2008
DOI: 10.1016/j.applthermaleng.2007.12.001
|View full text |Cite
|
Sign up to set email alerts
|

Optimization of a microchannel heat sink with temperature dependent fluid properties

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

1
39
0

Year Published

2009
2009
2021
2021

Publication Types

Select...
6
4

Relationship

0
10

Authors

Journals

citations
Cited by 92 publications
(40 citation statements)
references
References 20 publications
1
39
0
Order By: Relevance
“…Response surface method (RSM), a robust technique to reduce the computational cost for optimization [47][48][49][50], was used to achieve the optimal design. The overall optimization was performed in two steps.…”
Section: Optimization Methodologymentioning
confidence: 99%
“…Response surface method (RSM), a robust technique to reduce the computational cost for optimization [47][48][49][50], was used to achieve the optimal design. The overall optimization was performed in two steps.…”
Section: Optimization Methodologymentioning
confidence: 99%
“…When the heat flux reaches up to 100 W cm À2 , the conventional cooling technology is failed to meet the requirement of thermal removal as expected. Fortunately, the microchannel heat sink (MCHS) proposed by Tuckerman and Pease [1] in 1981 can endure a heat flux as high as 790 W cm À2 , which becomes a significant way for thermal management of microelectronic devices and attracts a great deal of attention [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”
Section: Introductionmentioning
confidence: 99%
“…GA has been shown to be able to determine the optimized performance of a rectangular MCHS with textbook data as well experimental data [13]. Past optimization of the MCHS have been based on the experimental or/and numerical methods approach [14][15][16][17][18]. Unlike the limited optimization outcomes with experiments and numerical simulation with discrete variation of the parameter to be optimized under specific conditions, GA is particularly useful as a fast optimization tool in the exploration of the performance of potential coolants with acceptable patterns and trends when compared to the limited available data.…”
Section: Introductionmentioning
confidence: 99%