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The main focus of the present study is to utilize the artifi cial neural network (ANN) in predicting the natural convection from horizontal isothermal cylinders arranged in vertical and inclined arrays. The effects of the vertical separation spacing to the cylinder diameter ratio (Py/d), horizontal separation spacing to the cylinder diameter ratio (Px/d) and Rayleigh number (Ra) variation on the average heat transfer from the arrays are considered via this prediction. The training data for optimizing the ANN structure is based on available experimental data. The Levenberg-Marquardt back propagation algorithm is used for ANN training. The proposed ANN is developed using MATLAB functions. For the best ANN structure obtained in this investigation, the mean relative errors of 0.027% and 0.482% were reached for the training and test data, respectively. The results show that the predicted values are very close to the experimental ones.
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Tom
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46--52
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Bibliogr. 27 poz., rys., tab.
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autor
autor
autor
autor
- Kermanshah University of Technology, Department of Chemical Engineering, Kermanshah, Iran, amin.amiri.84@gmail.com
Bibliografia
- 1. Kuehn, T.H. & Goldstein, R.J. (1980).Numerical solution to the Navier-Stokes equations forlaminar natural convection about a horizontal isothermal circular cylinder.Int. J. Heat Mass Transfer. 23(7), 971-979.DOI:10.1016/0017-9310(80)90071-X.
- 2. Wang, P., Kahawita, R. & Nguyen, T.H. (1990).Numerical computation of the natural convection flow about a horizontal cylinder using splines. Num. Heat Transfer. 17(2), 191-215. DOI:10.1080/10407789008944739.
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- 4. Corcione, M. (2005). Correlating equations for free convection heat transfer from horizontal isothermal cylinders set in a vertical array.Int. J. Heat Mass Transfer. 48(17), 3660-3673. DOI:10.1016/j.ijheatmasstransfer.2005.01.010.
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- 6. Tokura, I., Saito, H., Kisinami, K. & Muramoto, K. (1983). An experimental study of free convection heat transfer from a horizontal cylinder in a vertical array set in free space between parallel walls. J. heat Transfer. 105, 102-107.
- 7. Marsters, G.F. (1972).Array of heated horizontal cylinders in natural convection. Int. J. Heat Mass Transfer. 15(5), 921-933. DOI:10.1016/0017-9310(72)90231-1.
- 8. Lieberman, J. & Gebhart, B. (1969).Interaction in natural convection from an array of heated elements, experimental. Int.J. Heat Mass Transfer. 12(11), 1385-1396. DOI: 10.1016/0017- 9310(69)90023-4.
- 9. Rezvantalab, H., Ghazian, O., Yousefi, T. & Ashjaee, M. (2011). Effect of flow diverters on free convection heat transfer from a pair of vertical arrays of isothermal cylinders. Experimental Thermal and Fluid Science. 35(7), 1398-1408. DOI: 10.1016/j.expthermflusci.2011.05.008.
- 10. Ashjaee, M. & Yousefi, T. (2007). Experimental Study of Free Convection Heat Transfer from Horizontal Isothermal Cylinders Arranged in Vertical and Inclined Arrays. J. Heat TransferEngineering.28(5),460-471.DOI: 10.1080/01457630601165822.
- 11. Sozen, A. & Arcaklioglu, E. (2007).Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach. Appl. Therm. Eng. 27(2-3), 481-491.DOI: 10.1016/j.applthermaleng.2006.06.012.
- 12. Deng, S. & Hwang, Y. (2006).Applying neural networks to the solution of forward and inverse heat conduction problems. Int. J. of Heat and Mass Transfer.49(25-26), 4732-4750. DOI: 10.1016/j.ijheatmasstransfer.2006.06.009.
- 13. Zdaniuk, G.J., Chamra, L.M. & Walters, D.K. (2007).Correlating heat transfer and friction in helically-finned tubes using artificial neural networks. Int. J. of Heat and Mass Transfer 50(23- 24), 4713-4723. DOI: 10.1016/j.ijheatmasstransfer.2007.03.043.
- 14. Scalabrin, G. & Piazza, L. (2003).Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks. Int. J. of Heat and Mass Transfer 46(7), 1139-1154.DOI: 10.1016/S0017-9310(02)00382-4.
- 15. Diaz, G., Sen, M., Yang, K.T. & McClain, R.L. (2001). Dynamic prediction and control of heat exchangers using artificial neural networks. Int. J. of Heat and Mass Transfer.44(9), 1671-1679. DOI: 10.1016/S0017-9310(00)00228-3.
- 16. Chen, J., Wang, Kuan-Po. & Liang, M-Tsai. (2005).Predictions of heat transfer coefficients of supercritical carbon dioxide using the overlapped type of local neural network. Int. J. of Heat and Mass Transfer.48(12), 2483-2492. DOI:10.1016/j. ijheatmasstransfer.2004.12.040.
- 17. Hernández, J.A., Romero, R.J., Juárez, D., Escobar, R.F. & Siqueiros, J. (2009). A neural network approach and thermodynamic model of waste energy recovery in a heat transformer in a water purification process. Desalination. 243(1- 3), 273-285. DOI: 10.1016/j.desal.2008.05.015.
- 18. Hauf, W. & Grigull, U. (1970). Optical methods in heat transfer. Advances in Heat Transfer. 6, Academic Press, New York, 133-366.
- 19. Eckert, E.E.R.G. & Goldstein, R.J. (1972). Measurements in Heat Transfer. second edition, McGraw-Hill, New York, 241-293.
- 20. Karami, A., Rezaei, E., Shahhosseni, M. & Aghakhani, M. (2012). Fuzzy logic to predict the heat transfer in an air cooler equipped with different tube inserts. Int. J. of Therm. Sci. 53, 141-147. DOI: 10.1016/j.ijthermalsci.2011.10.016.
- 21. Rezaei, E., Karami, A.,Yousefi, T. & Mahmoudinezhad, S. (2012).Modeling the free convection heat transfer in a partitioned cavity using ANFIS, Int. Communications in Heatand Mass Transfer. 39(3), 470-475.DOI: 10.1016/j.icheatmasstransfer. 2011.12.006.
- 22. Minai, A.A. & Williams R.D. (1990). Acceleration of back propagation through learning rate and momentum adaptation. International joint conference on neural networks; 1, 676-9.
- 23. Neural Computing, (1996). A technology handbook for professional II/ PLUS and neural works explorer,. Pittsburgh: Neural Ware Inc, Technical Publications Group.
- 24. Haykin, S.(1994). Neural networks: a comprehensive foundation, New York: Macmillan College Publishing Company; ISBN 0-02352761-7.
- 25. Hammouda, HB,.Mhiri, M., Gafsi, Z., Besbes, K. (2008). Neural-based models of semiconductor devices for HSPICE Simulation. Am. J. Appl. Sci. 5(4), 385-391.DOI: 10.3844/ ajassp.2008.385.391.
- 26. Shirvany, Y., Hayati, M., Moradian, R. (2008).Numerical solution of the nonlinear Schrodinger equationbyfeedforward neural networks. Communications in Nonlinear Science and Numerical Simulation. 13(10), 2132-2145. DOI: 10.1016/j. cnsns.2007.04.024.
- 27. Gallant, AR. & White, H. (1992).On learning the derivatives of an unknown mapping with multilayer feed forward networks. Elsevier Science.5, 129-38.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS4-0004-0009