电磁线圈
机械
电感
导电体
有限元法
材料科学
瞬态(计算机编程)
时间常数
接触电阻
电阻抗
指数衰减
电压
物理
电气工程
计算机科学
工程类
热力学
复合材料
核物理学
操作系统
图层(电子)
作者
Sriharsha Venuturumilli,Ratu Mataira,R. W. Taylor,Jofferson T. Gonzales,Chris W. Bumby
出处
期刊:AIP Advances
[American Institute of Physics]
日期:2023-03-01
卷期号:13 (3)
被引量:3
摘要
High-temperature superconducting (HTS) non-insulated (NI) coils have the unique capability to bypass current through conductive turn-to-turn contacts, mitigating the possibility of a catastrophic failure in the event of a quench. However, this turn-to-turn conductivity leads to a significant increase in the coil decay/charging time constant. To understand this phenomenon, several modeling techniques have been proposed, including the lumped and distributed network (DN) circuit models, and more recently the finite-element (FE) models. In this paper, the decay results obtained from modeling HTS NI pancake coils using both a DN model and a 2D FE model approach are evaluated and compared. Steady-state fields, and transient charging and decay behaviors are calculated with each model and the results compared. Key differences are highlighted, including the computation speed and the capturing of various physical phenomena. Both models exhibit non-exponential decay during initial coil discharge due to current redistribution between the inner and outer turns. In addition, the FE model exhibits other effects arising from current redistribution in both the radial and axial directions, including remanent magnetization, and variation of the “apparent total inductance” during charging. Simulations of sudden discharge have also been analyzed using the common “lumped circuit” formula. This shows that extracted values for the apparent surface contact resistance between coil windings can differ by more than a factor of 5 from the initial input value. Our results confirms the optimal choice of architecture for future NI coil models and emphasize that caution should be exercised when interpreting experimental results using the lumped circuit approach.
科研通智能强力驱动
Strongly Powered by AbleSci AI