Shabalina, A. | Search NASA

DOE OSTI2024

Transfer of EP and Doping Technology for PIP-II HB650 Cavities from Fermilab to Industry

Fermilab has optimized the surface processing conditions for PIP-II high beta 650 MHz cavities. This encompasses conditions for bulk electropolishing, heat treatment, nitrogen doping, post-doping final electropolishing, and post-processing surface rinsing. The technology has been effectively transitioned to industry. This paper highlights the efforts made to fine-tune the process and to smoothly share them with the partner labs and an associated vendor.

Chouhan, V.↗

DOE OSTI2020

Machine Learning Based Cavity Fault Classification and Prediction

We report on the development of machine learning models for classifying C100 superconducting radio frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the eight cavities in the cryomodule. Subject matter experts (SME) can analyze the collected time-series data, identify which of the eight cavities faulted first, and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the ML models during a recent physics run.

43 PARTICLE ACCELERATORS↗