Improved heat transfer correlation in the transition region for a circular tube with three inlet configurations using artificial neural networks

Heat Transfer Engineering

Volumn 25, Issue 2, 2004, Pages 30-40

  Ghajar, Afshin J ^a   Tam, Lap Mou ^b   Tam, Sik Chung ^b  

^a Oklahoma State University   (United States)

^b UNIVERSITY OF MACAU   (Macao)

Author keywords

[No Author keywords available]

Indexed keywords

HEAT EXCHANGERS; HEAT FLUX; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PRANDTL NUMBER; REYNOLDS NUMBER; TUBES (COMPONENTS);

GRASHOF NUMBERS; HEAT TRANSFER TRANSITION REGION; HORIZONTAL CIRCULAR STRAIGHT TUBE;

HEAT TRANSFER COEFFICIENTS;

EID: 1542336970     PISSN: 01457632     EISSN: 15210537     Source Type: Journal    
DOI: 10.1080/01457630490276097     Document Type: Article

References (26)

1. Shah, R. K., and London, A. L., 1978. Laminar Flow Forced Convection in Ducts, a Supplement to Advances in Heat Transfer New York Academic
2. Shah, R. K., and Johnson, R. S., 1981. “ Correlations for Fully Developed Turbulent Flow Through Circular and Noncircular Channels ”. In Sixth National Heat and Mass Transfer Conf, D75–D95. Madras, India:Indian Institute of Technology.
3. Kakac, S., Shah, R. K., and Bergles, A. E., 1981. Low Reynolds Number Flow Heat Exchanger Hemisphere, New York
4. Kakac, S., Shah, R. K., and Aung, W., 1987. Handbook of Single-Phase Convective Heat Transfer, New York:Wiley.
5. Ghajar, A. J., and Tam, L. M., 1994. Heat Transfer Measurements and Correlations in the Transition Region for a Circular Tube with Three Different Inlet Configurations. Experimental Thermal and Fluid Science, vol. 8 (no. 1):pp. 79–90.
6. Thibault, J., and Grandjean, B. P. A., 1991. A Neural Network Methodology for Heat Transfer Data Analysis. Int. J. Heat Mass Transfer, vol. 34 (no. 8):pp. 2063–2070.
7. Jambunathan, K., Hartle, S. L., Ashforth-Frost, S., and Fontama, V. N., 1996. Evaluating Convective Heat Transfer Coefficients Using Neural Networks. Int. J. Heat Mass Transfer., vol. 39 (no. 11):pp. 2329–2332.
8. Sen, M., and Yang, K. T., 1999. “ Applications of Artificial Neural Networks and Genetic Algorithms in Thermal Engineering ”. In CRC Handbook of Thermal Engineering Edited by:Kreith, F., 620–661. Boca Raton, FL Section 4.24
9. Diaz, G., Sen, M., Yang, K. T., and McClain, R. L., 1999. Simulation of Heat Exchanger Performance by Artificial Neural Networks. HVAC&R Research, vol. 5 (no. 3):pp. 195–208.
10. Pacheco-Vega, A., Sen, M., Yang, K. T., and McClain, R. L., 2001. Neural Network Analysis of Fin-Tube Refrigerating Heat Exchanger with Limited Experimental Data. Int. J. of Heat and Mass Transfer, vol. 44:pp. 763–770.
11. Pacheco-Vega, A., Diaz, G., Sen, M., Yang, K. T., and McClain, R. L., 2001. Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks. ASME Trans., vol. 123:pp. 348–354.
12. Diaz, G., Sen, M., Yang, K. T., and McClain, R. L., 2001. Adaptive Neurocontrol of Heat Exchanger. ASME Trans., vol. 123:pp. 556–562.
13. Sieder, E. N., and Tate, G. E., 1936. Heat Transfer and Pressure Drop in Liquids in Tubes. Ind. Eng. Chem., vol. 28:pp. 1429–1435.
14. Churchill, S. W., 1977. Comprehensive Correlating Equations for Heat, Mass, and Momentum Transfer in Fully Developed Flow in Smooth Tubes. Ind. Eng. Chem. Fundam., vol. 16 (no. 1):pp. 109–116.
15. Hornik, K., 1991. Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks, vol. 4 (no. 2):pp. 251–257.
16. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986. “ Learning Internal Representations by Error Propagation ”. In Parallel Distributed Processing:Explorations in the Microstructure of Cognition, Edited by:Rumelhart, D. E., and McClelland, J. L., 318–362. Cambridge, Mass.:MIT Press.
17. Jacobs, R. A., 1988. Increased Rates of Convergence through Learning Rate Adaptation. Neural Networks, vol. 1 (no. 4):pp. 295–308.
18. Vogl, T. P., Mangis, J. K., Zigler, A. K., Zink, W. T., and Alkon, D. L., 1988. Accelerating the Convergence of the Backpropagation Method. Biological Cybernetics, vol. 59:pp. 256–264.
19. Barnard, E., 1992. Optimization for Training Neural Nets. IEEE Transactions on Neural Networks, vol. 3 (no. 2):pp. 232–240.
20. Batti, R., 1992. First and Second Order Methods for Learning:Between Steepest Descent and Newton's Method. Neural Computation, vol. 4 (no. 2):141–166.
21. Charalambous, C., 1992. Conjugate Gradient Algorithm for Efficient Training of Artificial Neural Networks, Circuits, Devices and Systems. IEEE Proceedings, vol. 139 (no. 3) June:301–310.
22. D'Heygere, T., Goethals, P. L. M., and De Pauw, N., 2002. Use of Genetic Algorithms to Select Input Variables in Artificial Neural Network Models for the Prediction of Benthic Macroinvertebrates. Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society, vol. 2 June:pp. 136–141.
23. Jack, L. B., and Nandi, A. K., April 1999. “ Feature Selection for ANNs Using Genetic Algorithms in Condition Monitoring ”. In ESANN 1999 Proceedings of the 7th European Symposium on Artificial Neural Networks April, 313–318. Bruges, Belgium
24. Cantú-Paz, E., and Kamath, C., 2003. Evolving Neural Networks to Identify Bent-double Galaxies in the FIRST Survey. Neural Networks, vol. 16:pp. 507–517.
25. Guo, Z., and Uhrig, R. E., June 1992. “ Using Genetic Algorithms to Select Inputs for Neural Networks ”. In Proceedings of COGANN-92, Edited by:Whitley, D., and Schaffer, J. D., June, 223–234. Baltimore, MD:IEEE, Computer Society Press.
26. Yao, X., Sep 1999. Evolving Artificial Neural Networks. Proceedings of the IEEE, vol. 87 (no. 9):pp. 1423–1447.