Flow, Turbulence and Combustion

, Volume 81, Issue 1–2, pp 77–96 | Cite as

POD Based Reconstruction of Subgrid Stresses for Wall Bounded Flows Using Neural Networks

Article

Abstract

A zonal hybrid method for the computation of wall bounded flows was developed. Data from a direct numerical simulation of channel flow at Reynolds number 500 were filtered and the resulting subgrid stresses expanded in a series using proper orthogonal decomposition. The series was truncated. A feed forward neural network was found to be superior to linear stochastic estimation for estimating the coefficient of the series. The neural network and the orthonormal base from the expansion were shown by a priori tests to be suitable as a subgrid model for the innermost part of a boundary layer. The system was applied together with a Smagorinsky subgrid model to channel flow at Reynolds number 500 with good results. Possible improvements and generalization to other types of flows and higher Reynolds numbers are discussed.

Keywords

LES POD Neural network Subgrid stresses 

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Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Division of Fluid Dynamics, Department of Applied MechanicsChalmers University of TechnologyGothenburgSweden

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