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Title Application of Machine Learning to Predict the Response of the Liquid Mercury Target at the Spallation Neutron Source
  • L. Lin, S. Gorti, J.C. Mach, H. Tran, D.E. Winder
    ORNL, Oak Ridge, Tennessee, USA
Abstract The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory is currently the most powerful accelerator-driven neutron source in the world. The intense proton pulses strike on SNS’s mercury target to provide bright neutron beams, which also leads to severe fluid-structure interactions inside the target. Prediction of resultant loading on the target is difficult particularly when helium gas is intentionally injected into mercury to reduce the loading and mitigate the pitting damage on the target’s internal walls. Leveraging the power of machine learning and the measured target strain, we have developed machine learning surrogates for modeling the discrepancy between simulations and experimental strain data. We then employ these surrogates to guide the refinement of the high-fidelity mercury/helium mixture model to predict a better match of target strain response.
Funding Basic Energy Sciences U.S. Department of Energy SC-22/Germantown Building 1000 Independence Avenue., SW Washington, DC 20585 P: (301) 903 - 3081 F: (301) 903 - 6594
Paper download WEPAB292.PDF [1.409 MB / 4 pages]
Poster download WEPAB292_POSTER.PDF [0.930 MB]
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Conference IPAC2021
Series International Particle Accelerator Conference (12th)
Location Campinas, SP, Brazil
Date 24-28 May 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-214-1
Online ISSN 2673-5490
Received 19 May 2021
Accepted 02 July 2021
Issue Date 10 August 2021
DOI doi:10.18429/JACoW-IPAC2021-WEPAB292
Pages 3340-3343
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.