Error-landscape-based multi-objective calibration of the Smagorinsky eddy-viscosity using high-Reynolds-number decaying turbulence data

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Meyers, Johan and Meneveau, Charles and Geurts, Bernard J. (2010) Error-landscape-based multi-objective calibration of the Smagorinsky eddy-viscosity using high-Reynolds-number decaying turbulence data. Physics of Fluids , 22 (12). p. 125106. ISSN 1070-6631

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Abstract:A suite of large-eddy simulation(LESs) of decaying homogeneous isotropic turbulence at high Reynolds numbers is performed and compared to wind-tunnel experiments in the tradition of Comte-Bellot and Corrsin. The error-landscape approach is used for the evaluation of the Smagorinsky model, and the results are used to identify an optimal combination of model parameter and resolution in a statistically robust fashion. The use of experimental reference data in the error-landscape approach allows to evaluate the optimal Smagorinsky coefficient at high Reynolds numbers and to perform detailed comparisons with analytical predictions. We demonstrate, using a pseudospectral discretization, that the optimal so-called Smagorinsky trajectory obtained from the error-landscape analysis converges at high simulation resolutions to the high-Re theoretical Lilly prediction for the Smagorinsky coefficient. Using modified wavenumbers in the same spectral code, the current study also presents error-landscape results based on LES with “second-order” discretization errors. By slightly revising Lilly’s analysis, we show that including the effect of numerical discretization when evaluating the strain-rate tensor needed in the subgrid-scale model leads to a good prediction of the optimal Smagorinsky parameter obtained from the corresponding error-landscape. Using similar analytical tools, we further demonstrate that the dynamic procedure can also be adapted to better account for the effects of discretization and test-filter shape.
Item Type:Article
Copyright:© 2010 American Institute of Physics
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Electrical Engineering, Mathematics and Computer Science (EEMCS)
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Link to this item:http://purl.utwente.nl/publications/75586
Official URL:http://dx.doi.org/10.1063/1.3526758
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