{"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}
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.