Author: Lin, L.
Paper Title Page
WEPAB292 Application of Machine Learning to Predict the Response of the Liquid Mercury Target at the Spallation Neutron Source 3340
 
  • L. Lin, S. Gorti, J.C. Mach, H. Tran, D.E. Winder
    ORNL, Oak Ridge, Tennessee, USA
 
  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
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.
 
poster icon Poster WEPAB292 [0.930 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB292  
About • paper received ※ 19 May 2021       paper accepted ※ 02 July 2021       issue date ※ 10 August 2021  
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