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International Journal of Applied Mechanics and Engineering

The Journal of University of Zielona Góra

Editor-in-Chief: Walicki, Edward

4 Issues per year


CiteScore 2016: 0.12

SCImago Journal Rank (SJR) 2016: 0.127
Source Normalized Impact per Paper (SNIP) 2016: 0.063

Open Access
Online
ISSN
2353-9003
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Optimization of Pin Fin Heat Sink by Application of CFD Simulations and Doe Methodology with Neural Network Approximation

K. Kasza / Ł. Malinowski / I. Królikowski
Published Online: 2013-06-08 | DOI: https://doi.org/10.2478/ijame-2013-0022

A design optimization of a staggered pin fin heat sink made of a thermally conductive polymer is presented. The influence of several design parameters like the pin fin height, the diameter, or the number of pins on thermal efficiency of the natural convection heat sink is studied. A limited number of representative heat sink designs were selected by application of the design of experiments (DOE) methodology and their thermal efficiency was evaluated by application of the antecedently validated and verified numerical model. The obtained results were utilized for the development of a response surface and a typical polynomial model was replaced with a neural network approximation. The particle swarm optimization (PSO) algorithm was applied for the neural network training providing very accurate characterization of the heat sink type under consideration. The quasi-complete search of defined solution domain was then performed and the different heat sink designs were compared by means of thermal performance metrics, i.e., array, space claim and mass based heat transfer coefficients. The computational fluid dynamics (CFD) calculations were repeated for the most effective heat sink designs.

Keywords : heat transfer; design optimization; heat sink; neural network approximation; numerical modeling; thermally conductive polymer

  • Aihara T., Maruyama S. and Kobayakawa S. (1990): Free convective/radiative heat transfer from pin-fin arrays with avertical base plate (general representation of heat transfer performance). - International Journal Heat Mass Transfer, vol.33, No.6, pp.1223-1232.Google Scholar

  • ANSYS. Inc. (2009): ANSYS FLUENT 12.0 Theory Guide.Google Scholar

  • ANSYS. Inc. (2009a): ANSYS FLUENT 12.0 User’s Guide.Google Scholar

  • Bahadur R.and Bar-Cohen A. (2005): Thermal design and optimization of natural convection polymer pin fin heat sink. - IEEE Transactions on Components Packing and Manufacturing Technology, vol.28, No.2, pp.238-246.Google Scholar

  • Beyer W., Liebscher M., Beer M. and Graf W. (2006): Neural Network Based Response Surface Methods - aComparative Study. -LS-DYNA Anwenderforum, Ulm.Google Scholar

  • Chen H-T., Chen P-L, Horng J-T. and Hung Y-H (2005): Design optimization for pin-fin heat sinks. - Journal of Electronic Packaging, vol.127, pp.397-406.Google Scholar

  • Chui E.H. and Raithby G.D. (1993): Computation of Radiant Heat Transfer on a Non-Orthogonal Mesh Using theFinite-Volume Method. - Numerical Heat Transfer, Part B 23:269-288.Google Scholar

  • Coolpolymers Inc. (2012): www.coolpolymers.com - Warwick, RI, USA.Google Scholar

  • Eberhart R.C., Simpson P.K. and Dobbings R.W. (1996): Computational Intelligence PC Tools. - Boston: Academic Press.Google Scholar

  • Fluent Inc. (2002): Icepak 4: Advanced Thermal Modeling Software for Electronic Design. - Lebanon, NH, USA.Google Scholar

  • Gurney K. (1997): An Introduction to Neural Networks. - CRC Press.Google Scholar

  • Hassoun M.H. (1995): Fundamentals of Artificial Neural Networks. - The MIT Press.Google Scholar

  • Jeff Wu C.F. and Hamada. M. (2000): Experiments - Planning. Analysis and Parameter Design Optimization. - John Wiley & Sons.Google Scholar

  • Kennedy J., Eberhart R.C. and Shi Y. (2001): Swarm Intelligence. - Morgan Kaufmann.Google Scholar

  • Lee S. (1995): Optimum Design and Selection of Heat Sinks. - IEEE Transactions on Components Packing and Manufacturing Technology, vol.18, No.4, pp.812-817.Google Scholar

  • Montgomery D.C. (1997): Design and Analysis of Experiments. - 4th edition, John Wiley & Sons.Google Scholar

  • Myers G.N. and Montgomery D.C. (202): Response Surface Methodology, 2nd edition. - John Wiley & Sons.Google Scholar

  • Raithby G.D. and Chui E.H. (1990): A Finite-Volume Method for Predicting a Radiant Heat Transfer in Enclosureswith Participating Media. - J. Heat Transfer, 112:415-423.Google Scholar

  • Spalart P. and Allmaras S. (1992): A one-equation turbulence model for aerodynamic flows. - Technical Report AIAA-92-0439, American Institute of Aeronautics and Astronautics.Google Scholar

  • Tadeusiewicz R. (1993): Sieci Neuronowe (Neural Networks). - Academic Publishing House.Google Scholar

About the article

Published Online: 2013-06-08

Published in Print: 2013-06-01


Citation Information: International Journal of Applied Mechanics and Engineering, ISSN (Print) 1734-4492, DOI: https://doi.org/10.2478/ijame-2013-0022.

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