Proceedings of Symposium on Energy Engineering in the 21st Century (SEE2000) Volume I-IV

ISBN Print: 1-56700-132-7

COEFFICIENT OF HEAT TRANSFER IN FIN-TUBE HEAT EXCHANGER ANALYSIS AND THE ARTIFICIAL NEURAL NETWORKS

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DOI: 10.1615/SEE2000.1160
pages 940-948

Kwang-tzu Yang Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556

Mihir Sen Department of Aerospace and Mechanical Engineering University of Notre Dame, Notre Dame, Indiana 46556, U.S.A.

Gerardo Diaz Department of Mechanical Engineering, University of California, Merced, CA 95343, U.S.A.

Arturo Pacheco-Vega Hydronics Laboratory, Department of Aerospace and Mechanical Engineering University of Notre Dame, Notre Dame, IN 46556, USA; Department of Mechanical Engineering ECST A-410; California State University–Los Angeles, Los Angeles, CA, 90032, USA

Rodney L. McClain Hydronics Laboratory, Department of Aerospace and Mechanical Engineering University of Notre Dame, Notre Dame, IN 46556, USA

Abstract

The use of correlated coefficients of heat transfer from the experimental data has been the universal choice to analyze complex heat transfer processes. Unfortunately, large errors often occur in the application of such coefficients to complex phenomena such as those occurring in compact fin-tube heat exchangers. Basic issues responsible for this difficulty are identified and articulated. An alternative approach of correlating the experimental heat transfer data by the application of artificial neural network (ANN) is presented, and this new approach is shown to possess several inherent advantages not enjoyed by the conventional approach of using correlated heat transfer coefficients, as demonstrated by examples dealing with two fin-tube heat exchangers with different complexities. Also mentioned are further extensions of the ANN approach to the dynamic modeling of heat exchangers and their control.

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