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Physics of Fluids 18 (2006) 105101
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Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models
Antoine Moreau 1, Olivier Teytaud 2, 3, Jean-Pierre Bertoglio 4
(2006-10-04)

The tools of optimal estimation are applied to the study of subgrid models for Large-Eddy Simulation of turbulence. The concept of optimal estimator is introduced and its properties are analyzed in the context of applications to a priori tests of subgrid models. Attention is focused on the Cook and Riley model in the case of a scalar field in isotropic turbulence. Using DNS data, the relevance of the beta assumption is estimated by computing (i) generalized optimal estimators and (ii) the error brought by this assumption alone. Optimal estimators are computed for the subgrid variance using various sets of variables and various techniques (histograms and neural networks). It is shown that optimal estimators allow a thorough exploration of models. Neural networks are proved to be relevant and very efficient in this framework, and further usages are suggested.
1:  Laboratoire des sciences et matériaux pour l'électronique et d'automatique (LASMEA)
CNRS : UMR6602 – Université Blaise Pascal - Clermont-Ferrand II
2:  Laboratoire de Recherche en Informatique (LRI)
CNRS : UMR8623 – Université Paris XI - Paris Sud
3:  TAO (INRIA Futurs)
INRIA – CNRS : UMR8623 – Université Paris XI - Paris Sud
4:  Laboratoire de Mecanique des Fluides et d'Acoustique (LMFA)
CNRS : UMR5509 – Université Claude Bernard - Lyon I – Ecole Centrale de Lyon – Institut National des Sciences Appliquées de Lyon
Physics/Mechanics/Mechanics of the fluids

Engineering Sciences/Mechanics/Fluids mechanics

Computer Science/Neural and Evolutionary Computing
large eddy simulation – optimal estimation – neural networks – turbulence – passive scalar – turbulent combustion
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