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Nikola Jerance

Publications and source records attributed to Nikola Jerance.

3 recordsLinked to original sources

A fully coupled electromagnetic-thermal-mechanical model for metal-insulated HTS high field magnets

Ultra high field REBCO magnets operate under strongly coupled electromagnetic, thermal and mechanical conditions, where screening currents, localized heating, thermal expansion and Lorentz forces can modify both the structural state and the critical current density of the conductor. In this work, a coupled electromagnetic, thermal and mechanical model is developed for a metal-insulated nested REBCO insert designed for a 40 T class SuperEMFL magnet. The existing electromagnetic formulation resolves the non-uniform screening currents in the REBCO tapes. The thermal model is extended from an explicit Finite Difference Method (FDM) to an implicit Backward Euler scheme with Picard iteration, while a new axisymmetric mechanical FDM solver based on BiCGSTAB is introduced to calculate displacements, strains and stresses in the coil windings and G10 spacer regions. Thermal expansion and Lorentz force contributions are included, and the calculated longitudinal mechanical strain is coupled back to the electromagnetic model through a strain dependent critical current density, which also depends on temperature, magnetic field, and its orientation. A literature-informed Parabolic-Weibull model is used to model reversible and irreversible strain degradation of the REBCO conductor. The numerical methods are benchmarked, and the resulting framework provides a computationally efficient approach for investigating temperature gradients, thermo-mechanical stresses, strain-dependent critical current degradation and quench behaviour in full scale nested high field REBCO magnets.

physics.app-ph

Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay

Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artificial Intelligence (AI), including machine learning and advanced optimisation techniques, offers promising approaches to address these challenges and accelerate the design process. This paper presents a new AI-based optimisation and data management platform, and highlights several ongoing applications of AI methods carried out at CEA Paris-Saclay, including multiphysics optimisation using active learning, topology optimisation, holistic modelling of an Electron Cyclotron Resonance (ERC) ion source, and anomaly detection in quench events.

physics.acc-ph

Screening currents increase thermal quench propagation speed in ultra-high-field REBCO magnets

Superconducting REBCO ($RE$Ba$_2$Cu$_3$O$_{7-x}$, where $RE$ is a rare earth, typically Y, Gd or Eu) electromagnets are useful for many applications like medical magnetic resonace imaging (MRI), nuclear magnetic resonance (NMR) spectroscopy, and magnets for particle accelerators and detectors. REBCO magnets are also the core of many nuclear fusion energy start-ups. In order to avoid permanent damage during operation, magnet design needs to take electro-thermal quench into account, which is due to unavoidable REBCO tape or magnet imperfections. However, most high-field magnet designs do not take superconducting screening currents into account. In this work, we show that it is essential to consider screening currents in magnet design, since they highly speed-up electrothermal quench propagation. Our study is based on detailed numerical modeling, based on the Minimum Electromagnetic Entropy Production (MEMEP) and Finite Differences (MEMEP-FD). Benchmarking with well-established Partial Element Equivalent Circuit (PEEC) model supports the correctness of MEMEP-FD. This work focusses on a 32 T all-superconducting magnet design and we analyze in detail the time evolution of electrothermal quench. Our findings will have an impact in the design of ultra-high-field magnets for NMR or user facilities, and possibly for other kinds of magnets, like those for fusion energy.

physics.app-ph