基于知识及遗传退火混合算法的翅片管换热器管路优化方法

Knowledge based genetic-simulated-annealing method for optimizing tube circuit of fin-and-tube heat exchangers

通过在现有方法中引入专业知识及模拟退火算法,提出了一种基于知识及遗传退火混合算法的换热器管路连接优化方法。以一个实际翅片管换热器为例,在满足实际制造工艺的约束条件下,以换热器换热能力最大为优化目标对提出的方法进行了验证。结果表明,基于知识及遗传退火混合算法得到的最优管路连接中,各支路均匀交叉分布于空气流中,且所含换热管数目相等,避免了流体在不同支路之间换热不平衡的问题;优化后得到换热器的换热能力比优化前提高22%,比单纯基于遗传算法的优化方法得到换热器的换热能力提高10.3%。

A knowledge based genetic-simulated-annealing method for optimizing the tube circuit of finand-tube heat exchangers was presented by introducing specialized knowledge and the simulated annealing method into the existing method. Case studies were performed on a practical fin-and-tube heat exchanger. The optimization target is to obtain maximum heat exchange capacity of the heat exchanger with the constraint conditions of practical manufacture. The test results showed that after optimization, all paths of the optimal tube circuit were uniformly distributed in the airflow field and contained equal number of tubes, and the imbalance of heat transfer among the paths was avoided. The heat exchange capacity of the heat exchanger was 22% larger than that before optimization, and was 10.3% larger than that obtained by the existing pure-genetic-algorithm-based optimization method.