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高速电磁阀动态响应的多目标优化

范立云 周伟 刘鹏 赵建辉 格列霍夫·雷奥尼德

哈尔滨工程大学学报2018,Vol.39Issue(1):53-59,7.
哈尔滨工程大学学报2018,Vol.39Issue(1):53-59,7.DOI:10.11990/jheu.201703089

高速电磁阀动态响应的多目标优化

Multi objective optimization of dynamic response of high speed solenoid valve

范立云 1周伟 1刘鹏 1赵建辉 1格列霍夫·雷奥尼德2

作者信息

  • 1. 哈尔滨工程大学动力与能源工程学院,黑龙江哈尔滨150001
  • 2. 莫斯科国立鲍曼技术大学往返活塞发动机教研室,俄罗斯莫斯科105005
  • 折叠

摘要

Abstract

In order to improve the dynamic response speed of high-speed solenoid valve ( HSV) , a method consis-ting of numerical simulations along with lab experiments is employed.Firstly, the transient numerical simulation model of HSV was established, then its accuracy was verified by experiments that the errors of the closing response time and the opening response time were 1%and 3%respectively.At the same time, the five key factors that af-fect the dynamic response characteristics of HSV were selected, and the sample points were found by the Optimal Latin hypercube experimental design for building Kriging models.The Kriging approximate models were built ai-ming at the closing response time, the opening response time and the electromagnetic force.Finally, the dynamic response characteristics of HSV were studied by the multi-objective optimization technique.The Pareto solution was obtained by NSGA-Ⅱ genetic algorithm, and the final solution was determined.The results show that the dynamic response speed of HSV is improved, and the closing response time decreases by 10.1% from 0.691 ms to 0.621 ms, the opening response time decreases by 10.4%from 0.546 ms to 0.489 ms.It provides a theoretical basis for the optimization of the structure of HSV.

关键词

高速电磁阀/动态响应/NSGA-Ⅱ遗传算法/Kriging模型/多目标优化/Pareto解

Key words

high-speed solenoid valve/dynamic response/NSGA-Ⅱ genetic algorithm/Kriging approximation mod-el/multi-objective optimization/Pareto solution

分类

能源科技

引用本文复制引用

范立云,周伟,刘鹏,赵建辉,格列霍夫·雷奥尼德..高速电磁阀动态响应的多目标优化[J].哈尔滨工程大学学报,2018,39(1):53-59,7.

基金项目

国家自然科学基金项目(51379041,51475100,51679048). (51379041,51475100,51679048)

哈尔滨工程大学学报

OA北大核心CSCDCSTPCD

1006-7043

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