Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted Multiobjective Evolutionary Algorithm

Siwadol Kanyakam; Sujin Bureerat

doi:10.4028/www.scientific.net/AMR.308-310.1122

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 308-310Optimal Design of a Pin-Fin Heat Sink Using a...

Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted Multiobjective Evolutionary Algorithm

Abstract:

In this work, performance enhancement of a multiobjective evolutionary algorithm (MOEA) by integrating a surrogate model to the design process is presented. The MOEA used in this work is multiobjective population-based incremental learning (PBIL). The bi-objective design problem of a pin-fin heat sink (PFHS) is posed to minimize junction temperature and fan pumping power while meeting design constraints. A Kriging (KRG) model is used for improving the performance of PBIL. The training points for constructing a surrogate KRG model are sampled by means of a Latin hypercube sampling (LHS) technique. It is shown that hybridization of PBIL and KRG can enhance the search performance of PBIL.

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Periodical:

Advanced Materials Research (Volumes 308-310)

Pages:

1122-1128

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.308-310.1122

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Online since:

August 2011

Authors:

Siwadol Kanyakam, Sujin Bureerat

Keywords:

Computational Fluid Dynamics (CFD), Kriging Model, Multiobjective Evolutionary Algorithm, Pin-Fin Heat Sink, Population-Based Incremental Learning, Surrogate Model

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