Status and performance of the spallation neutron source superconducting linac

Campisi, I.E.; Assadi, S.; Casagrande, F.; Crofford, M.; Dodson, G.; Galambos, J.; Giannella, M.; Henderson, Stuart; Howell, M.; Kang, Y.; Kasemir, Kay; Kim, Sang-ho; Kursun, Z.; Ladd, P.; Ma, Hengjie; Stout, D.; Strong, W.; Zhang, Y.; Champion, M.

doi:10.1109/pac.2007.4441297

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…ADS require high reliability and availability which restrict accelerator downtime to few seconds on a daily scale [5,6]. Such requirements are currently beyond the performance of existing machines today [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting particle accelerator failures using binary classifiers

Reščič

Seviour

Blokland

2020

Nuclear Instruments and Methods in Physics Research Section A:

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Particle accelerator failures lead to unscheduled downtime and lower reliability. Although simple to mitigate while they are actually happening such failures are difficult to predict or identify beforehand. In this work we propose using machine learning approaches to predict machine failures via beam current measurements before they actual occur. To demonstrate this technique in this paper we examine beam pulses from the Oakridge Spallation Neutron Source (SNS). By evaluating a pulse against a set of common classification techniques we show that accelerator failure can be identified prior to actually failing with almost 80% accuracy. We also show that tuning classifier parameters and using pulse properties for refining datasets can further lead to almost 92% accuracy in classification of bad pulses. Most importantly, in the paper we establish there is information about the failure encoded in the pulses prior to it, so we also present a list of feasible next steps for increasing pulse classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Predicting particle accelerator failures using binary classifiers

Reščič

Seviour

Blokland

2020

Nuclear Instruments and Methods in Physics Research Section A:

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“…The main limiting factor for the stable operating gradients of the SNS SRF cavities is related to the thermal load from electrons due to field emission and multipacting [15,16]. In the SNS SCL, the cryogenic load from electron activities is equal or slightly higher than the thermal loads from BCS resistance at 2.1 K and residual resistance.…”

Section: Stable Operating Gradientsmentioning

confidence: 99%

Overview of ten-year operation of the superconducting linear accelerator at the Spallation Neutron Source

Kim

Afanador

Barnhart

et al. 2017

Nuclear Instruments and Methods in Physics Research Section A:

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The Spallation Neutron Source (SNS) has acquired extensive operational experience of a pulsed proton superconducting linear accelerator (SCL) as a user facility. Numerous lessons have been learned in its first 10 years operation to achieve a stable and reliable operation of the SCL. In this paper, an overview of the SNS SCL design, qualification of superconducting radio frequency (SRF) cavities and ancillary subsystems, an overview of the SNS cryogenic system, the SCL operation including SCL output energy history and downtime statistics, performance stability of the SRF cavities, efforts for SRF cavity performance recovery and improvement at the SNS, and maintenance activities for cryomodules are introduced.

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“…An 800 MeV H − linac design has been produced [457], following the designs of the SNS [458] and European Spallation Source (ESS) [459] linacs. The initial 74.8 MeV stage is based around the 324 MHz frequency of the 2.5 MW peak power Toshiba klystron used in the J-PARC linac [460].…”

Section: B 800 Mev Linac Studiesmentioning

confidence: 99%