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Research Papers: Experimental Techniques

Inverse Estimation for Heat Flux Distribution at the Metal-Mold Interface Using Fuzzy Inference

[+] Author and Article Information
Lina Zhu, Hong Chen, Zhaoming Luo

School of Power Engineering, Chongqing University, Chongqing 400044, PR China

Guangjun Wang1

School of Power Engineering, Chongqing University, Chongqing 400044, PR Chinawangguangjun@cqu.edu.cn

1

Corresponding author.

J. Heat Transfer 133(8), 081602 (May 03, 2011) (6 pages) doi:10.1115/1.4003743 History: Received October 05, 2010; Revised February 25, 2011; Published May 03, 2011; Online May 03, 2011

This study provides a new inverse approach based on fuzzy inference for solving the problem of estimating heat flux distribution at the metal-mold interface in the continuous casting process. Measured temperatures acquired with the thermocouples buried in the mold are used to obtain corresponding inference results with the fuzzy inference. Then according to the importance of measured information for estimating the heat flux distribution, inference results are weighted to realize estimation of heat flux distribution at the metal-mold interface. Some numerical tests are presented to discuss the validity of the present approach by using different initial guesses of heat flux distribution, the number of measuring points, and measurement errors. In comparison with the conjugate gradient method, it is concluded that the method based on fuzzy inference is of a good anti-ill-posed characteristic.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic diagram of round continuous casting mold

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Figure 2

Membership of fuzzy sets Al

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Figure 3

Membership of fuzzy sets Bl

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Figure 4

Temperature distribution on the inner wall and the circumference located measuring point

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Figure 5

Inversion results by fuzzy inference with different initial guesses

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Figure 6

Inversion results by CGM with different initial guesses

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Figure 7

Inversion results by fuzzy inference with different number of measuring points

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Figure 8

Inversion results by CGM with different number of measuring points

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Figure 9

Inversion results by fuzzy inference with different measurement errors

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Figure 10

Inversion results by CGM with different measurement errors

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