This thesis focuses on the development of a real-time state estimation system for superconducting magnets, aimed at detecting early signs of instability and preventing quench events. The activity will investigate the use of advanced estimation techniques to reconstruct the internal electro-thermal state of a superconducting coil from available sensor measurements.
The student will develop numerical models of the magnet dynamics, including current distribution, temperature evolution, voltage response and thermal disturbances. These models will be used to design real-time observers capable of identifying abnormal operating conditions before they evolve into a quench. A key part of the thesis will be the implementation of the estimation algorithm on an FPGA platform, enabling deterministic low-latency operation. The final objective is to test the system on a superconducting coil operated in liquid nitrogen, demonstrating real-time monitoring and active support for safe magnet operation. The thesis will be carried out in collaboration with INFN.
