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Electromagnetic nonlinearities in a Roebel-cable-based accelerator magnet prototype: Variational approach

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Original languageEnglish
Article number024008
JournalSuperconductor Science and Technology
Issue number2
Publication statusPublished - 1 Feb 2017
Publication typeA1 Journal article-refereed


Superconducting magnets are the most expensive series of components produced in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). When developing such magnets beyond state-of-the-art technology, one possible option is to use high-temperature superconductors (HTS) that are capable of tolerating much higher magnetic fields than low-temperature superconductors (LTS), carrying simultaneously high current densities. Significant cost reductions due to decreased prototype construction needs can be achieved by careful modelling of the magnets. Simulations are used, e.g. for designing magnets fulfilling the field quality requirements of the beampipe, and adequate protection by studying the losses occurring during charging and discharging. We model the hysteresis losses and the magnetic field nonlinearity in the beampipe as a function of the magnet's current. These simulations rely on the minimum magnetic energy variation principle, with optimization algorithms provided by the open-source optimization library interior point optimizer. We utilize this methodology to investigate a research and development accelerator magnet prototype made of REBCO Roebel cable. The applicability of this approach, when the magnetic field dependence of the superconductor's critical current density is considered, is discussed. We also scrutinize the influence of the necessary modelling decisions one needs to make with this approach. The results show that different decisions can lead to notably different results, and experiments are required to study the electromagnetic behaviour of such magnets further.


  • AC loss, accelerator magnets, high-temperature superconductors, interior point optimizer, magnetization, minimum magnetic energy variation, nonlinear optimization

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