Heat Rate Predictions in Humid Air-Water Heat Exchangers Using Correlations and Neural Networks

[+] Author and Article Information
Arturo Pacheco-Vega, Gerardo Dı́az, Mihir Sen, K. T. Yang, Rodney L. McClain

Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556

J. Heat Transfer 123(2), 348-354 (Oct 03, 2000) (7 pages) doi:10.1115/1.1351167 History: Received January 12, 2000; Revised October 03, 2000
Copyright © 2001 by ASME
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Schematic of a compact fin-tube heat exchanger
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Section of the surface Sjs(a,b,c,d); A is the global minimum; B is a local minimum
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ANN prediction errors versus percentage of data used for training; –×– error Ea using training data; –○– error Eb using data not used for training; –⋄– error E using complete data. Error bars indicate standard deviations. The Ea and E curves have been shifted horizontally by −1 percent and 1 percent, respectively, for clarity.
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A 5-5-3-3 neural network used
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Experimental versus predicted js for heat exchanger with dry surface; + ANN; ◃ McQuiston 16; ○ Gray and Webb 17. Straight line is the perfect prediction.
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Experiments versus predictions for heat exchanger with dropwise condensation; + ANN; ◃ McQuiston 16. Straight line is the perfect prediction: (a) sensible heat js; (b) total heat jt.
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Experiments versus predictions for heat exchanger with film condensation; +ANN; ◃ McQuiston 16. Straight line is the perfect prediction: (a) sensible heat js; (b) total heat jt.




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