The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{lin:ipac2021-wepab292,
author = {L. Lin and S. Gorti and J.C. Mach and H. Tran and D.E. Winder},
title = {{Application of Machine Learning to Predict the Response of the Liquid Mercury Target at the Spallation Neutron Source}},
booktitle = {Proc. IPAC'21},
pages = {3340--3343},
eid = {WEPAB292},
language = {english},
keywords = {target, neutron, simulation, proton, experiment},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB292},
url = {https://jacow.org/ipac2021/papers/wepab292.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB292},
abstract = {{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.}},
}