Optical fiber-based RF-breakdown detection and prediction

Supervisors: Carsten P Welsch (UoL), Joseph Wolfenden (UoL) and David Rowlands (Teledyne e2V)
Institution: University of Liverpool

The QUASAR Group, based at the Cockcroft Institute, in collaboration with the beam instrumentation company D-Beam Ltd, have pioneered the development and commercialization of optical fiber-based beam loss monitors for particle accelerators. During this development it was noted that the device presented a certain sensitivity to the measurement of RF-breakdown events, a commonplace failure mode in RF systems used in accelerator facilities to accelerate particle beams. The signal produced has the potential to provide a means of ongoing live monitoring of the RF source condition and, notably, the possibility to predict failure ahead of time.

To fully exploit the potential of this novel monitor, in particular for RF sources used in radiotherapy facilities, detailed simulation studies are required to further quantify and understand the generation of the fiber signals during breakdown events. Monte Carlo tools will be leveraged to study the radiation generation and detection in various RF-based configurations. Additionally, the operating parameters of this potential device are significantly removed from that of the current application to beam loss monitoring that an optimization study on the electronics, detection system, and fiber layout will also be required. This combination of simulation and experiment will allow machine learning-based studies into the predictive power of this device.

All results will be benchmarked against data which the student will obtain from measurements using the RF test stand at Teledyne e2V, which can run and monitor a variety of RF sources, such as magnetrons.

This project will be carried out in the QUASAR Group within the Accelerator Science cluster and has a focus on HPC-based simulation studies/data intensive science.

Throughout the project you will have access to the Cockcroft Institute’s comprehensive postgraduate training in accelerator science, as well as to targeted training in data science provided by the University of Liverpool with the Centre for Doctoral Training LIV.INNO.

https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/

Please ensure you mention Prof Carsten Welsch as the proposed supervisor on your application form and quote studentship reference: PPPR048.

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