Document Zbl 1154.76029

POD based reconstruction of subgrid stresses for wall bounded flows using neural networks. (English) Zbl 1154.76029

Flow Turbul. Combust. 81, No. 1-2, 77-96 (2008).

Summary: We develop a zonal hybrid method for computation of wall-bounded flows. Data from direct numerical simulations of channel flow at Reynolds number 500 were filtered and the resulting subgrid stresses expanded in a series using proper orthogonal decomposition (POD). 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 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.

Cited in 1 Document

MSC:

76F65	Direct numerical and large eddy simulation of turbulence
76F40	Turbulent boundary layers
92B20	Neural networks for/in biological studies, artificial life and related topics

Keywords:

zonal hybrid method; linear stochastic estimation; Smagorinsky model; boundary layer

Software:

Matlab

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Full Text: DOI

References:

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