Electrodynamics of an High Luminosity LHC Superconducting Magnet

Superconducting magnets are key components in particle accelerators, essential for the precise guidance and focusing of particle beams. However, these magnets are subjected to stresses, like radiation and mechanical disturbances, which can lead to faults such as short circuits in the coil windings. Identifying such faults in the magnets installed in the LHC is a major challenge. A valuable method to assess their electromagnetic properties and detecting potential problems is to analyse their complex impedance over a wide range of frequencies. This thesis contributes to the advancement of CERN's STEAM (Simulation of Transient Effects in Accelerator Magnets) framework by introducing a new tool specifically designed to simulate complex impedance in the frequency domain. While STEAM already supports the modeling of transient effects and magnet circuits, it has lacked a comprehensive solution for complex impedance simulations in the frequency domain. To address this gap, a new Python-based tool was developed, built on a physics-driven lumped element network model. This model integrates finite element modeling using COMSOL Multiphysics® with analytical techniques, taking into account key factors such as coupling losses and interactions between coil turns. This approach also facilitates the modeling of short circuits by incorporating resistances across the coil turns. This thesis provides an overview of the development and functionality of this tool and presents its validation through impedance measurements of the Main Bending Recombination Dipole (MBRD), a magnet recently measured at CERN's SM18 facility. The validation process included simulations of both standard operating conditions and faulty scenarios with short circuits, demonstrating the tool's ability to capture the complex behavior of superconducting magnets. The results confirm the accuracy of the tool in predicting and analyzing magnet performance, including the effects of short circuits with varying resistance values and fault locations. In summary, this work improves the understanding of superconducting magnet impedance characteristics and introduces a reliable method for early detection of potential failures, thereby contributing to the ongoing safety and performance of the LHC's magnets.