Abstract
Based on the established model of the irreversible rectangular cycle in the previous literature, in this paper, finite time thermodynamics theory is applied to analyze the performance characteristics of an irreversible rectangular cycle by firstly taking power density and effective power as the objective functions. Then, four performance indicators of the cycle, that is, the thermal efficiency, dimensionless power output, dimensionless effective power, and dimensionless power density, are optimized with the cycle expansion ratio as the optimization variable by applying the nondominated sorting genetic algorithm II (NSGA-II) and considering four-objective, three-objective, and two-objective optimization combinations. Finally, optimal results are selected through three decision-making methods. The results show that although the efficiency of the irreversible rectangular cycle under the maximum power density point is less than that at the maximum power output point, the cycle under the maximum power density point can acquire a smaller size parameter. The efficiency at the maximum effective power point is always larger than that at the maximum power output point. When multi-objective optimization is performed on dimensionless power output, dimensionless effective power, and dimensionless power density, the deviation index obtained from the technique for order preference by similarity to an ideal solution (TOPSIS) decision-making method is the smallest value, which means the result is the best.
Highlights
IntroductionA series of instructive and practical achievements have been obtained in finite time thermodynamics [1,2,3,4,5,6,7,8], the research objects of which include heat engines [9,10,11,12,13,14,15], refrigerators [16,17], heat pumps [18], chemical cycles [19], and quantum cycles [20,21]
The γ is used as the optimization variable; the Pmax and (Pd), P, η, and Wep are taken as the optimization goals; and the irreversible rectangular cycle (RC) is optimized by using the “gamultiobj” algorithm that comes from the MATLAB software
It can be seen from the figures that the genetic algorithm stops when convergence is attained, and it can be observed that this occurs at 626 and 666 generations for the four‐objective optimization and three‐objective FFiioggpuutrrieem118i8z. .PaPatriaoertneotfoornofnrotiPnert,iceoPrrdrce,osarprnoendsdpiWonngepdto.infogutro‐ofbojeucrt-ivoebojepcttiimviezaotpiotnim. ization
Summary
A series of instructive and practical achievements have been obtained in finite time thermodynamics [1,2,3,4,5,6,7,8], the research objects of which include heat engines [9,10,11,12,13,14,15], refrigerators [16,17], heat pumps [18], chemical cycles [19], and quantum cycles [20,21]. Based on the established irreversible Dual-Miller cycle model, Abedinnezhad et al [51] carried out MOO for the TEF, ecological function, and ecological performance coefficient. Based on the established irreversible Atkinson cycle model with constant SH of WF, Shi et al [38] applied NSGA-II to carry out MOO for the dimensionless POW, dimensionless PD, TEF, and dimensionless ecological function. Tang et al [52] modeled the improved irreversible closed modified Brayton cycle when the heat source temperature was changed; derived the expressions of cycle dimensionless POW, dimensionless PD, TEF, and dimensionless ecological function; and performed MOO on the cycle to obtain the optimal solutions of four-objective, three-objective, and two-objective optimizations. T1, τ, and T0 are given, the temperatures of each state point in the cycle can be calculated, and the cycle POW, TEF, PD, and effective power can be obtained by substituting the calculation results into Equations (11)–(13) and (16)
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