建立了以容积换热系数为目标函数,工质流率U0、喷头喷孔直径di、导热油液位高度Z为决策变量的直接接触式换热器性能优化模型,同时进一步将液滴群行为与传热协同关系作为约束条件引入优化模型中,重点分析该约束条件对优化过程及结果的影响。运用遗传算法对原模型和补充模型进行了优化分析,结果表明:原模型优化后的容积换热系数达到了初始值的6.7倍;而补充模型最优值的迭代次数比原模型减小了约55%,同时最优值比原模型提高了0.3%。所以该约束条件不仅提高了迭代速率,还提高了寻求全局最优值的概率,使得最优解更逼近全局最优值。
A performance optimization model of direct-contact heat exchangers was established, in which the volumetric heat transfer coefficient was used as objective function, the refrigerant initial flow rate U0, the nozzle diameter di, and the heat transfer oil height Z were used as decision variables. The linear model of bubble swarm and heat transfer performance was used as constraint condition in the optimization model, and the influence of the constraint condition on optimization process and results were mainly analyzed. The genetic algorithm was used to analyze the original model and supplementary model. And the results show that the optimized volumetric heat transfer coefficient of the original model is promoted by 6.7 times compared with that of the original design. The number of iterations of the optimal value in the supplement model is reduced by around 55%, and the optimal value increases by 0.3% compared with those of the original model. Therefore, this constraint condition improves not only the iteration rate, but also the probability of seeking the global optimum, which makes the optimal solution more approximate to the global optimal value.