) while being numerically stable. The performance of the MULU is studied through statistical comparisons , energy spectra, and sub-grid scale (SGS) contributions. The physics behind the MULU are characterised and explored using divergence and curl functions. The additional terms present (velocity bias) in the MULU are shown to improve model performance. The spanwise periodicity observed at low Reynolds is achieved at this moderate Reynolds number through the curl function, in coherence with the birth of streamwise vortices.
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Pranav Chandramouli, Dominique Heitz, Sylvain Laizet, Etienne Mémin. Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty. Computers and Fluids, Elsevier, 2018, 168, pp.170-189. ⟨10.1016/j.compfluid.2018.04.001⟩. ⟨hal-01764616⟩

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