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Application of Machine Learning to Predict the Response of the Liquid Mercury Target at the Spallation Neutron Source...

by Lianshan Lin, Sarma B Gorti, Justin C Mach, Hoang A Tran, Drew E Winder
Publication Type
Conference Paper
Journal Name
Joint Accelerator Conferences Website Proceedings
Book Title
Proceedings of the 12th International Particle Accelerator Conference
Publication Date
Page Numbers
3340 to 3343
Publisher Location
Conference Name
12th International Particle Accelerator Conference - IPAC’21
Conference Location
Iguassu Falls (virtual meeting), Brazil
Conference Sponsor
Brazilian Center for Research in Energy and Materials (CNPEM)
Conference Date

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.