The approximate deconvolution model for large-eddy simulation of compressible flows and its application to shock-turbulent-boundary-layer interaction

S. Stolz, N. A. Adams, L. Kleiser

Research output: Contribution to journalArticlepeer-review

225 Scopus citations

Abstract

A formulation of the approximate deconvolution model (ADM) for the large-eddy simulation (LES) of compressible flows in complex geometries is detailed. The model is applied to supersonic compression ramp flow where shock-turbulence interaction occurs. With the ADM approach an approximation to the unfiltered solution is obtained from the filtered solution by a series expansion involving repeated filtering. Given a sufficiently good approximation of the unfiltered solution at a time instant, the flux terms of the underlying filtered transport equations can be computed directly, avoiding the need to explicitly compute subgrid-scale closures. The effect of nonrepresented scales is modeled by a relaxation regularization involving a secondary filter operation and a dynamically estimated relaxation parameter. Results of the large-eddy simulation of the turbulent supersonic boundary layer along a compression ramp compare well with filtered DNS data. The filtered shock solution is correctly predicted by the ADM procedure, demonstrating that turbulent and nonturbulent subgrid-scales are properly modeled. We found that a computationally expensive shock-capturing technique was not necessary for stable integration. As a consequence, the computational effort for simulations with ADM is approximately as large as for a coarse-grid DNS with a hybrid compact-upwind-ENO scheme, since the additional computational cost for the subgrid-scale model is more than compensated due to the fact that in the LES flux-derivatives can be computed by linear central finite differences on the entire domain.

Original languageEnglish
Pages (from-to)2985-3001
Number of pages17
JournalPhysics of Fluids
Volume13
Issue number10
DOIs
StatePublished - Oct 2001
Externally publishedYes

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