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基于NSGA-II的不可逆阿特金森循环四目标优化

Four-Objective Optimization of Irreversible Atkinson Cycle Based on NSGA-II.

作者信息

Shi Shuangshuang, Ge Yanlin, Chen Lingen, Feng Huijun

机构信息

Institute of Thermal Science and Power Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

出版信息

Entropy (Basel). 2020 Oct 13;22(10):1150. doi: 10.3390/e22101150.

Abstract

Variation trends of dimensionless power density (PD) with a compression ratio and thermal efficiency (TE) are discussed according to the irreversible Atkinson cycle (AC) model established in previous literature. Then, for the fixed cycle temperature ratio, the maximum specific volume ratios, the maximum pressure ratios, and the TEs corresponding to the maximum power output (PO) and the maximum PD are compared. Finally, multi-objective optimization (MOO) of cycle performance with dimensionless PO, TE, dimensionless PD, and dimensionless ecological function (EF) as the optimization objectives and compression ratio as the optimization variable are performed by applying the non-dominated sorting genetic algorithm-II (NSGA-II). The results show that there is an optimal compression ratio which will maximize the dimensionless PD. The relation curve of the dimensionless PD and compression ratio is a parabolic-like one, and the dimensionless PD and TE is a loop-shaped one. The AC engine has smaller size and higher TE under the maximum PD condition than those of under the maximum PO condition. With the increase of TE, the dimensionless PO will decrease, the dimensionless PD will increase, and the dimensionless EF will first increase and then decrease. There is no positive ideal point in Pareto frontier. The optimal solutions by using three decision-making methods are compared. This paper analyzes the performance of the PD of the AC with three losses, and performs MOO of dimensionless PO, TE, dimensionless PD, and dimensionless EF. The new conclusions obtained have theoretical guideline value for the optimal design of actual Atkinson heat engine.

摘要

根据先前文献中建立的不可逆阿特金森循环(AC)模型,讨论了无量纲功率密度(PD)随压缩比和热效率(TE)的变化趋势。然后,对于固定的循环温度比,比较了最大功率输出(PO)和最大PD对应的最大比容比、最大压力比和热效率。最后,以无量纲PO、TE、无量纲PD和无量纲生态函数(EF)为优化目标,压缩比为优化变量,应用非支配排序遗传算法-II(NSGA-II)对循环性能进行多目标优化(MOO)。结果表明,存在一个使无量纲PD最大化的最佳压缩比。无量纲PD与压缩比的关系曲线呈抛物线状,无量纲PD与TE的关系曲线呈环状。与最大功率输出条件相比,阿特金森循环发动机在最大PD条件下尺寸更小、热效率更高。随着热效率的增加,无量纲PO将降低,无量纲PD将增加,无量纲EF将先增加后降低。帕累托前沿不存在正理想点。比较了三种决策方法的最优解。本文分析了存在三种损失时阿特金森循环的功率密度性能,并对无量纲PO、TE、无量纲PD和无量纲EF进行了多目标优化。所得新结论对实际阿特金森热机的优化设计具有理论指导价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98e4/7597310/2e306c6186c7/entropy-22-01150-g001.jpg

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