TY - GEN
T1 - Multi-objective optimizations of structural parameter determination for serpentine channel heat sink
AU - Li, Xuekang
AU - Hao, Xiaohong
AU - Chen, Yi
AU - Zhang, Muhao
AU - Peng, Bei
PY - 2013/2/25
Y1 - 2013/2/25
N2 - This paper presents an approach for modeling and optimization of the channel geometry of a serpentine channel heat sink using multi-objective genetic algorithm. A simple thermal resistance network model was developed to investigate the overall thermal performance of the serpentine channel heat sink. Based on a number of simulations, bend loss coefficient correlation for 1000<Re<2200 was obtained which was function of the aspect ratio (a), ratio of fins width to channel width (b). In this study, two objectives minimization of overall thermal resistance and pressure drop are carried out using multi-objective genetic algorithms. The channel width, fin width, channel height and inlet velocity are variables to be optimized subject to constraints of fixed length and width of heat sink. The study indicates that reduction in both thermal resistance and pressure drop can be achieved by optimizing the channel configuration and the inlet velocity.
AB - This paper presents an approach for modeling and optimization of the channel geometry of a serpentine channel heat sink using multi-objective genetic algorithm. A simple thermal resistance network model was developed to investigate the overall thermal performance of the serpentine channel heat sink. Based on a number of simulations, bend loss coefficient correlation for 1000<Re<2200 was obtained which was function of the aspect ratio (a), ratio of fins width to channel width (b). In this study, two objectives minimization of overall thermal resistance and pressure drop are carried out using multi-objective genetic algorithms. The channel width, fin width, channel height and inlet velocity are variables to be optimized subject to constraints of fixed length and width of heat sink. The study indicates that reduction in both thermal resistance and pressure drop can be achieved by optimizing the channel configuration and the inlet velocity.
KW - heat sink
KW - serpentine channel
KW - bend loss coefficient
KW - multi-objective optimizations
UR - http://www.springer.com/gp/book/9783642371912
U2 - 10.1007/978-3-642-37192-9_45
DO - 10.1007/978-3-642-37192-9_45
M3 - Conference contribution
SN - 9783642371912
T3 - Lecture Notes in Computer Science
SP - 449
EP - 458
BT - Applications of Evolutionary Computing
A2 - Esparcia-Alcazar, Anna I.
PB - Springer
ER -