| Research Title |
Shape optimization of multiple cross-sections pin fin heat sinks using Surrogate
modeling |
| Date of Distribution |
29 July 2019 |
| Conference |
| Title of the Conference |
The 11th International Conference on Science, Technology and Innovation for Sustainable Well-Being (STISWB XI) |
| Organiser |
Faculty of Engineering, Rajamangala University of Technology Isan Khon Kaen Campus and Universiti Teknologi Malaysia |
| Conference Place |
Universiti Teknologi Malaysia: UTM Malaysia - Singapore |
| Province/State |
Johor Bahru, Malaysia |
| Conference Date |
29 July 2019 |
| To |
1 August 2019 |
| Proceeding Paper |
| Volume |
- |
| Issue |
- |
| Page |
880-898 |
| Editors/edition/publisher |
|
| Abstract |
This paper presents the comparative performance of several surrogate-assisted
multiobjective Evolutionary algorithms (MOEAs) for geometrical design of multiple crosssections pin fin heat sinks (MCSPFHS). The surrogate-assisted MOEAs are achieved by
integrating multiobjective population-based Incremental learning PBIL with a quadratic response
surface model (QRS), a radial-basis Function RBF interpolation technique, and a Kriging (KRG)
or Gaussian process model. The mixed integer/continuous multiobjective design problem of
PFHS with the objectives to minimize junction temperature and fan pumping power
simultaneously is posed. The optimum results obtained from using the original multiobjective
PBIL and the three versions of hybrid PBIL are compared. It is shown that the hybrid PBIL
using KRG is the best performer. The hybrid PBILs require significantly less number of function
evaluations to surpass the original PBIL. |
| Author |
|
| Peer Review Status |
มีผู้ประเมินอิสระ |
| Level of Conference |
นานาชาติ |
| Type of Proceeding |
Full paper |
| Type of Presentation |
Oral |
| Part of thesis |
true |
| Presentation awarding |
false |
| Attach file |
|
| Citation |
0
|
|
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Publication
Journal Publication
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