Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station

Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station

The reliability of the mercury spallation target is mission-critical for the neutron science program of the spallation neutron source at the Oak Ridge National Laboratory. We present an inverse uncertainty quantification (UQ) study using the Bayesian framework for the mercury equation of state model...

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Bibliographic Details
Main Authors: Majdi I. Radaideh, Lianshan Lin, Hao Jiang, Sarah Cousineau
Format: Article
Language:English
Published: Elsevier 2022-05-01
Series:Results in Physics
Subjects:
Bayesian statistics
Inverse problems
Markov chain Monte Carlo
Polynomial chaos expansions
Spallation neutron source
Mercury target
Online Access:http://www.sciencedirect.com/science/article/pii/S2211379722001759

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http://www.sciencedirect.com/science/article/pii/S2211379722001759

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