Research Papers: Heat Exchangers

Experimental Study and Genetic-Algorithm-Based Correlation on Pressure Drop and Heat Transfer Performances of a Cross-Corrugated Primary Surface Heat Exchanger

[+] Author and Article Information
Qiu-Wang Wang1

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

Dong-Jie Zhang, Gong-Nan Xie

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


Corresponding author.

J. Heat Transfer 131(6), 061802 (Mar 31, 2009) (8 pages) doi:10.1115/1.3090716 History: Received May 06, 2008; Revised October 23, 2008; Published March 31, 2009

Heat transfer and pressure drop characteristics of a cross-corrugated (CC) primary surface heat exchanger with different CC passages (P/H=2, θ=60 and 120 deg, called CC2-60 and CC2-120, respectively) in two air sides have been experimentally investigated in this study. It is shown that the corrugation angle (θ) and the ratio of the wavelength P to height H(P/H) are the two key parameters of CC passages to influence the heat transfer and flow friction performances. The heat transfer and friction factor correlations for these two configurations are also obtained with Reynolds numbers ranging from Re=4505500(CC2-60) and Re=5706700(CC2-120). At a certain P/H, the Nusselt number, Nu, and the friction factor, f, are affected by the corrugation angle, θ. The heat transfer performance of CC2-120 are much better than that of CC2-60 while the pressure drop of the former is higher than that of the latter, especially at high Reynolds numbers region. The critical Reynolds numbers at which the flow mode transits from laminar to turbulent in the two different passages are also estimated. Furthermore, in this study a genetic algorithm (GA) has been used to determine the coefficients of heat transfer correlations by separation of total heat transfer coefficient without any information of measured wall temperatures. It is concluded that the GA-based separated heat transfer Nusselt number provides a good agreement with the experimental data; the averaged relative deviation by GA (1.95%) is lower than that by regression analysis (2.84%). The inversely yielding wall temperatures agree well with the measured data in turn supporting the reliability of experimental system and measurements. It is recommended that GA techniques can be used to handle more complicated problems and to obtain both-side heat transfer correlations simultaneously, where the conventional Wilson-plot method cannot be applied.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

Sketch of experimental system

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Figure 2

Exploded view of CCPS heat exchanger

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Figure 3

Sketch of CCPS plate

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Figure 4

f Re versus Re for two corrugation angles

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Figure 5

f versus Re for corrugation angle θ=60 deg

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Figure 6

f versus Re for corrugation angle θ=120 deg

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Figure 7

Evolution process of maximizing fitness

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Figure 8

Plot of separated Nusselt numbers against experimental data. (The variables in x-coordinate refer to the experimental measured data while the variables in y-coordinate stand for genetic algorithm separated data.)

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Figure 9

Comparison of heat transfer rate from genetic algorithm and regression analysis. (The variables in x-coordinate refer to experimental measured heat transfer rate while the variables in y-coordinate stand for those data determined from heat transfer coefficients inserting total heat transfer coefficient equation, Q=kAdΔtm; “GA” means heat transfer coefficients are separated by genetic algorithm and “RA” means heat transfer coefficients are reduced from experimental data directly.)

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Figure 10

Comparison of relative errors from genetic algorithm and regression analysis. (The relative error is produced from the deviation of the GA-separated or RA-obtained heat transfer rate and experimental heat transfer rate.)

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Figure 11

Comparison wall temperature and its relative error

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Figure 12

Nu versus Re for two corrugation angles based on GA method



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