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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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:
Online Access:http://www.sciencedirect.com/science/article/pii/S2211379722001759
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author Majdi I. Radaideh
Lianshan Lin
Hao Jiang
Sarah Cousineau
author_facet Majdi I. Radaideh
Lianshan Lin
Hao Jiang
Sarah Cousineau
author_sort Majdi I. Radaideh
collection DOAJ
description 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.
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spelling doaj.art-b64bcbf592fc42758fe175301e4f94612022-12-21T18:59:57ZengElsevierResults in Physics2211-37972022-05-0136105414Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target stationMajdi I. Radaideh0Lianshan Lin1Hao Jiang2Sarah Cousineau3Corresponding author.; Spallation Neutron Source, Oak Ridge National Laboratory, 8600 Spallation Dr, Oak Ridge, TN 37830, United States of AmericaSpallation Neutron Source, Oak Ridge National Laboratory, 8600 Spallation Dr, Oak Ridge, TN 37830, United States of AmericaSpallation Neutron Source, Oak Ridge National Laboratory, 8600 Spallation Dr, Oak Ridge, TN 37830, United States of AmericaSpallation Neutron Source, Oak Ridge National Laboratory, 8600 Spallation Dr, Oak Ridge, TN 37830, United States of AmericaThe 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.http://www.sciencedirect.com/science/article/pii/S2211379722001759Bayesian statisticsInverse problemsMarkov chain Monte CarloPolynomial chaos expansionsSpallation neutron sourceMercury target
spellingShingle Majdi I. Radaideh
Lianshan Lin
Hao Jiang
Sarah Cousineau
Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
Results in Physics
Bayesian statistics
Inverse problems
Markov chain Monte Carlo
Polynomial chaos expansions
Spallation neutron source
Mercury target
title Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
title_full Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
title_fullStr Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
title_full_unstemmed Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
title_short Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
title_sort bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
topic Bayesian statistics
Inverse problems
Markov chain Monte Carlo
Polynomial chaos expansions
Spallation neutron source
Mercury target
url http://www.sciencedirect.com/science/article/pii/S2211379722001759
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AT lianshanlin bayesianinverseuncertaintyquantificationofthephysicalmodelparametersforthespallationneutronsourcefirsttargetstation
AT haojiang bayesianinverseuncertaintyquantificationofthephysicalmodelparametersforthespallationneutronsourcefirsttargetstation
AT sarahcousineau bayesianinverseuncertaintyquantificationofthephysicalmodelparametersforthespallationneutronsourcefirsttargetstation