Research Papers: Heat Exchangers

Multi-Objective Optimization of Heat Exchanger Design by Entropy Generation Minimization

[+] Author and Article Information
Jiangfeng Guo, Lin Cheng

Institute of Thermal Science and Technology, Shandong University, Jinan 250061, P. R. China

Mingtian Xu1

Institute of Thermal Science and Technology, Shandong University, Jinan 250061, P. R. Chinamingtian@sdu.edu.cn


Corresponding author.

J. Heat Transfer 132(8), 081801 (Jun 02, 2010) (8 pages) doi:10.1115/1.4001317 History: Received November 06, 2009; Revised February 02, 2010; Published June 02, 2010; Online June 02, 2010

In the present work, a multi-objective optimization of heat exchanger thermal design in the framework of the entropy generation minimization is presented. The objectives are to minimize the dimensionless entropy generation rates related to the heat conduction under finite temperature difference and fluid friction under finite pressure drop. Constraints are specified by the admissible pressure drop and design standards. The genetic algorithm is employed to search the Pareto optimal set of the multi-objective optimization problem. It is found that the solutions in the Pareto optimal set are trade-off between the pumping power and heat exchanger effectiveness. In some sense, the optimal solution in the Pareto optimal set achieves the largest exchanger effectiveness by consuming the least pumping power under the design requirements and standards. In comparison with the single-objective optimization design, the multi-objective optimization design leads to the significant decrease in the pumping power for achieving the same heat exchanger effectiveness and presents more flexibility in the design process.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Diagram of a typical shell-and-tube heat exchanger

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Figure 2

Bejan’s entropy generation number versus the effectiveness

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Figure 3

Flow chart of a genetic algorithm

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Figure 4

Variation in Ns1 with the number of generations

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Figure 5

Variations in Ns1,ΔT and Ns1,ΔP with the number of generations for single-objective optimization

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Figure 6

The Pareto front obtained by multi-objective optimization for a fixed heat transfer area

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Figure 7

The pumping power and effectiveness for a Pareto optimal set

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Figure 8

The relations of Ns1 with Ns1,ΔT and Ns1,ΔP in a Pareto optimal set for fixed heat duty



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