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Numerical Heat Transfer, Part B: Fundamentals
An International Journal of Computation and Methodology
Volume 77, 2020 - Issue 5
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Original Articles

Modeling of double-layer triangular microchannel heat sink based on thermal resistance network and multivariate structural optimization using firefly algorithm

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Pages 417-428 | Received 28 Aug 2019, Accepted 14 Jan 2020, Published online: 11 Feb 2020
 

Abstract

The micro-channel heat dissipation system has minor specifications and good thermal conductivity per unit, which is the best choice for heat dissipation of micro-chips. By optimizing the cross section of microchannel, the heat exchange efficiency and temperature uniformity can be effectively improved. In this article, a double-layer triangular microchannel heat sink is proposed, which uniquely combines triangular cross section and double-layer structure to obtain a better heat dissipation performance. A new thermal resistance network model is established. At the same time, the model of pressure drop in microchannel heat sink is obtained by use of fluid theory. Taking thermal resistance and pressure drop as optimization objectives, the thermal resistance of double-layer triangular microchannel heat sink is 0.284 K/W and the pressure drop is 1386.89 Pa by using the firefly algorithm based on the Pareto optimal solution set, obtaining the optimal structural parameters. The thermal-flow-solid coupling simulation analysis shows that the thermal resistance and theoretical analysis error is 5.19%, and the pressure drop and theoretical analysis error is 9.49%, which can verify the accuracy of the thermal resistance network model. This article has a guiding significance for the thermal resistance analysis and heat dissipation improvement of non-rectangular cross section microchannel heat sinks.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This project is supported by National Natural Science Foundation of China (Grant No. 51805298), Natural Science Foundation of Shandong Province (Grant No. ZR201807090390), the Program of Science and Technology of Suzhou (Grant No. SYG201734) and Fundamental Research Funds for Central Universities (Grant No. 2019ZRJC006).

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