A prediction method for the screening current induced field in HTS magnets based on time series models

Due to their special electromagnetic properties, high temperature superconducting (HTS) conductors have become a potential solution for ultra-high field magnet and energy storage applications. However, the screening current induced field (SCIF) has been demonstrated to be the main limitation of high field HTS magnets in actual applications. Based on time series models, this paper presents a prediction method of SCIF to support the design and application of HTS magnets. First, we analyze the data characteristics of the SCIF hysteresis loop. The simulated dataset is prepared for two typical magnet structures: single pancake and solenoid. Then, time series models are proposed for the SCIF prediction. Through intuitive analysis and evaluation metrics, the training performance of time series models is confirmed. After a discussion of hyper-parameters and dimension reduction, the optimized prediction performance is obtained for the SCIF hysteresis loop. In conjunction with the iterative prediction mode, we finally achieve a feasible and effective prediction method of SCIF for HTS magnets. This will provide a tool and research strategy to support the general finite element method.