Three-Dimensional Swirl Flow Velocity-Field Reconstruction Using a Neural Network With Radial Basis Functions

[+] Author and Article Information
J. Pruvost, J. Legrand, P. Legentilhomme

GEPEA, University of Nantes, CRTT-IUT, BP 406, F-44602 Saint-Nazaire Cedex, France

J. Fluids Eng 123(4), 920-927 (May 15, 2001) (8 pages) doi:10.1115/1.1412847 History: Received October 25, 2000; Revised May 15, 2001
Copyright © 2001 by ASME
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Schematic representation of the tangential inlet and the swirling decaying flow
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Experimental set-up for flow-field investigation using PIV measurement method
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Neural network architecture
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Influence of the size of the learning set of data on the neural network reconstruction efficiency
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Influence of the number of angular positions of the learning set of data on the neural network reconstruction efficiency
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Mean velocity reconstruction. (a) Axial component; (b) radial component; (c) circumferential component
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Turbulent intensity reconstruction. (a) Axial component; (b) radial component; (c) circumferential component
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Examples of velocity-field reconstruction. (a) Axial-radial plane projection (ξ=0); (b) Radial-circumferential plane projection (x=205 mm)




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