Framework for automatic superconducting magnet model generation & validation against transients measured in LHC magnets

Superconducting magnet circuits are a key part of particle accelerators. Simulating complex transients occurring in these circuits with dedicated models is crucial to analyze their behavior and assess the impact of failure cases. The recently developed STEAM framework from CERN tackles this challenge with a series of dedicated software tools and model libraries. These models need to be validated and evaluated against experimental transients. This thesis discusses a newly developed addition to the STEAM framework, which provides the possibility to automatically generate and validate the superconducting magnet models, and hence to significantly shorten the required time and effort to validate such models. This is achieved by conducting a parametric sweep analysis aiming at determining the unknown model parameters. The developed solution is supplementing the already existing Python programming interface within the STEAM framework for the model generation. Its usage, implementation, and integration into the framework is described. This thesis presents the application on two different use cases of LHC superconducting magnets. These achieve an excellent agreement between simulations and measurements collected during LHC operation. Furthermore, it is shown that this solution can also be used to predict the occurrence of specific behavior in the superconducting magnets such as quench-back. The application on a specific use-case on multiple quench events in the main bending dipoles of the LHC presents a way to offer simulation results to quickly and easily support the everyday operation of the accelerator. Therefore, this thesis can be seen as a guideline and presentation of the practicability, abilities, and limitations of the automated framework

A Simplified Approach to Simulate Quench Development in a Superconducting Magnet

Cross-sectional 2D models often represent a computationally efficient alternative to full 3D models, when simulating complex multi-physical magnet systems. However, especially for the case of self-protected, superconducting magnets, where the stored energy has to be dissipated within the magnet coils, the thermal diffusion and the quench development in all three dimensions become key aspects. In order to further improve the simulation of transients in 2D models, a new modelling method for simplified quench development along the direction of the transport current is introduced. The original 2D model is hereby utilized for modelling the thermal domain, and the electrical resistance of each turn is scaled by the estimated time-dependent fraction of quenched conductor. Furthermore, the turn to turn quench propagation following the electrical connections is implemented. The proposed approach allows a very computationally efficient and easy-to-implement calculation since the model is effectively two-dimensional while providing a good approximation of the coil resistance development with sufficient accuracy. In order to illustrate the proposed quench-propagation modelling approach, simulations are compared to experimental results for the case of a self-protected, superconducting Nb-Ti dipole magnet. In general, a very good agreement between measurements and simulations was achieved

Interpretable Anomaly Detection in the LHC Main Dipole Circuits With Nonnegative Matrix Factorization

CERN's Large Hadron Collider (LHC), with its eight superconducting main dipole circuits, has been in operation for over a decade. During this time, relevant operational parameters of the circuits, including circuit current, voltages across magnets and their coils, and current to ground, have been recorded. These data allow for a comprehensive analysis of the circuit characteristics, the interaction between their components, and their variation over time. Such insights are essential to understand the state of health of the circuits and to detect and react to hardware fatigue and degradation at an early stage. In this work, a systematic approach is presented to better understand the behavior of the main LHC dipole circuits following fast power aborts. Nonnegative matrix factorization is used to model the recorded frequency spectra as common subspectra by decomposing the recorded data as a linear combination of basis vectors, which are then related to hardware properties. The loss in reconstructing the recorded frequency spectra allows to distinguish between normal and abnormal magnet behavior. In the case of abnormal behavior, the analysis of the subspectra properties enables to infer possible hardware issues. Following this approach, five dipole magnets with abnormal behavior were identified, of which one was confirmed to be damaged. As three of the other four identified magnets share similar subspectra characteristics, they are also treated as potentially critical. These results are essential for preparing targeted magnet measurements and may lead to preventive replacements