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CFD and Artificial Neural Networks Analysis of Plane Sudden Expansion Flows
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International Journal of Engineering (IJE)
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Volume:  4    Issue:  4
Pages:  262-320
Publication Date:   October 2010
ISSN (Online): 1985-2312
296 - 307
Lyes Khezzar - United Arab Emirates
Published Date   
CSC Journals, Kuala Lumpur, Malaysia
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
KEYWORDS:   sudden expansion, CFD, ANN, fluid mechanics 
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It has been clearly established that the reattachment length for laminar flow depends on two non-dimensional parameters, the Reynolds number and the expansion ratio, therefore in this work, an ANN model that predict reattachment positions for the expansion ratios of 2, 3 and 5 based on the above two parameters has been developed. The R2 values of the testing set output Xr1, Xr2, Xr3, and Xr4 were 0.9383, 0.8577, 0.997 and 0.999 respectively. These results indicate that the network model produced reattachment positions that were in close agreement with the actual values. When considering the reattachment length of plane sudden-expansions the judicious combination of CFD calculated solutions with ANN will result in a considerable saving in computing and turnaround time. Thus CFD can be used in the first instance to obtain reattachment lengths for a limited choice of Reynolds numbers and ANN will be used subsequently to predict the reattachment lengths for other intermediate Reynolds number values. The CFD calculations concern unsteady laminar flow through a plane sudden expansion and are performed using a commercial CFD code STAR-CD while the training process of the corresponding ANN model was performed using the NeuroShellTM simulator. 
Lyes Khezzar : Colleagues
Saleh M. Al-Alawi : Colleagues  
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