Staff Members - Benha University
Radial basis function neural network model for mean velocity and vorticity of capillary flow
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS • 2011
Publication Information
Authors
Mostafa Y. El-Bakry
Keywords
neural networks; radial basis function neural network; mean velocity; vorticity; laminar
flow; capillary flow
Journal
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS
Publisher
Copyright 2010 John Wiley & Sons, Ltd.
Volume
67
Issue
Not Available
Pages
1283–1290
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
The radial basis function neural network (RBFNN) simulation has been designed to simulate and predict
the mean velocity of capillary flow in transition from laminar to turbulent flow and the root-mean-square
vorticity as a function of wall-normal position at different values of Reynolds number. The system was
trained on the available data of the two cases. Therefore, we designed the system to work in automatic
way for finding the best network that has the ability to have the best test and prediction. The proposed
system shows an excellent agreement with that of an experimental data in these cases. The technique has
been also designed to simulate the other distributions not presented in the training set and predicted them
with effective matching.
the mean velocity of capillary flow in transition from laminar to turbulent flow and the root-mean-square
vorticity as a function of wall-normal position at different values of Reynolds number. The system was
trained on the available data of the two cases. Therefore, we designed the system to work in automatic
way for finding the best network that has the ability to have the best test and prediction. The proposed
system shows an excellent agreement with that of an experimental data in these cases. The technique has
been also designed to simulate the other distributions not presented in the training set and predicted them
with effective matching.