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An Intelligent System for the Heatsink Design

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Via two-stage back-propagation neural network (BNN) learning algorithm, this paper establishes the relationship between different heatsink design parameters and performance evaluation, and induces 5 corresponding performance outputs from 6 different heatsink design and operating condition parameters (inlet airflow velocity, heatsink length or width, fin thickness, fin gap, fin height and heatsink base height) by using Computation Fluid Dynamics (CFD). After two stages well-trained, the BNN model with error compensator is able to accurate estimate the output values under different heatsink design and operation conditions.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Northern Taiwan Institute of Science and Technology, 2, Xue Yuan Rd., Peitou, Taipei, 112, Taiwan, ROC

    Yao-Wen Hsueh, Hsin-Chung Lien & Ming-Hsien Hsueh

Authors
  1. Yao-Wen Hsueh
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  2. Hsin-Chung Lien
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  3. Ming-Hsien Hsueh
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Hsueh, YW., Lien, HC., Hsueh, MH. (2006). An Intelligent System for the Heatsink Design. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_120

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  • DOI: https://doi.org/10.1007/11760191_120

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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