Application of thermal resistance network model in optimization design of micro‐channel cooling heat sink

Shaoi, Baodong; Wang, Lifeng; Li, Jianyun; Sun, Zhihui

doi:10.1108/09615530910938425

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…The genetic algorithm is a global search optimization tool which is similar to the Darwinian natural selection process to obtain the optimal solution. It has been used to determine the optimized performance of micro-channel heat sinks (Badenhorst, 2019;Jeevan et al, 2005;Khan et al, 2013;Lee et al, 2007;Luo et al, 2019;Shao et al, 2007Shao et al, , 2009Shao et al, , 2011 and proved to be a fast optimization tool in the exploration of the performance of potential coolants with limited available data (Ghazali-Mohd et al, 2015). Thus, the genetic algorithm was utilized to optimize the design of heat sink in this study.…”

Section: Optimization Using Genetic Algorithmmentioning

confidence: 99%

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

Xiang

Fan

Liu

et al. 2019

HFF

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Purpose The purpose of this paper is to compare the thermal resistances between optimized gallium- and water-based heat sinks to show which one is superior. Design/methodology/approach Taking the thermal resistances of heat sinks as the goal function, an optimization process is programmed based on the genetic algorithm. The optimal channel/fin widths and the corresponding thermal resistances of gallium- and water-based heat sinks are obtained and compared with/without a laminar flow constraint. The analytic model and CFD method are applied in different situations to ensure sufficient accuracy. Findings The results show that in the laminar regime, the thermal resistance of optimized gallium-based heat sink is lower than the water-based counterpart in most cases, but the latter becomes better if it is long enough or the channel is sufficient high. Without the laminar constraint, the thermal resistance of the optimized gallium-based heat sink can be decreased by 33-45 per cent compared with the water-based counterparts. It is interesting to find that when the heat sink is long or the channel height is short, the optimal geometry of gallium-based heat sink is a mini gap. Originality/value This paper demonstrates that the cooling performance of gallium-based heat sink can be significantly improved by optimization without the laminar flow constraint.

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Section: Optimization Using Genetic Algorithmmentioning

confidence: 99%

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

Xiang

Fan

Liu

et al. 2019

HFF

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“…The outcomes of the models were often based on and compared with the landmark work of Tuckerman and Pease 5) , shown in Table 2. As the number of fins was found to affect the thermal resistance both in modelling and experimental studies 32) , Lei et al 33) also proposed a wall model for the single stack followed by Shao et al 34) for a double-stack MCHS. It was found that the wall model is a better representation of the multi-stack MCHS.…”

Section: Introductionmentioning

confidence: 99%

Effect of Wall Resistance on the Total Thermal Resistance of a Stacked Microchannel Heat Sink

Shamsuddin¹,

Abidin²,

Abd-Zaidan³

et al. 2021

Evergreen

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This paper reports on the different modeling approach of the total thermal resistance in a microchannel heat sink (MCHS); with wall resistance and the frequently used fin model, in comparison with experimental results. For a single stack MCHS, the wall model caused more than 10% difference but it can be extended to a stacked MCHS while the fin model could not, due to the adiabatic top condition. The wall resistance model is idealized, assuming a 100% efficient convective heat transfer while in the fin model 70% was the maximum. Meanwhile, stacking showed that at a constant flow rate, the thermal resistance could be reduced by 3% for a double stack, while increasing beyond that will decrease the thermal performance of the MCHS. The study showed the limits of models used and possible stacking of a MCHS for improved heat removal capability.

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“…A study performed a computed tomography (CT) scan to examine a prototype polymer-based EMHX such as this, and it indicates significant flow maldistribution due to channel height variations [19]. There have been significant improvements in the modeling of micro-channel HX, which can be used to drive the development of EMHX [4,[20][21][22][23][24][25][26][27][28][29][30][31]. Recently, a paper [32] developed a finite difference model that can be applied to this EMHX that was scanned, allowing for a comparison between modeled and experimental heat transfer characteristics when maldistribution is present.…”

Section: Introductionmentioning

confidence: 99%

Expanded Microchannel Heat Exchanger: Finite Difference Modeling

Denkenberger

Pearce

Brandemuehl

et al. 2021

Designs

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A finite difference model of a heat exchanger (HX) considered maldistribution, axial conduction, heat leak, and the edge effect, all of which are needed to model a high effectiveness HX. An HX prototype was developed, and channel height data were obtained using a computerized tomography (CT) scan from previous work along with experimental results. This study used the core geometry data to model results with the finite difference model, and compared the modeled and experimental results to help improve the expanded microchannel HX (EMHX) prototype design. The root mean square (RMS) error was 3.8%. Manifold geometries were not put into the model because the data were not available, so impacts of the manifold were investigated by varying the temperature conditions at the inlet and exit of the core. Previous studies have not considered the influence of heat transfer in the manifold on the HX effectiveness when maldistribution is present. With no flow maldistribution, manifold heat transfer increases overall effectiveness roughly as would be expected by the greater heat transfer area in the manifolds. Manifold heat transfer coupled with flow maldistribution for the prototype, however, causes a decrease in the effectiveness at high flow rate, and an increase in effectiveness at low flow rate.

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Application of thermal resistance network model in optimization design of micro‐channel cooling heat sink

Cited by 14 publications

References 16 publications

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

Effect of Wall Resistance on the Total Thermal Resistance of a Stacked Microchannel Heat Sink

Expanded Microchannel Heat Exchanger: Finite Difference Modeling

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