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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4768

Title: A neural network combined with a three-dimensional finite element method applied to optimize eddy current and temperature distributions of traveling wave induction heating system
Authors: Wang, Youhua
Wang, Junhua
Ho, Siu-lau
Pang, Lingling
Fu, Weinong
Subjects: Conducting materials
Eddy currents
Finite element analysis
Induction heating
Iron
Iterative methods
Neural nets
Temperature distribution
Issue Date: 1-Apr-2011
Publisher: American Institute of Physics
Citation: Journal of applied physics, 1 Apr. 2011, v. 109, no. 7, 07E522, p. 1-3.
Abstract: In this paper, neural networks with a finite element method (FEM) were introduced to predict eddy current distributions on the continuously moving thin conducting strips in traveling wave induction heating (TWIH) equipments. A method that combines a neural network with a finite element method (FEM) is proposed to optimize eddy current distributions of TWIH heater. The trained network used for tested examples shows quite good accuracy of the prediction. The results have then been used with reference to a double-side TWIH in order to analyze the distributions of the magnetic field and eddy current intensity, which accelerates the iterative solution process for the nonlinear coupled electromagnetic matters. The FEM computation of temperature converged conspicuously faster using the prediction results as initial values than using the zero values, and the number of iterations is reduced dramatically. Simulation results demonstrate the effectiveness and characteristics of the proposed method.
Description: DOI: 10.1063/1.3560902
Rights: © 2011 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Y. Wang et al., J. Appl. Phys. 109, 07E522 (2011) and may be found at http://link.aip.org/link/?jap/109/07E522.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/4768
ISSN: 0021-8979 (print)
1089-7550 (online)
Appears in Collections:EE Journal/Magazine Articles

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