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Research Papers: Design and Analysis

Applying Neural Networks to the Solution of the Inverse Heat Conduction Problem in a Gun Barrel

[+] Author and Article Information
Y. Hwang

Department of Weapon System Engineering, Chung Cheng Institute of Technology, National Defense University, No. 190, Sanyuan 1st Street, Dashi Jen, Taoyuan, Taiwan 33509, R.O.Cg960405@ccit.edu.tw

S. Deng

Department of Weapon System Engineering, Chung Cheng Institute of Technology, National Defense University, No. 190, Sanyuan 1st Street, Dashi Jen, Taoyuan, Taiwan 33509, R.O.Csgdeng@ccit.edu.tw

J. Pressure Vessel Technol 130(3), 031203 (Jun 12, 2008) (8 pages) doi:10.1115/1.2937763 History: Received April 18, 2006; Revised January 09, 2007; Published June 12, 2008

The primary cause of gun barrel erosion is the heat generated by the shell as its travels along the barrel. Therefore, calculating the heat flux input to the gun bore is very important when investigating wear problems in the gun barrel and examining its thermomechanical properties. This paper employs the continuous-time analog Hopfield neural network (CHNN) to compute the temperature distribution in various forward heat conduction problems. An efficient technique is then proposed for the solution of inverse heat conduction problems using a three-layered backpropagation neural network (BPN). The weak generalization capacity of BPN networks when applied to the solution of nonlinear function approximations is improved by employing the Bayesian regularization algorithm. The CHNN scheme is used to calculate the temperature in a 155mm gun barrel and the trained BPN is then used to estimate the heat flux of the inner surface of the barrel. The results show that the proposed neural network analysis method successfully solves forward heat conduction problems and is capable of predicting the unknown parameters in inverse problems with an acceptable error.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

1D boundary and initial conditions for hollow cylinder

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Figure 2

Structure of multilayered neural network

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Figure 3

Flow chart of forward and inverse neural network analysis

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Figure 4

Comparison of CHNN and FDM results for temperature with step-ramp heat flux (Case1)

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Figure 5

Comparison of CHNN and FDM results for temperature with triangular-sine heat flux (Case 2)

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Figure 6

Comparison of solutions for temperature profile obtained using FDM and CHNN schemes for sine-sine time-varying heat flux (Case 3)

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Figure 7

Estimated heat flux of one-dimensional IHCPs. Top: Case 1, Middle: Case 2, and Bottom: Case 3.

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Figure 8

Bore temperature (left) and temperature-time curve obtained by Lawton (right)

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Figure 9

Estimated heat flux of a 155mm gun barrel

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