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TECHNICAL PAPERS: Heat Exchangers

Experimental Study and Genetic-Algorithm-Based Correlation on Shell-Side Heat Transfer and Flow Performance of Three Different Types of Shell-and-Tube Heat Exchangers

[+] Author and Article Information
Qiu-wang Wang

State Key Laboratory of Multiphase Flow in Power Engineering, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, Chinawangqw@mail.xjtu.edu.cn

Gong-nan Xie, Bo-tao Peng, Min Zeng

State Key Laboratory of Multiphase Flow in Power Engineering, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

J. Heat Transfer 129(9), 1277-1285 (Dec 03, 2006) (9 pages) doi:10.1115/1.2739611 History: Received July 08, 2006; Revised December 03, 2006

The heat transfer and pressure drop of three types of shell-and-tube heat exchangers, one with conventional segmental baffles and the other two with continuous helical baffles, were experimentally measured with water flowing in the tube side and oil flowing in the shell side. The genetic algorithm has been used to determine the coefficients of correlations. It is shown that under the identical mass flow, a heat exchanger with continuous helical baffles offers higher heat transfer coefficients and pressure drop than that of a heat exchanger with segmental baffles, while the shell structure of the side-in-side-out model offers better performance than that of the middle-in-middle-out model. The predicted heat transfer rates and friction factors by means of the genetic algorithm provide a closer fit to experimental data than those determined by regression analysis. The predicted corrections of heat transfer and flow performance in the shell sides may be used in engineering applications and comprehensive study. It is recommended that the genetic algorithm can be used to handle more complicated problems and to obtain the optimal correlations.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Experimental loop

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Figure 2

Shell-and-tube heat exchangers

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Figure 3

Flow chart of genetic algorithm and thermal prediction

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Figure 4

Heat transfer performance for three heat exchangers

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Figure 5

Heat transfer coefficient versus flow rate

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Figure 6

Flow friction performance for three heat exchangers

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Figure 7

Pressure drop versus flow rate

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Figure 8

Evolution process of maximizing fitness

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Figure 9

Predicted Nusselt number versus experimental data

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Figure 10

Predicted heat transfer rates versus experimental data of three exchangers

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Figure 11

Predicted friction factors versus experimental data of three exchangers

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