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Stochastic coherent adaptive large eddy simulation of forced isotropic turbulence

Published online by Cambridge University Press:  08 March 2010

G. DE STEFANO  and
O. V. VASILYEV
G. DE STEFANO
Affiliation:
Dipartimento di Ingegneria Aerospaziale e Meccanica, Seconda Università di Napoli, I 81031 Aversa, Italy
O. V. VASILYEV*
Affiliation:
Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
*
†Email address for correspondence: oleg.vasilyev@colorado.edu
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Abstract

The stochastic coherent adaptive large eddy simulation (SCALES) methodology is a novel approach to the numerical simulation of turbulence, where a dynamic grid adaptation strategy based on wavelet threshold filtering is utilized to solve for the most ‘energetic’ eddies. The effect of the less energetic unresolved motions is simulated by a model. Previous studies have demonstrated excellent predictive properties of the SCALES approach for decaying homogeneous turbulence. In this paper the applicability of the method is further explored for statistically steady turbulent flows by considering linearly forced homogeneous turbulence at moderate Reynolds number. A local dynamic subgrid-scale eddy viscosity model based on the definition of the kinetic energy associated with the unresolved motions is used as closure model. The governing equations for the wavelet filtered velocity field, along with the additional evolution equation for the subgrid-scale kinetic energy, are numerically solved by means of a dynamically adaptive wavelet collocation method. It is demonstrated that adaptive simulations closely match results from a reference pseudo-spectral fully de-aliased direct numerical simulation, by using only about 1% of the corresponding computational nodes. In contrast to classical non-adaptive large eddy simulation, the agreement with direct solution holds for the mean flow statistics as well as in terms of energy and enstrophy spectra up to the dissipative wavenumbers range.

Type
Papers
Information
Journal of Fluid Mechanics , Volume 646 , 10 March 2010 , pp. 453 - 470
Copyright
Copyright © Cambridge University Press 2010

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