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Research Papers

Integration of Genetic Programing With Genetic Algorithm for Correlating Heat Transfer Problems

[+] Author and Article Information
Yan Liu, Jian Yang, Zhi-long Cheng

Key Laboratory of Thermal-Fluid Science
and Engineering,
Ministry of Education,
Xi'an Jiaotong University,
Xi'an 710049, Shaanxi, China

Jing Xu

Suzhou Nuclear Power Research Institute (SNPI),
Suzhou 215000, Jiangsu, China

Qiu-wang Wang

Key Laboratory of Thermal-Fluid Science
and Engineering,
Ministry of Education,
Xi'an Jiaotong University,
Xi'an 710049, Shaanxi, China
e-mail: wangqw@mail.xjtu.edu.cn

1Corresponding author.

Manuscript received July 6, 2014; final manuscript received December 22, 2014; published online March 17, 2015. Assoc. Editor: Giulio Lorenzini.

J. Heat Transfer 137(6), 061012 (Jun 01, 2015) (8 pages) Paper No: HT-14-1443; doi: 10.1115/1.4029871 History: Received July 06, 2014; Revised December 22, 2014; Online March 17, 2015

In the present paper, the genetic programing (GP) is integrated with the genetic algorithm (GA) for deriving heat transfer correlations. In the process of developing heat transfer correlations with the approach (GP with GA (GPA)), the GP is first employed to obtain some potential optimal forms. After that, the forms are further optimized with the global GA to reach minimum errors between the predicted values and experimental values. With the proposed approach, three typical different heat transfer problems are applied to the data reduction processes from published experimental data, which are heat transfer in a shell-and-tube heat exchanger (STHE) with continuous helical baffles, a single row heat exchanger with helically finned tubes and a finned oval-tube heat exchanger with double rows of tubes, respectively. The results indicate that the GPA approach could improve the performance of heat transfer correlations obtained with the GP. Compared with the power-law-based correlations, the heat transfer correlations obtained with the approach have higher predicted accuracies and more excellent robustness.

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Figures

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Fig. 1

Flow chart of approach integrating GPA

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Fig. 2

Flow chart of GA from Refs. [6] and [16]

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Fig. 3

A STHE with continuous helical baffles: (a) sketch of the tube bundle, (b) side view of tube bundle, and (c) shell configurations [22]

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Fig. 4

Experimental values from Ref. [22] versus GPA-based correlation predicted Nu of the STHE with continuous helical baffles

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Fig. 5

Experimental values from Ref. [22] and GPA-based correlation predicted values of Nu plotted versus different experimental points

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Fig. 6

A single row heat exchanger with helically finned tubes: (a) side view and (b) cross-sectional view [25]

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Fig. 7

Experimental values of f plotted versus Rec from Ref. [25]

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Fig. 8

Experimental values from Ref. [25] versus GPA-based correlation predicted f of the single row heat exchanger with helically finned tubes

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Fig. 9

Experimental values from Ref. [25] and GPA-based correlation predicted values of f plotted versus Rec

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Fig. 10

Schematic view of the finned oval-tube heat exchanger with double rows of tubes [28,29]

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Fig. 11

Experimental values of k plotted versus va from Refs. [28] and [29]

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Fig. 12

Experimental values from Refs. [28] and [29] versus GPA-based correlation predicted k of the finned oval-tube heat exchanger with double rows of tubes

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Fig. 13

Experimental values from Refs. [28] and [29] and GPA-based correlation predicted values of k plotted versus va

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