A heat dissipation model of a rectangular porous fin is established based on constructal theory. First, the constructal design of rectangular porous fin is conducted by selecting a complex function minimization, which...A heat dissipation model of a rectangular porous fin is established based on constructal theory. First, the constructal design of rectangular porous fin is conducted by selecting a complex function minimization, which composed of linear weighting sum of maximum temperature difference and pumping power consumption, as optimization objective. Effects of gap height, air inlet velocity, total porous fin volume and porosity on the optimal constructs are investigated, respectively. The findings show that the complex function can attain its double minimum at a value of 0.802 when the fin length and number are optimized, and the corresponding optimal fin length and number are 8.01 mm and 10, respectively. In comparison to original design, the complex function and maximum temperature difference after twice optimization are decreased by 19.80% and 66.31%, respectively.Second, the comprehensive performance of porous fin is improved by simultaneously optimizing the fin length and number. The artificial neural network is applied to predict the fin performances, which is used to conduct multi-objective optimization based on NSGA-II algorithm. Optimal structure of porous fin for multiple requirements is gained by LINMAP and TOPSIS decisionmaking strategies. The findings in this study can serve as theoretical guides for fin thermal designs of electronic devices.展开更多
基金supported by the National Natural Science Foundation of China(Grant No. 52171317)Graduate Innovative Fund of Wuhan Institute of Technology(Grant No. CX2022070)。
文摘A heat dissipation model of a rectangular porous fin is established based on constructal theory. First, the constructal design of rectangular porous fin is conducted by selecting a complex function minimization, which composed of linear weighting sum of maximum temperature difference and pumping power consumption, as optimization objective. Effects of gap height, air inlet velocity, total porous fin volume and porosity on the optimal constructs are investigated, respectively. The findings show that the complex function can attain its double minimum at a value of 0.802 when the fin length and number are optimized, and the corresponding optimal fin length and number are 8.01 mm and 10, respectively. In comparison to original design, the complex function and maximum temperature difference after twice optimization are decreased by 19.80% and 66.31%, respectively.Second, the comprehensive performance of porous fin is improved by simultaneously optimizing the fin length and number. The artificial neural network is applied to predict the fin performances, which is used to conduct multi-objective optimization based on NSGA-II algorithm. Optimal structure of porous fin for multiple requirements is gained by LINMAP and TOPSIS decisionmaking strategies. The findings in this study can serve as theoretical guides for fin thermal designs of electronic devices.
