Iterative multiscale method for accurate estimation of current density and hysteresis losses in large scale HTS systems
Abstract
In recent years, commercial HTS superconductors have gained an increasing interest for their use in applications involving largescale superconductor systems. These systems are typically made from hundreds to thousands of turns of conductors. Due to the large number of turns, the simulations of a whole system can become prohibitive in terms of computing time and load. Therefore, an efficient strategy which does not compromise the accuracy of calculations is needed. Recently, a method, based on a multiscale approach, showed that the computational load can be lowered by simulating, in detail, only several significant tapes from the system. The main limitation of this approach is the inaccuracy of the estimation of the background magnetic field. To address this issue, we consider the following two complementary strategies. The first strategy consists in the iterative implementation of the multiscale method. The multiscale method solves itself a dynamic problem, the iterative implementation proposed here is the iterative application of the multiscale method, and a dynamic solution is obtained at each iteration. The second strategy is a new interpolation method for current distributions, based on the inverse cumulative density function interpolation technique. With respect to conventional interpolation methods, a more realistic current density distribution is then obtained, which allows for a better estimation of the background magnetic field, and consequently, a better estimation of the hysteresis losses. In contrast with previous works, here we do not focus only on the estimation of the hysteresis losses, but also the estimation of background field and the current density distribution is addressed.
 Publication:

arXiv eprints
 Pub Date:
 November 2017
 arXiv:
 arXiv:1711.07447
 Bibcode:
 2017arXiv171107447B
 Keywords:

 Physics  Computational Physics;
 Physics  Accelerator Physics