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Four-Objective Optimizations of a Single Resonance Energy Selective Electron Refrigerator

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 ([Formula: see text]), coefficient...

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Detalles Bibliográficos
Autores principales: He, Jinhu, Chen, Lingen, Ge, Yanlin, Shi, Shuangshuang, Li, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601456/
https://www.ncbi.nlm.nih.gov/pubmed/37420465
http://dx.doi.org/10.3390/e24101445
Descripción
Sumario: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 ([Formula: see text]), coefficient of performance ([Formula: see text]), ecological function ([Formula: see text]), and figure of merit ([Formula: see text]) of the ESER are taken as objective functions. Energy boundary [Formula: see text] and resonance width [Formula: see text] 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 [Formula: see text] and [Formula: see text] 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 ([Formula: see text]), while the deviation indices are 0.1085, 0.8455, 0.1865, and 0.1780 for four single-objective optimizations of maximum [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] , 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 [Formula: see text] and [Formula: see text] range mainly from 12 to 13, and 1.5 to 2.5, respectively, for the four-objective optimization.