Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

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

Open Access
See all formats and pricing
In This Section

Optimization of Pin Fin Heat Sink by Application of CFD Simulations and Doe Methodology with Neural Network Approximation

K. Kasza
  • ABB Corporate Research ul. Starowiślna 13a, 31-038 Kraków, POLAND
  • Email:
/ Ł. Malinowski
  • ABB Corporate Research ul. Starowiślna 13a, 31-038 Kraków, POLAND
/ I. Królikowski
  • AGH University of Science and Technology ul. Mickiewicza 30, 30-590 Kraków, POLAND
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.

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

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

  • 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.

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

  • 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.

  • 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.

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

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

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

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

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

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

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

  • 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.

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

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

  • 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.

  • 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.

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

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. Export Citation

This content is open access.

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Xiangjie Chen, Yuehong Su, David Reay, and Saffa Riffat
Renewable and Sustainable Energy Reviews, 2016, Volume 60, Page 1367

Comments (0)

Please log in or register to comment.
Log in