Architecture of Approximate Deconvolution Models of Turbulence

A. Labovschii, W. Layton, C. Manica, M. Neda, L. Rebholz, Iuliana Stanculescu, C. Trenchea

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This report presents the mathematical foundation of approximate deconvolution LES models together with the model phenomenology downstream of the theory. This mathematical foundation now begins to be complete for the incompressible Navier–Stokes equations. It is built upon averaging, deconvolving and addressing closure so as to obtain the physically correct energy and helicity balances in the LES model. We show how this is determined and how correct energy balance implies correct prediction of turbulent statistics. Interestingly, the approach is simple and thus gives a road map to develop models for more complex turbulent flows. We illustrate this herein for the case of MHD turbulence.

Original languageAmerican English
Title of host publicationQuality and Reliability of Large-Eddy Simulations
EditorsJohan Meyers, Bernard J. Geurts, Pierre Sagaut
Pages3-20
Number of pages18
StatePublished - Jan 1 2008

Publication series

NameERCOFTAC Series
Volume12

Funding

This work was partially supported by NSF Grant DMS 0508260

FundersFunder number
National Sleep FoundationDMS 0508260

    ASJC Scopus Subject Areas

    • Fluid Flow and Transfer Processes
    • Computational Mathematics

    Keywords

    • Deconvolution
    • Energy cascade
    • Helicity
    • MHD

    Disciplines

    • Mathematics

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