基于现有的不可逆磁流体动力学循环模型,本文利用有限时间热力学理论和多目标遗传算法(NSGA-II),引入换热器热导分布和工质等熵温度比作为优化变量,并获得功率输出,效率,生态功能,和功率密度作为目标函数,以不同的目标函数组合进行多目标优化,并将优化结果与LINMAP的三种决策方法进行对比,TOPSIS,和香农熵.结果表明,在气体速度恒定的条件下,当执行四目标优化时,通过LINMAP和TOPSIS方法获得的偏差指数为0.1764,小于香农熵方法的(0.1940)和最大功率输出的四个单目标优化的(0.3560,0.7693,0.2599,0.1940),效率,生态功能,和功率密度,分别。在马赫数恒定的条件下,进行四目标优化时,通过LINMAP和TOPSIS获得的偏差指数为0.1767,小于香农熵方法的(0.1950)和四个单目标优化的(0.3600,0.7630,0.2637,0.1949),分别。这表明多目标优化结果优于任何单目标优化结果。
Based on the existing model of an irreversible magnetohydrodynamic cycle, this paper uses finite time thermodynamic theory and multi-objective genetic algorithm (NSGA-II), introduces heat exchanger thermal conductance distribution and isentropic temperature ratio of working fluid as optimization variables, and takes power output, efficiency, ecological function, and power density as objective functions to carry out multi-objective optimization with different objective function combinations, and contrast optimization results with three decision-making approaches of LINMAP, TOPSIS, and Shannon Entropy. The results indicate that in the condition of constant gas velocity, deviation indexes are 0.1764 acquired by LINMAP and TOPSIS approaches when four-objective optimization is performed, which is less than that (0.1940) of the Shannon Entropy approach and those (0.3560, 0.7693, 0.2599, 0.1940) for four single-objective optimizations of maximum power output, efficiency, ecological function, and power density, respectively. In the condition of constant Mach number, deviation indexes are 0.1767 acquired by LINMAP and TOPSIS when four-objective optimization is performed, which is less than that (0.1950) of the Shannon Entropy approach and those (0.3600, 0.7630, 0.2637, 0.1949) for four single-objective optimizations, respectively. This indicates that the multi-objective optimization result is preferable to any single-objective optimization result.