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船舶核动力装置蒸汽旁排工况的动态特性及影响因素分析
引用本文:林焰, 辛登月, 卞璇屹, 等. 改进自适应惯性权重粒子群算法及其在核动力管道布置中的应用[J]. 中国舰船研究, 2023, 18(3): 1–12, 25. doi: 10.19693/j.issn.1673-3185.02940
作者姓名:林焰  辛登月  卞璇屹  张乔宇  李铁骊
作者单位:1.大连理工大学 船舶工程学院,辽宁 大连 116024
基金项目:工业装备结构分析国家重点实验室专项基金资助项目(S18315)
摘    要:  目的  旨在研究非线性自适应惯性权重粒子群优化算法,实现船用核动力一回路系统管道路径的布置优化设计。  方法  根据船用核动力一回路系统的管道布局设计特点,建立一回路系统的管道布局空间模型、约束条件和评价函数;基于管道节点数量,提出一种粒子群优化(PSO)算法的新型定长编码方法,然后结合该编码方法建立方向引导机制;在此基础上,针对粒子群优化算法易陷入局部最优解、收敛速度慢的缺点,结合辅助线性变化的学习因子,提出一种基于非线性自适应惯性权重的改进粒子群优化算法;将改进粒子群优化算法与协同进化算法相结合,提出一种用于求解分支管道布局问题的协同进化粒子群优化算法,以用于核动力一回路系统的管道布局优化。  结果  仿真结果显示,所提的改进算法与标准算法相比收敛速度提高了40%~50%,不仅能够得到更好的管道布局效果,还解决了标准粒子群优化算法容易陷入局部最优解的问题。  结论  研究成果可为船用核动力一回路系统管道布置的优化设计提供有益的参考。

关 键 词:船用核动力  一回路系统  粒子群优化算法  非线性惯性权重  自适应  线性学习因子
收稿时间:2022-05-31
修稿时间:2022-07-03

Analysis of dynamic characteristics and influencing factors of steam dump conditions of marine nuclear power plant
LIN Y, XIN D Y, BIAN X Y, et al. Improved adaptive inertia weight PSO algorithm and its application in nuclear power pipeline layout optimization[J]. Chinese Journal of Ship Research, 2023, 18(3): 1–12, 25. doi: 10.19693/j.issn.1673-3185.02940
Authors:LIN Yan  XIN Dengyue  BIAN Xuanyi  ZHANG Qiaoyu  LI Tieli
Affiliation:1.School of Naval Architecture Engineering, Dalian University of Technology, Dalian 116024, China
Abstract:  Objectives  This study explores the use of a nonlinear adaptive inertia weight particle swarm optimization (PSO) algorithm to realize the optimal design of the path and arrangement of pipelines in the nuclear power primary loop systems of ships.   Methods  According to the pipeline layout design characteristics, the constraints, evaluation functions and spatial model of the primary loop system are established. Based on the number of pipeline nodes, a new fixed-length coding method for the PSO algorithm is proposed, along with a direction guidance mechanism. As the standard PSO algorithm has such shortcomings as a slow convergence speed and susceptibility to falling into the local optimal solution, an improved nonlinear adaptive inertia weight PSO algorithm supplemented by a linearly changing learning factor is proposed. The improved PSO algorithm is combined with a co-evolutionary algorithm to form a co-evolutionary PSO algorithm for solving branch pipeline problems. The improved algorithm is then applied to the pipeline layout optimization problem of the nuclear power primary loop systems of ships.   Results  The simulation results show that the convergence speed of the proposed algorithm is increased by 40% –50% compared with that of the standard algorithm. The improved algorithm can not only obtain higher quality pipeline layouts, but also solve the problem in which the standard PSO algorithm can easily fall into the local optimal solution.   Conclusions   The results of this study can provide useful references for the pipeline layout optimization of the nuclear power primary loop systems of ships.
Keywords:marine nuclear power  primary loop system  particle swarm optimization (PSO) algorithm  nonlinear inertia weights  adaptive  linear learning factor
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