Keyword: electron
Paper Title Other Keywords Page
MOP01 Improvement of Capture Ratio for an X-Band Linac Based on Multi-Objective Genetic Algorithm cavity, linac, impedance, detector 18
 
  • J.Y. Li, T. Hu, J. Yang, B.Q. Zeng
    HUST, Wuhan, People’s Republic of China
  • H.G. Xu
    SINR, Jiading, Shanghai, People’s Republic of China
 
  Funding: This work was supported by National Natural Science Foundation of China (NSFC) under Project Numbers 11905074.
Electron linear accelerators with an energy of ~MeV are widely required in industrial applications. Whereas miniaturized accelerators, especially those working at X-band, attract more and more attention due to their compact structures and high gradients. Since the performance of a traveling wave (TW) accelerator is determined by its structures, considerable efforts must be made for structure optimization involving numerous and complex parameters. In this context, functional key parameters are obtained through deep analysis for structure and particle motion characteristics of the TW accelerator, then a multi-objective genetic algorithm (MOGA) is successfully applied to acquire an optimized phase velocity distribution which can contribute to achieving a high capture ratio and a low energy spread. Finally, a low-energy X-band TW tube used for rubber vulcanization is taken as an example to verify the reliability of the algorithm under a single-particle model. The capture ratio is 91.2%, while the energy spread is 5.19%, and the average energy is 3.1MeV.
 
video icon
        Right click on video for
Picture-in-Picture mode
or Full screen display.

At start the sound is muted!
 
poster icon Poster MOP01 [1.124 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2021-MOP01  
About • Received ※ 04 October 2021 — Revised ※ 18 October 2021 — Accepted ※ 18 December 2021 — Issued ※ 03 February 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
MOP09 HL-LHC Beam Dynamics with Hollow Electron Lenses optics, emittance, operation, simulation 59
 
  • P.D. Hermes, R. Bruce, R. De Maria, M. Giovannozzi, A. Mereghetti, D. Mirarchi, S. Redaelli
    CERN, Geneva, Switzerland
  • G. Stancari
    Fermilab, Batavia, Illinois, USA
 
  Each of the two proton beams in the High-Luminosity Large Hadron Collider (HL-LHC) will carry a total energy of 720 MJ. One concern for machine protection is the energy stored in the transverse beam tails, estimated to potentially reach up to 5% of the total stored energy. Several failure scenarios could drive these tails into the collimators, potentially causing damage and therefore severely affecting operational efficiency. Hollow Electron Lenses (HEL) were integrated in the HL-LHC baseline to mitigate this risk by depleting the tails in a controlled way. A hollow-shaped electron beam runs co-axially to the hadron beam over about 3 m, such that halo particles at large amplitudes become unstable, while core particles ideally remain undisturbed. Residual fields from e-beam asymmetries can, however, induce emittance growth of the beam core. Various options for the pulsing of the HEL are considered and are compared using two figures of merit: halo depletion efficiency and core emittance growth. This contribution presents simulations for these two effects with different HEL pulsing modes using the final HL-LHC optics, that was optimized at the location of the lenses.  
poster icon Poster MOP09 [0.970 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2021-MOP09  
About • Received ※ 06 October 2021 — Revised ※ 02 November 2021 — Accepted ※ 22 November 2021 — Issued ※ 19 January 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUEC2 Operational Experience with Nanocrystalline Injection Foils at SNS operation, injection, target, ECR 176
 
  • N.J. Evans
    ORNL RAD, Oak Ridge, Tennessee, USA
 
  Funding: SNS is managed by UT-Battelle, LLC, under contract DE- AC05-00OR22725 for the U.S. Department of Energy.
The Spallation Neutron Source (SNS) uses 300-400μ g/cm2 nanocrystalline diamond foils grown in-house at the Center for Nanophase Materials Sciences to facilitate charge exchange injection (CEI) from the 1 GeV H⁻ linac into the 248~m circumference accumulation ring. These foils have performed exceptionally well with lifetimes of thousands of MW·hrs. This contribution shares some experience with the operation of these foils during 1.4 MW operation, and discusses current operational concerns including injection related losses, foil conditioning, deformation, and sublimation due to high temperatures. The implications for the SNS Proton Power Upgrade are also discussed.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2021-TUEC2  
About • Received ※ 17 October 2021 — Revised ※ 21 October 2021 — Accepted ※ 23 November 2021 — Issued ※ 06 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUEC4 Test of Machine Learning at the CERN LINAC4 controls, linac, target, network 181
 
  • V. Kain, N. Madysa, P.K. Skowroński, I. Vojskovic
    CERN, Geneva, Switzerland
  • N. Bruchon
    University of Trieste, Trieste, Italy
  • S. Hirlaender, G. Valentino
    University of Malta, Information and Communication Technology, Msida, Malta
 
  The CERN H⁻ linear accelerator, LINAC4, served as a test bed for advanced algorithms during the CERN Long Shutdown 2 in the years 2019/20. One of the main goals was to show that reinforcement learning with all its benefits can be used as a replacement for numerical optimization and as a complement to classical control in the accelerator control context. Many of the algorithms used were prepared beforehand at the electron line of the AWAKE facility to make the best use of the limited time available at LINAC4. An overview of the algorithms and concepts tested at LINAC4 and AWAKE will be given and the results discussed.  
slides icon Slides TUEC4 [2.879 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2021-TUEC4  
About • Received ※ 07 October 2021 — Revised ※ 21 October 2021 — Accepted ※ 23 November 2021 — Issued ※ 19 December 2021
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)