克隆选择算法在热管设计中的应用

Optimal design of heat pipe based on clonal selection algorithm

热管作为一种高效热传输设备,被广泛应用于许多工程领域。对高效热管的优化设计,通常会涉及到多个目标参数,而采用传统的设计方法,往往无法同时有效地优化多个目标。克隆算法具有很强的自学习和自适应能力,在解决多目标优化问题方面优势显著,因此文中提出了基于克隆算法的热管多目标优化算法。通过对热管建立热网络分析模型和热传输极限模型,给出了热管多目标优化问题的数学描述,并针对该数学描述,设计了基于克隆算法的多目标优化算法。通过多个算例的计算分析,证实了该算法的有效性和优越性。由于数学问题的提出是基于热管的基本原理,因此该算法具有可推广性,可用于多种形式的热管设计。

Heat pipes are used widely in many engineering fields due to their higher rate of heat transfer.However,the design of heat pipes often involves selection of multi-object parameters,and the conventional design algorithms are not efficient in optimizing these parameters simultaneously.Clonal selection algorithm（CSA） is very useful in solving such a problem because of its strong self-learning and self-adaptive abilities.The purpose of the present work is to design a multi-object optimization algorithm for heat pipe based on the CSA.The thermal network model and heat transfer limit model of a typical heat pipe were built.On this basis,the optimal problem of the heat pipe was defined mathematically,and the algorithm was introduced to solve this problem.The results of several case studies based on this algorithm were discussed.Its effectiveness and superiority were clearly demonstrated.Since the models are built on the basic heat pipe analysis theory,the algorithm can be easily extended to solve the optimal problem of many types of heat pipe.