激发
各向同性
振幅
领域(数学)
超导电性
磁场
凝聚态物理
材料科学
导电体
物理
核磁共振
光学
复合材料
数学
量子力学
纯数学
作者
Yating Liu,Yinshun Wang,Guangyi Zhang,Yuheng Chen
标识
DOI:10.1088/1361-6668/ac3634
摘要
Abstract The screening current induced field (SCIF) in the flat REBCO coated conductors so called the second generation high temperature superconducting tapes cause undesirable effects in multiple applications. Their existence reduces the spatial uniformity and temporal stability of magnetic fields for applications of superconducting magnets. In this paper, we numerically and experimentally investigate the characterization of the screening current and SCIF of quasi-isotropic strand (Q-IS) and simply stacked strand (SSS) under external alternating magnetic field with various amplitudes, orientations, and excitation rates. The two-dimensional finite element method based on T-A formulation is adopted for simulation, the Q-IS and SSS samples are fabricated for experiments. The field angle is in the range of 0°–90° at intervals of 15°, the excitation rate varies from 20 mT s −1 to 800 mT s −1 . We display the distribution of screening current in both strands under various field amplitudes and orientations. Then the dependence of the SCIF on the amplitude and orientation of the external field is studied, respectively. The spatial distribution of SCIF of both strands with different amplitudes and angles of the external field are also discussed. Besides, we analyze the properties of SCIF under various excitation rates. As a result, the SCIF of Q-IS is much smaller and has quasi-isotropic distribution compared to SSS, which represents that Q-IS has a relative smaller screening effect. The spatial point with the largest SCIF of Q-IS locates at the corner of the strand and is independent of the external field, but the corresponding point in SSS varies with the angle and amplitude. The Q-IS is also less susceptible to the change of rate. Therefore, Q-IS has more advantages when the screening effect is considered in superconducting applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI