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Research Papers: Forced Convection

Solution of Inverse Convection Heat Transfer Problem Using an Enhanced Particle Swarm Optimization Algorithm

[+] Author and Article Information
Peng Ding

 College of Storage & Transportation and Architectural Engineering, China University of Petroleum (Hua Dong), QingDao, ShanDong 266555, Chinaztdep@yahoo.com.cn

J. Heat Transfer 134(1), 011702 (Nov 29, 2011) (10 pages) doi:10.1115/1.4004854 History: Received January 23, 2011; Revised July 29, 2011; Published November 29, 2011; Online November 29, 2011

The main objective of this paper is to solve the inverse convection heat transfer problems with particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortcoming of earlier convergence of standard PSO algorithms. The performance of EPSO is tested by some benchmark functions; it is shown that EPSO has a strong antilocal trap capability especially for high dimensional multimodal optimization problems. At last, EPSO is used to identify the unknown boundary heat flux in a channel flow. According to the computational results of four test problems, it is clear that the proposed EPSO algorithm is able to estimate the unknown heat flux accurately even when the input data contain measurement error.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic view of the channel flow system

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Figure 2

Various shapes of heat flux functions q(x) used to examine the performance of the inverse problem solvers. (a), (b), (c), and (d) correspond to examples 1, 2, 3, and 4, respectively.

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Figure 3

Inverse estimation of unknown heat flux for example 1. (a) Best fitness, (b) estimated heat flux after 100 iterations, (c) estimated heat flux after 300 iterations, (d) estimated heat flux after 500 iterations.

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Figure 4

Inverse estimation of unknown heat flux for example 2. (a) Best fitness, (b) estimated heat flux after 300 iterations.

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Figure 5

Inverse estimation of unknown heat flux for example 3. (a) Best fitness, (b) estimated heat flux after 10 iterations, (c) estimated heat flux after 100 iterations, (d) estimated heat flux after 300 iterations.

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Figure 6

Inverse estimation of unknown heat flux for example 4

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Figure 7

Inverse estimation of unknown heat flux for example 1 with a measurement error of 1%. (a) Best fitness, (b) evolution of relative error, (c) estimated heat flux after 50 iterations, (d) estimated heat flux after 200 iterations.

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Figure 8

Inverse estimation of unknown heat flux for example 2 with a measurement error of 1%. (a) Estimated heat flux after 50 iterations, (b) estimated heat flux after 200 iterations.

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Figure 9

Inverse estimation of unknown heat flux for example 3 with different measurement error. (a) ξ=1%, (b) ξ=3%.

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Figure 10

Inverse estimation of unknown heat flux for example 4 with different measurement error. (a) ξ=1%, (b) ξ=3%.

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