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

• Published in: Results in physics Vol. 36; no. C; p. 105414
• Main Authors: Radaideh, Majdi I., Lin, Lianshan, Jiang, Hao, Cousineau, Sarah
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2022; Elsevier
• Subjects: Bayesian statistics; Inverse problems; Markov chain Monte Carlo; Mercury target; Polynomial chaos expansions; Spallation neutron source; Polynomial chaos expansions; Inverse problems; Spallation neutron source; Mercury target; Bayesian statistics; Markov chain Monte Carlo
• ISSN: 2211-3797; 2211-3797
• DOI: 10.1016/j.rinp.2022.105414

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 parameters, with the assistance of polynomial chaos expansion surrogate models. By leveraging high-fidelity structural mechanics simulations and real measured strain data, the inverse UQ results reveal a tight posterior distribution for the tensile cutoff threshold, the mercury density, and the mercury speed of sound. The updated distributions do not necessarily represent the nominal mercury physical properties, but the ones that fit the strain data and the solid mechanics model we have used, and can be explained by three reasons. First, the limitations of the computer model or what is known as the “model-form uncertainty” that would result from numerical methods and physical approximations. Second, is the biases and errors in the experimental data. Third, is the mercury cavitation damage that also contributes to the change in mercury behavior. Consequently, the mercury equation of state model parameters try to compensate for these effects to improve fitness to the real data. The mercury target simulations using the posterior parametric values result in an excellent agreement with 88% average accuracy compared to experimental data, 6% average increase compared to reference parameters, with some sensors experiencing an increase of more than 25%. With a more accurate strain response predicted by the calibrated simulations, the component fatigue analysis can utilize the comprehensive strain history data to evaluate the target vessel’s lifetime closer to its real limit, saving tremendous target cost and improving the design of future targets as well. •Bayesian inverse uncertainty quantification is applied to accelerator physics.•Physical model parameters of the liquid mercury target are calibrated.•The calibrated simulations can be used for fatigue analysis of the mercury target.•Improvement in strain prediction can be observed against sensor measurements.