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CFD and Artificial Neural Networks Analysis of Plane Sudden Expansion Flows
Lyes Khezzar, Saleh M. Al-Alawi
Pages - 296 - 307     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
sudden expansion, CFD, ANN, fluid mechanics
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.
CITED BY (2)  
1 Kanna, P. R., Taler, J., Anbumalar, V., Santhosh Kumar, A. V., Pushparaj, A., & Christopher, D. S. (2015). Conjugate Heat Transfer from Sudden Expansion Using Nanofluid. Numerical Heat Transfer, Part A: Applications, 67(1), 75-99.
2 Part, A. (2013). Applications: An International Journal of Computation and Methodology. Numerical Heat Transfer, Part A, 63, 590-603.
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1 F. Durst, J. F.C. Pereira, C. Tropea. “The plane symmetric sudden-expansion flow at low Reynolds numbers”. J. Fluid Mechanics, 248:567-581, 1993.
2 W. Cherdron, F. Durst, J. H. Whitelaw. “Asymmetric flows and instabilities in symmetric ducts with sudden expansions”. J. Fluid Mechanics, 84:13-31, 1974.
3 R.M. Fearn, T. Mullin, K.A. Cliffe, K. “Non-linear flow phenomena in a symmetric sudden expansion”. J. Fluid Mechanics, 211:595-608, 1990.
4 F. Battaglia, S. J. Tavener, A. K. Kulkarni, C. L. Merkle. “Bifurcation of low Reynolds number flows in symmetric channels”. AIAA J., 35:99-105, 1997.
5 B. P. Leonard. “A stable and accurate connective modelling procedure based on quadratic upstream interpolation”. Comput. Methods Appl. Mech. Eng., 19:59-98, 1979.
6 R.I. Issa. “Solution of the implicitly discretised fluid flow equations by operator splitting”. J.Comput. Phys., 62:66-82, 1996.
7 S. S. S. Sakla. “Neural network modelling of the load-carrying capacity of eccentrically-loaded single angle strut”. Journal Constructional Steel Research, 60:965-987, 2004.
8 H.S. Stern. “Neural networks in applied statistics”. Proceedings of the statistical computing section. American Statistical Association, pp.150-154, 1991.
9 K.B. DeTienne, D. H., DeTienne, L. W., Lewis. “Artificial neural networks for the management researchers: the state of the art”. Research Report, Department of Organizational Leadership and Strategy, Brigham Young University, 2003.
10 W.C. Carpenter, M.E. Hoffman. “Training Back-Propagation Neural Networks”. AI Expert,10:30-33, 1995.
11 Lapeds, R. Forber. “How Neural Network Works”. Neural Information Processing Systems,American Institute of Physics, pp.442-456, 1988.
12 R. Hecht-Nielsen. “Theory of Backpropagation Neural Network”. Proc. Int. Joint Conf. on Neural Networks, IEEE, Washington, D.C., 1:593-605, 1989.
13 Y. Ouwa, M. Watanabe, H. Asawo. “Flow visualisation of a two-dimensional water jet in a rectangular channel”. Japanese J. of Applied Physics, 20:243-247, 1981.
14 D. E. Rumelhart, J. L. McClelland. In: Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Foundations, MIT Press, Cambridge, Massachusetts, (1986).
15 P.K. Simpson. “Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations”, Pergamon Press, first edition, Elmsford, New York, (1990).
Mr. Lyes Khezzar
- United Arab Emirates
Mr. Saleh M. Al-Alawi
- Oman