IPAC2021 - List of Authors (Winder, D.E.)

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 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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WEPAB367	Bubble Generation in the SNS 2 MW Mercury Target	3567
	C.N. Barbier, M.P. Costa, K.C. Johns, D. Ottinger, F. Rasheed, B.W. Riemer, R.L. Sangrey, J.R. Weinmeister, D.E. Winder ORNL, Oak Ridge, Tennessee, USA
	The accelerator at the Spallation Neutron Source is currently being upgraded to increase the proton beam power from 1.4 MW to 2.8 MW. About 2 MW will go to the first target station, while the rest will go to the future second target station. The first target station uses a mercury target. When the short proton beam pulse hits it, strong pressure waves are developed inside the mercury and the vessel itself, causing weld failures and cavitation erosion. The pressure wave can be significantly mitigated by injecting small helium bubbles into the mercury. SNS has been injecting helium since 2017 using small orifices but has met challenges in fabrication and operations with them. Thus, for the 2 MW target, swirl bubblers will be used to increase gas injection and improve reliability. A 2 MW prototypical target was built and tested in a mercury process loop available at Oak Ridge National Laboratory. Acrylic viewports on the top of the target were used to determine the bubble size distribution (BSD) generated by the swirl bubblers. It was found that the bubblers were not only capable of generating small bubbles but that the BSD was independent of gas injection rate.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB367
About •	paper received ※ 10 May 2021 paper accepted ※ 22 June 2021 issue date ※ 20 August 2021
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THXC03	Evolution of the High-Power Spallation Neutron Mercury Target at the SNS	3735
	D.E. Winder ORNL, Oak Ridge, Tennessee, USA
	Funding: UT-Battelle, LLC, under Grant DE-AC05-00OR22725 with the US Department of Energy (DOE). The Spallation Neutron Source (SNS) began operation in 2006 and first operated at its full 1.4 MW power in 2013. Targets, which receive the pulsed proton beam, were a limiting factor for reliable full power operation for several years. Reaching reliable target operation at 1.4 MW required not only changes to the target design but also support and coordination across the entire SNS enterprise. The history and some key lessons learned are presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXC03
About •	paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 01 September 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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 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

Export •

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

WEPAB367

Bubble Generation in the SNS 2 MW Mercury Target

3567

C.N. Barbier, M.P. Costa, K.C. Johns, D. Ottinger, F. Rasheed, B.W. Riemer, R.L. Sangrey, J.R. Weinmeister, D.E. Winder
ORNL, Oak Ridge, Tennessee, USA

The accelerator at the Spallation Neutron Source is currently being upgraded to increase the proton beam power from 1.4 MW to 2.8 MW. About 2 MW will go to the first target station, while the rest will go to the future second target station. The first target station uses a mercury target. When the short proton beam pulse hits it, strong pressure waves are developed inside the mercury and the vessel itself, causing weld failures and cavitation erosion. The pressure wave can be significantly mitigated by injecting small helium bubbles into the mercury. SNS has been injecting helium since 2017 using small orifices but has met challenges in fabrication and operations with them. Thus, for the 2 MW target, swirl bubblers will be used to increase gas injection and improve reliability. A 2 MW prototypical target was built and tested in a mercury process loop available at Oak Ridge National Laboratory. Acrylic viewports on the top of the target were used to determine the bubble size distribution (BSD) generated by the swirl bubblers. It was found that the bubblers were not only capable of generating small bubbles but that the BSD was independent of gas injection rate.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB367

About •

paper received ※ 10 May 2021 paper accepted ※ 22 June 2021 issue date ※ 20 August 2021

Export •

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

THXC03

Evolution of the High-Power Spallation Neutron Mercury Target at the SNS

3735

D.E. Winder
ORNL, Oak Ridge, Tennessee, USA

Funding: UT-Battelle, LLC, under Grant DE-AC05-00OR22725 with the US Department of Energy (DOE).
The Spallation Neutron Source (SNS) began operation in 2006 and first operated at its full 1.4 MW power in 2013. Targets, which receive the pulsed proton beam, were a limiting factor for reliable full power operation for several years. Reaching reliable target operation at 1.4 MW required not only changes to the target design but also support and coordination across the entire SNS enterprise. The history and some key lessons learned are presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXC03

About •

paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 01 September 2021

Export •

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