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
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
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
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)