Novel and Performant Quench Analysis Tools for Superconducting Magnets: Technical Documentation

Abstract

Particle accelerators rely on superconducting magnets to guide particles in circular trajectories. These magnets can undergo quenching, a process in which a portion of the magnet’s coils experience a loss in superconductivity, thus causing a shift to a normal (resistive) state. Consequently, a current redistribution towards other wires in the coil occurs, which is detectable by relevant sensors. Quenches are irreversible, and it takes significant resources for the magnet to recover (e.g., additional liquid helium for the cryogenic magnet bath). Thus, understanding the causes of any given quench is vital. Our research introduces a Python-based tool used to visualize and process data from sensors surrounding superconducting magnets as they quench. We leverage a standalone back end, responsible for interacting with the data stored in TDMS files, and a front end in the form of a Graphical User Interface (GUI) built with Tkinter, which facilitates no-code interaction with the back end. The tool is designed to normalize units across the dataset, zero the data relative to the time of the quench to enable cross-sensor analysis, and also introduces various data handling tools important for user analysis. It used some functionality developed by Kiernan 2021, but it is the first tool attempting to be a modular package featuring easy use and upgrades. Future research shall leverage our tool to better understand the specific causes for a particular quench, with the ultimate goal of preventing them altogether.

Keywords: Superconductors, Quench, Data Science, Particle Accelerators