{"title":"Estimation of critical current density of bulk superconductor with artificial neural network","authors":"Gangling Wu, Huadong Yong","doi":"10.1016/j.supcon.2023.100055","DOIUrl":null,"url":null,"abstract":"<div><p>In the applications of superconducting materials, the critical current density <span><math><mrow><msub><mi>J</mi><mi>c</mi></msub><mrow><mfenced><mrow><mi>B</mi></mrow></mfenced></mrow></mrow></math></span> is a crucial performance parameter. The conventional method of measuring <span><math><mrow><msub><mi>J</mi><mi>c</mi></msub><mrow><mfenced><mrow><mi>B</mi></mrow></mfenced></mrow></mrow></math></span> of bulk superconductor is magnetization method. However, there are errors in the estimation of <span><math><mrow><msub><mi>J</mi><mi>c</mi></msub><mrow><mfenced><mrow><mi>B</mi></mrow></mfenced></mrow></mrow></math></span> in the lower field, and the estimation is not applicable in the region where the magnetic field reverses. In this paper, <span><math><mrow><msub><mi>J</mi><mi>c</mi></msub><mrow><mfenced><mrow><mi>B</mi></mrow></mfenced></mrow></mrow></math></span> of the bulk superconductor is determined by the hysteresis and magnetostriction loops with artificial neural network (ANN), respectively. Compared with double-output ANN, the critical current density obtained by single-output ANN is more accurate. Finally, the prediction results given by the hysteresis and magnetostriction loops are discussed.</p></div>","PeriodicalId":101185,"journal":{"name":"Superconductivity","volume":"7 ","pages":"Article 100055"},"PeriodicalIF":5.6000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Superconductivity","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772830723000200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In the applications of superconducting materials, the critical current density is a crucial performance parameter. The conventional method of measuring of bulk superconductor is magnetization method. However, there are errors in the estimation of in the lower field, and the estimation is not applicable in the region where the magnetic field reverses. In this paper, of the bulk superconductor is determined by the hysteresis and magnetostriction loops with artificial neural network (ANN), respectively. Compared with double-output ANN, the critical current density obtained by single-output ANN is more accurate. Finally, the prediction results given by the hysteresis and magnetostriction loops are discussed.