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)

1 LASMEA - Laboratoire des sciences et matériaux pour l'électronique et d'automatique
2 LRI - Laboratoire de Recherche en Informatique
3 TANC - Algorithmic number theory for cryptology
4 LMFA - Laboratoire de Mecanique des Fluides et d'Acoustique

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.

Mots clés: large eddy simulation; optimal estimation; neural networks; turbulence; passive scalar; turbulent combustion

Domaines: Mécanique des fluides [physics.class-ph]; Réseau de neurones [cs.NE]; Mécanique des fluides [physics.class-ph]

Antoine Moreau, Olivier Teytaud, Jean-Pierre Bertoglio. Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models. Physics of Fluids, 2006, 18, pp.105101. ⟨10.1063/1.2357974⟩. ⟨hal-00078115v2⟩