T. Matsuo, Yuki Nishimura, Yutaka Mishima, T. Mifune, Yasuhito Takahashi, K. Fujiwara
{"title":"Pinning Field Modeling Using Stop Hysterons for Multi-domain Particle Model","authors":"T. Matsuo, Yuki Nishimura, Yutaka Mishima, T. Mifune, Yasuhito Takahashi, K. Fujiwara","doi":"10.1109/COMPUMAG45669.2019.9032764","DOIUrl":null,"url":null,"abstract":"The stress-dependent magnetization of silicon steel is efficiently analyzed by applying an independent particle approximation to an assembled domain structure model. This simplifies magnetostatic computation. Stop hysterons are assembled to represent the pinning field, where the distribution of stop hysterons is determined using the identification methods of scalar and vector stop models. The stress-dependent loss is successfully predicted without parameter fitting to stress-dependent measurements.","PeriodicalId":317315,"journal":{"name":"2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPUMAG45669.2019.9032764","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The stress-dependent magnetization of silicon steel is efficiently analyzed by applying an independent particle approximation to an assembled domain structure model. This simplifies magnetostatic computation. Stop hysterons are assembled to represent the pinning field, where the distribution of stop hysterons is determined using the identification methods of scalar and vector stop models. The stress-dependent loss is successfully predicted without parameter fitting to stress-dependent measurements.