Multi-objective optimization of an irreversible Stirling cryogenic refrigerator cycle

• Published in: Energy conversion and management 2014-06, Vol.82, p.351-360
• Main Authors: Ahmadi, Mohammad H., Ahmadi, Mohammad Ali, Mohammadi, Amir H., Feidt, Michel, Pourkiaei, Seyed Mohsen
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Capacitance; Cooling loads; Decision making; Energy; Energy. Thermal use of fuels; Engineering Sciences; Evolutionary algorithms; Exact sciences and technology; Fuzzy; Heat exchangers; Input power; Multi-objective optimization; Optimization; Optimum performance; Refrigerating engineering; Refrigerating engineering. Cryogenics. Food conservation; Refrigerators; Stirling cryogenic refrigeration cycles; Techniques. Materials
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2014.03.033

•A parametric investigation of irreversible Stirling cryogenic refrigerator cycles is presented.•Both internal and external irreversibilities are included in this study, moreover, heat capacities of external reservoirs are involved.•Multi-objective evolutionary algorithm based on NSGA-II approach is utilized.•Three robust decision making approaches are utilized to determine final optimum solution. The main aim of this research article is a parametric demonstration of irreversible Stirling cryogenic refrigerator cycles that includes irreversibilities such as external and internal irreversibilities. In addition, through this study, finite heat capacities of external reservoirs are considered accordingly. To reach the addressed goal of this research, three objective functions that include the input power of the Stirling refrigerator, the coefficient of performance (COP) and cooling load (RL) have been involved in optimization process simultaneously. The first aforementioned objective function has to minimize; the rest objective functions, on the other hand, have to maximize in parallel optimization process. Developed multi objective evolutionary approaches (MOEAs) based on NSGA-II algorithm is implemented throughout this work. Moreover, cold-side’s effectiveness of the heat exchanger, hot-side’s effectiveness of the heat exchanger, heat source’s heat capacitance rate, heat sink’s capacitance rate, temperature ratio ThTc, temperature of cold side are assigned as decision variables for decision making procedure. To gain a robust decision, different decision making approaches that include TOPSIS, LINMAP and fuzzy Bellman–Zadeh are used. Pareto optimal frontier was determined precisely and then three final outputs have been gained by means of the mentioned decision making approaches.