微型换热器优化的比较研究

T. Okabe, K. Foli, M. Olhofer, Yaochu Jin, B. Sendhoff
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引用次数: 15

摘要

尽管如K. Deb(2001)所述,许多处理多目标优化(MOO)问题的方法是可用的,并且C.A. Coello等人(2001)也报道了成功的应用,但很少对应用于现实问题的MOO方法进行比较。本文将MOO方法应用于实际问题的比较,即微型换热器(/spl mu/HEX)的优化。本研究采用了Y. Jin等人(2001)提出的动态加权聚合(DWA)和K. Deb等人(2000)和K. Deb等人(2002)提出的非支配排序遗传算法(NSGA-II)两种MOO方法。商业计算流体动力学(CFD)求解器CFD- ace +用于评估适应度。我们介绍了如何将商业求解器与进化计算(EC)接口,并报告了用于优化的商业求解器的必要功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative studies on micro heat exchanger optimisation
Although many methods for dealing with multi-objective optimisation (MOO) problems are available as stated in K. Deb (2001) and successful applications have been reported on C.A. Coello et al. (2001), the comparison between MOO methods applied to real-world problem was rarely carried out. This paper reports the comparison between MOO methods applied to a real-world problem, namely, the optimization of a micro heat exchanger (/spl mu/HEX). Two MOO methods, dynamically weighted aggregation (DWA) proposed by Y. Jin et al. (2001) and non-dominated sorting genetic algorithms (NSGA-II) proposed by K. Deb et al. (2000) and K. Deb et al. (2002), were used for the study. The commercial computational fluid dynamics (CFD) solver CFD-ACE+ is used to evaluate fitness. We introduce how to interface the commercial solver with evolutionary computation (EC) and also report the necessary functionalities of the commercial solver to be used for the optimisation.
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