根据以往文献中建立的具有热泄漏的单共振能量选择性电子制冷机模型,本文利用有限时间热力学理论和NSGA-II算法进行多目标优化。冷负荷(R’’),性能系数(ε),生态功能(ECO’),将ESER的品质因数(χ’)作为目标函数。能量边界(E'/kB)和共振宽度(ΔE/kB)被视为优化变量,并获得了它们的最佳间隔。四元的最优解,三-,bi-,单目标优化是通过用三种TOPSIS方法选择最小偏差指数来获得的,LINMAP,和香农熵;偏差指数的值越小,结果越好。结果表明,E\'/kB和ΔE/kB的值与四个优化目标的值密切相关;选择合适的系统值可以设计出性能最优的系统。使用LINMAP和TOPSIS方法进行四目标优化(ECO并-R并-ε-χ)的偏差指数为0.0812,而最大ECO的四个单目标优化的偏差指数为0.1085、0.8455、0.1865和0.1780,R,ε,和χ,分别。与单目标优化相比,四目标优化可以通过选择适当的决策方法更好地考虑不同的优化目标。E\'/kB和ΔE/kB的最佳值主要分别为12~13和1.5~2.5,用于四目标优化。
According to the established model of a single resonance energy selective electron refrigerator with heat leakage in the previous literature, this paper performs multi-objective optimization with finite-time thermodynamic theory and NSGA-II algorithm. Cooling load (R¯), coefficient of performance (ε), ecological function (ECO¯), and figure of merit (χ¯) of the ESER are taken as objective functions. Energy boundary (E\'/kB) and resonance width (ΔE/kB) are regarded as optimization variables and their optimal intervals are obtained. The optimal solutions of quadru-, tri-, bi-, and single-objective optimizations are obtained by selecting the minimum deviation indices with three approaches of TOPSIS, LINMAP, and Shannon Entropy; the smaller the value of deviation index, the better the result. The results show that values of E\'/kB and ΔE/kB are closely related to the values of the four optimization objectives; selecting the appropriate values of the system can design the system for optimal performance. The deviation indices are 0.0812 with LINMAP and TOPSIS approaches for four-objective optimization (ECO¯-R¯-ε-χ¯), while the deviation indices are 0.1085, 0.8455, 0.1865, and 0.1780 for four single-objective optimizations of maximum ECO¯, R¯, ε, and χ¯, respectively. Compared with single-objective optimization, four-objective optimization can better take different optimization objectives into account by choosing appropriate decision-making approaches. The optimal values of E\'/kB and ΔE/kB range mainly from 12 to 13, and 1.5 to 2.5, respectively, for the four-objective optimization.