Design of shell-and-tube heat exchangers using multiobjective optimization
Design of shell-and-tube heat exchangers using multiobjective optimization
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DOI：
10.1016/j.ijheatmasstransfer.2012.12.047
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发表时间：
2013-05
影响因子：
5.2
通讯作者：
Salim Fettaka;J. Thibault;Y. Gupta
中科院分区：
文献类型：
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作者：
Salim Fettaka;J. Thibault;Y. Gupta
本文对管壳式换热器的传热面积和抽气功率进行了多目标优化，为设计人员提供了多个pareto最优解，可以在两个目标之间进行权衡。考虑了9个决策变量：管布置型式、管道数、挡板间距、挡板切割、管-挡板直径间隙、壳-挡板直径间隙、管长、管外径和管壁厚度。采用MATLAB®多目标遗传算法模块中提供的快速精英非支配排序遗传算法（NSGA-II）进行优化。为了验证该方法提供的设计改进，从公开文献中提出了两个案例研究。结果表明，对于这两个案例研究，两个目标函数的值都比先前发表的值更好。此外，NSGA-II提供了一个具有更大范围最优决策变量的帕累托前沿。使用简单的成本函数对帕累托最优解进行排序表明，最优设计的成本低于这两个案例研究文献中报道的成本。该算法还用于确定使用连续的管道长度、直径和厚度值而不是使用离散的标准工业值对获得最佳传热面积和泵送功率的影响。结果表明，与离散值相比，使用这三个决策变量的连续值只能略微提高性能。
In this paper, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is presented to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. Nine decision variables were considered: tube layout pattern, number of tube passes, baffle spacing, baffle cut, tube-to-baffle diametrical clearance, shell-to-baffle diametrical clearance, tube length, tube outer diameter, and tube wall thickness. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) available in the multiobjective genetic algorithm module of MATLAB®. In order to verify the improvements in design that the method offers, two case studies from the open literature are presented. The results show that for both case studies, better values of the two objective functions can be obtained than the ones previously published. In addition, NSGA-II provides a Pareto front with a wider range of optimal decision variables. Ranking the Pareto-optimal solutions using a simple cost function shows that the costs for optimal design are lower than those reported in the literature for both case studies. The algorithm was also used to determine the impact of using continuous values of the tube length, diameter and thickness rather than using discrete standard industrial values to obtain the optimal heat transfer area and pumping power. Results show that using continuous values of these three decision variables only leads to marginally improved performance compared to discrete values.