油系统静电传感器空间灵敏度的优化设计

C. Zhixiong, Jin Zelai, Y. Jun
{"title":"油系统静电传感器空间灵敏度的优化设计","authors":"C. Zhixiong, Jin Zelai, Y. Jun","doi":"10.1109/ICEMI46757.2019.9101604","DOIUrl":null,"url":null,"abstract":"In this paper, the authors present spatial sensitivity optimization model of an electrostatic sensor for application in aero-engine oil system. This method is strongly constrained towards the structural design of the sensor because problems involving nonlinearities and sensitivity functions are inherent in current numerical solutions. The optimization of the sensitivity design model enables the establishment of each decision variable and constraints. The constraints were established through the limiting of the probe radius and the axial length. Under these constraints, this paper uses genetic algorithms to maximize the application of the constraints of each component to find the global optimal. The results clearly depicted the following: 1) the smaller radius of the probe, the greater sensor sensitivity, 2) the greater axial length, the greater sensitivity, 3) the closer charged particles were to the tube wall, the more sensitive was the induced charge, and 4) when the axial-to-radial ratio is 1, the sensor has a high and uniform sensitivity.","PeriodicalId":419168,"journal":{"name":"2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization design of electrostatic sensor spatial sensitivity in the oil system\",\"authors\":\"C. Zhixiong, Jin Zelai, Y. Jun\",\"doi\":\"10.1109/ICEMI46757.2019.9101604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the authors present spatial sensitivity optimization model of an electrostatic sensor for application in aero-engine oil system. This method is strongly constrained towards the structural design of the sensor because problems involving nonlinearities and sensitivity functions are inherent in current numerical solutions. The optimization of the sensitivity design model enables the establishment of each decision variable and constraints. The constraints were established through the limiting of the probe radius and the axial length. Under these constraints, this paper uses genetic algorithms to maximize the application of the constraints of each component to find the global optimal. The results clearly depicted the following: 1) the smaller radius of the probe, the greater sensor sensitivity, 2) the greater axial length, the greater sensitivity, 3) the closer charged particles were to the tube wall, the more sensitive was the induced charge, and 4) when the axial-to-radial ratio is 1, the sensor has a high and uniform sensitivity.\",\"PeriodicalId\":419168,\"journal\":{\"name\":\"2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEMI46757.2019.9101604\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMI46757.2019.9101604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种用于航空发动机油系统的静电传感器的空间灵敏度优化模型。由于涉及非线性和灵敏度函数的问题在目前的数值解中是固有的,因此这种方法在传感器的结构设计中受到强烈的约束。灵敏度设计模型的优化使各决策变量和约束的建立成为可能。通过限制探针半径和轴向长度来建立约束条件。在这些约束条件下,本文采用遗传算法最大化应用各分量的约束条件,求出全局最优解。结果表明:探头半径越小,传感器灵敏度越高;轴向长度越大,传感器灵敏度越高;带电粒子离管壁越近,感应电荷越敏感;轴向径向比为1时,传感器灵敏度高且均匀。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization design of electrostatic sensor spatial sensitivity in the oil system
In this paper, the authors present spatial sensitivity optimization model of an electrostatic sensor for application in aero-engine oil system. This method is strongly constrained towards the structural design of the sensor because problems involving nonlinearities and sensitivity functions are inherent in current numerical solutions. The optimization of the sensitivity design model enables the establishment of each decision variable and constraints. The constraints were established through the limiting of the probe radius and the axial length. Under these constraints, this paper uses genetic algorithms to maximize the application of the constraints of each component to find the global optimal. The results clearly depicted the following: 1) the smaller radius of the probe, the greater sensor sensitivity, 2) the greater axial length, the greater sensitivity, 3) the closer charged particles were to the tube wall, the more sensitive was the induced charge, and 4) when the axial-to-radial ratio is 1, the sensor has a high and uniform sensitivity.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信