Author: Radaideh, M.I.
Paper Title Page
WEPA37 Benchmarking and Exploring Parameter Space of the 2-Phase Bubble Tracking Model for Liquid Mercury Target Simulation 711
  • L. Lin, M.I. Radaideh, H. Tran, D.E. Winder
    ORNL, Oak Ridge, Tennessee, USA
  Funding: This project was funded by the U.S. DOE under grant DE-SC0009915.
High intensity proton pulses strike the Spallation Neutron Source (SNS)’s mercury target to provide bright neutron beams. These strikes deposit extensive energy into the mercury and its steel vessel. Prediction of the resultant loading on the target is difficult when helium gas is intentionally injected into the mercury to reduce the loading and to mitigate the pitting damage on the vessel. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. We present a study comparing the measured target strains in the SNS target station with the simulation results of the solid mechanics simulation framework. We investigate a wide range of various physical model parameters, including the number of bubble families, bubble size distribution, viscosity, surface tension, etc. to understand their impact on simulation accuracy. Our initial findings reveal that using 8-10 bubble families in the model renders a simulation strain envelope that covers the experimental ones. Further optimization studies are planned to predict the strain response more accurately.
poster icon Poster WEPA37 [1.985 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA37  
About • Received ※ 27 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 12 August 2022 — Issue date ※ 01 September 2022
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WEPA38 Progress on Machine Learning for the SNS High Voltage Converter Modulators 715
  • M.I. Radaideh, S.M. Cousineau, D. Lu
    ORNL, Oak Ridge, Tennessee, USA
  • T.J. Britton, K. Rajput, M. Schram, L.S. Vidyaratne
    JLab, Newport News, Virginia, USA
  • G.C. Pappas, J.D. Walden
    ORNL RAD, Oak Ridge, Tennessee, USA
  The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.  
poster icon Poster WEPA38 [1.320 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA38  
About • Received ※ 25 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 03 October 2022
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