The purpose of this paper is to show how, with a view to the shortcomings of traditional optimization methods, a multi‐objective optimization concerning the structure sizes of micro‐channel heat sink is performed by adaptive genetic algorithm. The optimized micro‐channel heat sink is simulated by computational fluid dynamics (CFD) method, and the total thermal resistance is calculated to compare with that of thermal resistance network model.
Taking the thermal resistance and the pressure drop as goal functions, a multi‐objective optimization model was proposed for the micro‐channel cooling heat sink based on the thermal resistance network model. The coupled solution of the flow and heat transfer is considered in the optimization process, and the aim of the procedure is to find the geometry most favorable to simultaneously maximize heat transfer while obtaining a minimum pressure drop. The optimized micro‐channel heat sink was numerically simulated by CFD software.
The results of optimization show that the base convection thermal resistance contributes to maximum the total thermal resistance, and base conduction thermal resistance contributes to least. The width of optimized micro‐channel and fin are 197 and 50 μm, respectively, and the corresponding total thermal resistance of the whole micro‐channel heat sink is 0.838 K/W, which agrees well with the analysis result of thermal resistance network model.
The convection heat transfer coefficient is calculated approximately here for convenience, and that may induce some errors.
The maximum difference in temperature of the optimized micro‐channel cooling heat sink is 84.706 K, which may satisfy the requirement for removal of high heat flux in new‐generation chips. The numerical simulation results are also presented, and the results of numerical simulation show that the optimized micro‐channel heat sink can enhance thermal transfer performance.
Baodong, S., Lifeng, W., Jianyun, L. and Heming, C. (2011), "Multi‐objective optimization design of a micro‐channel heat sink using adaptive genetic algorithm", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 21 No. 3, pp. 353-364. https://doi.org/10.1108/09615531111108512Download as .RIS
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