Prediction of AC Loss of REBCO Lap Joint Using Artificial Intelligence-Based Models

被引：0
|
作者：
Zhu, Yunpeng [1 ]
Yang, Zongyu [1 ]
Yang, Xinsheng [2 ]
Hu, Xinbo [1 ]
Liu, Jian [1 ]
Cai, Lijun [1 ]
Jiang, Jing [2 ]
Zhang, Shengnan [3 ]
Tan, Yunfei [4 ]
Zhao, Yong [2 ]
机构：
[1] Southwestern Inst Phys, Chengdu 610041, Peoples R China
[2] Southwest Jiaotong Univ, Key Lab Magnet Suspens Technol & Maglev Vehicle, Minist Educ, Chengdu 610031, Peoples R China
[3] Northwest Inst Nonferrous Met Res, Xian 710016, Peoples R China
[4] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY | 2024年 / 34卷 / 08期
关键词：
Resistance; Superconducting magnets; Superconducting cables; Superconductivity; Data models; Superconducting films; Numerical models; High-temperature superconductor; lap joint; AC loss;
D O I：
10.1109/TASC.2024.3420317
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The AC loss of the high temperature superconducting (HTS) cable with joints is a key factor to estimate the cooling effect of the HTS cable. In this work, an artificial intelligence-based model is proposed to predict the AC loss of REBCO lap joints when carrying alternating current. The training data was obtained from simulation by a 3-D lap joint model based on H-formulation. The artificial intelligence-based model is demonstrated to evaluate the effect of inhomogeneity of joint resistance on AC loss. It is shown that the AC loss of lap joints in the cable is increased when the nonuniformity of joint resistance is higher. The proposed model can increase the speed of calculating the statistical results depending on the mass of investigating data.
引用
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页码：1 / 4
页数：4
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