Estimation of critical current density of bulk superconductor with artificial neural network

作     者：Gangling Wu Huadong Yong 

作者机构：Key Laboratory of Mechanics on Disaster and Environment in Western ChinaThe Ministry of Education of ChinaLanzhou UniversityLanzhouGansu 730000People’s Republic of China Department of Mechanics and Engineering SciencesCollege of Civil Engineering and MechanicsLanzhou UniversityLanzhouGansu 730000People’s Republic of China 

出 版 物：《Superconductivity》 (超导（英文）)

年 卷 期：2023年第7卷第3期

页      面：34-45页

学科分类：0808[工学-电气工程] 07[理学] 0701[理学-数学] 0702[理学-物理学] 

基　　金：support from the National Natural Science Foundation of China(Grant Nos.U2241267 12172155 and 11872195). 

主　　题：Critical current density ANN Kim model Hysteresis loop Magnetostriction loop Bulk superconductor 

摘      要：In the applications of superconducting materials,the critical current density J_(c)(B)is a crucial performance parameter.The conventional method of measuring J_(c)(B)of bulk superconductor is magnetization method.However,there are errors in the estimation of J_(c)(B)in the lower field,and the estimation is not applicable in the region where the magnetic field reverses.In this paper,J_(c)(B)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.