Numerical optimization of design parameters for a modified double-layer microchannel heat sink. (August 2019)

• Record Type: Journal Article
• Authors: Chamoli, Sunil; Lu, Ruixin; Chen, Hao; Cheng, Yongpan; Yu, Peng
• Is Part Of: International journal of heat and mass transfer. Volume 138(2019)
• Page Start: 373
• Page End: 389
• Publication Date: 2019-08
• Subjects: Numerical simulation -- Double-layered microchannel heat sink -- Multi-objective optimization -- Thermal resistance -- Pumping power; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2019.04.032
• Languages: English
• ISSNs: 0017-9310

Abstract

Graphical abstract: Highlights: Modified double layer heat sink performance is better than conventional one. Genetic algorithm is used to predict the optimum design variables. Six Pareto fronts selected imparting low thermal resistance and low pumping power. Abstract: In the present work the multi-objective genetic algorithm is employed to optimize the design variables in a double-layer microchannel heat sink. The design matrix is formulated with Latin hypercube sampling and the model approximation is implemented with response surface approximation to reduce the computing time. The design variables for optimization are the relative axial ridge length ( Xi ) from 0.4 to 1.4, the relative transverse ridge length ( Yi ) from 0.4 to 0.9, the relative channel length ratio ( Li ) from 0.2 to 1, and the relative flow rate ratio ( σ ) from 0.25 to 0.75, respectively. The objective functions are λR (ratio of the thermal resistance of the modified case over base case) and λP (ratio of the total pumping power of the modified case over base case), respectively. The Pareto optimal solutions are extracted and depicted tradeoff between the two objective functions, thus providing a principal understanding into the design variables and allowing autonomy of selection among the optimal solutions. Total 135 Pareto optimal solutions are extracted by using the K-means clustering. Six Pareto optimal combinations (POCs) named as POC – A, POC – B, POC – C, POC – D, POC – E, and POC – F are selected for the comparison of designs. The best design for the heat transfer performance is POC – A ( Xi = 0.4003, Yi = 0.4481, Li = 0.6378, σ = 0.2504) with 37.34% lower λR than that for the POC-F, while the best design for the pumping power is POC – F ( Xi = 0.4149, Yi = 0.4081, Li = 0.6544, σ = 0.2515) with 96.51% lower λP than that for the POC-A.