Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Dhulipala, Somayajulu Lakshmi Narasimha; Toptan, Aysenur; Che, Yifeng; Schwen, Daniel; Sweet, Ryan Terrence; Hales, Jason D.; Novascone, Stephen R.

doi:10.1016/j.jnucmat.2023.154790

Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Journal Article · Tue Oct 24 00:00:00 EDT 2023 · Journal of Nuclear Materials

DOI:https://doi.org/10.1016/j.jnucmat.2023.154790· OSTI ID:2274764

^[1]; ^[2]; ^[2]; ^[2]; ^[2]; ^[2]; ^[2]

Idaho National Laboratory (INL), Idaho Falls, ID (United States); Idaho State Univ., Pocatello, ID (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Tristructural isotropic (TRISO) particle fuel is one of the most promising fuel concepts enabling high temperature and high burnup reactor operation. One dominant source of radioactivity released from the TRISO particles is silver (Ag), which is subject to a high release fraction and long decay life compared to other fission products. Previous modeling efforts using the fuel performance code BISON indicated nonnegligible uncertainties in modeling the diffusion process of fission products in TRISO compared to the Advanced Gas Reactor experiments. The overall uncertainties observed when modeling the fission product diffusion can result from uncertainties in model parameters, noisy experimental measurements, and deficiencies in the developed models. The three types of underlying uncertainties have not yet been properly quantified in open literature. Here, this paper presents the Bayesian uncertainty quantification (UQ) using massively parallelizable Markov chain Monte Carlo samplers. The uncertainties due to model parameters, model inadequacy, and experimental measurement noise are quantified, with the σ term used to represent the sum of the model inadequacy and measurement noise uncertainties. It is worth noting that this is the first time the σ term is inferred for nuclear fuel experiments, as compared to using prescribed values for uncertainty quantification in previous work. The parallelizable Markov chain Monte Carlo samplers efficiently infer the model parameters and the σ term, giving insight into physical parameters like diffusion coefficients and the combined model discrepancy and measurement noise. A subsequent forward uncertainty quantification (UQ) is also performed based on the calibration results to generate more accurate predictions of the Ag release. The model inadequacy plus experimental noise is the most dominant source of uncertainty compared to the parametric uncertainty. All the UQ analyses presented in this work are based on the second series of the irradiation experiments in the Advanced Gas Reactor program.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE), Nuclear Energy Advanced Modeling and Simulation (NEAMS)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 2274764

Report Number(s):: INL/JOU--23-73653-Revision-0

Journal Information:: Journal of Nuclear Materials, Journal Name: Journal of Nuclear Materials Vol. 588; ISSN 0022-3115

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (24)

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces Braak, Cajo J. F. Ter Statistics and Computing, Vol. 16, Issue 3 https://doi.org/10.1007/s11222-006-8769-1	journal	September 2006
The DOE advanced gas reactor fuel development and qualification program Petti, David; Maki, John; Hunn, John JOM, Vol. 62, Issue 9 https://doi.org/10.1007/s11837-010-0140-5	journal	September 2010
Making best use of model evaluations to compute sensitivity indices Saltelli, Andrea Computer Physics Communications, Vol. 145, Issue 2 https://doi.org/10.1016/S0010-4655(02)00280-1	journal	May 2002
Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release Che, Yifeng; Wu, Xu; Pastore, Giovanni Annals of Nuclear Energy, Vol. 153 https://doi.org/10.1016/j.anucene.2020.108046	journal	April 2021
Bayesian calibration with summary statistics for the prediction of xenon diffusion in UO2 nuclear fuel Robbe, Pieterjan; Andersson, David; Bonnet, Luc Computational Materials Science, Vol. 225 https://doi.org/10.1016/j.commatsci.2023.112184	journal	June 2023
Subset simulation for problems with strongly non-Gaussian, highly anisotropic, and degenerate distributions Shields, Michael D.; Giovanis, Dimitris G.; Sundar, V. S. Computers & Structures, Vol. 245 https://doi.org/10.1016/j.compstruc.2020.106431	journal	March 2021
Multidimensional multiphysics simulation of TRISO particle fuel Hales, J. D.; Williamson, R. L.; Novascone, S. R. Journal of Nuclear Materials, Vol. 443, Issue 1-3 https://doi.org/10.1016/j.jnucmat.2013.07.070	journal	November 2013
Coated particle fuel: Historical perspectives and current progress Demkowicz, Paul A.; Liu, Bing; Hunn, John D. Journal of Nuclear Materials, Vol. 515 https://doi.org/10.1016/j.jnucmat.2018.09.044	journal	March 2019
Modeling fission product diffusion in TRISO fuel particles with BISON Hales, Jason D.; Jiang, Wen; Toptan, Aysenur Journal of Nuclear Materials, Vol. 548 https://doi.org/10.1016/j.jnucmat.2021.152840	journal	May 2021
Numerical evaluation of AGR-2 fission product release Hales, Jason D.; Toptan, Aysenur; Jiang, Wen Journal of Nuclear Materials, Vol. 558 https://doi.org/10.1016/j.jnucmat.2021.153325	journal	January 2022
Efficient high-fidelity TRISO statistical failure analysis using Bison: Applications to AGR-2 irradiation testing Jiang, Wen; Singh, Gyanender; Hales, Jason D. Journal of Nuclear Materials, Vol. 562 https://doi.org/10.1016/j.jnucmat.2022.153585	journal	April 2022
Mechanistic calculation of the effective silver diffusion coefficient in polycrystalline silicon carbide: Application to silver release in AGR-1 TRISO particles Simon, P. -C. A.; Aagesen, Larry K.; Jiang, Chao Journal of Nuclear Materials, Vol. 563 https://doi.org/10.1016/j.jnucmat.2022.153669	journal	May 2022
Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory Wu, Xu; Kozlowski, Tomasz; Meidani, Hadi Nuclear Engineering and Design, Vol. 335 https://doi.org/10.1016/j.nucengdes.2018.06.004	journal	August 2018
A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes Wu, Xu; Xie, Ziyu; Alsafadi, Farah Nuclear Engineering and Design, Vol. 384 https://doi.org/10.1016/j.nucengdes.2021.111460	journal	December 2021
Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under Bayesian statistics Radaideh, Majdi I.; Borowiec, Katarzyna; Kozlowski, Tomasz Reliability Engineering & System Safety, Vol. 189 https://doi.org/10.1016/j.ress.2019.04.020	journal	September 2019
MOOSE Stochastic Tools: A module for performing parallel, memory-efficient in situ stochastic simulations Slaughter, Andrew E.; Prince, Zachary M.; German, Peter SoftwareX, Vol. 22 https://doi.org/10.1016/j.softx.2023.101345	journal	May 2023
Differential Evolution: A survey of theoretical analyses Opara, Karol R.; Arabas, Jarosław Swarm and Evolutionary Computation, Vol. 44 https://doi.org/10.1016/j.swevo.2018.06.010	journal	February 2019
High-dimensional posterior exploration of hydrologic models using multiple-try DREAM _(ZS) and high-performance computing : EFFICIENT MCMC FOR HIGH-DIMENSIONAL PROBLEMS Laloy, Eric; Vrugt, Jasper A. Water Resources Research, Vol. 48, Issue 1 https://doi.org/10.1029/2011WR010608	journal	January 2012
SAPIUM: A Generic Framework for a Practical and Transparent Quantification of Thermal-Hydraulic Code Model Input Uncertainty Baccou, Jean; Zhang, Jinzhao; Fillion, Philippe Nuclear Science and Engineering, Vol. 194, Issue 8-9 https://doi.org/10.1080/00295639.2020.1759310	journal	June 2020
Globally Centered Autocovariances in MCMC Agarwal, Medha; Vats, Dootika Journal of Computational and Graphical Statistics, Vol. 31, Issue 3 https://doi.org/10.1080/10618600.2022.2037433	journal	March 2022
RUN DMC: AN EFFICIENT, PARALLEL CODE FOR ANALYZING RADIAL VELOCITY OBSERVATIONS USING N -BODY INTEGRATIONS AND DIFFERENTIAL EVOLUTION MARKOV CHAIN MONTE CARLO Nelson, Benjamin; Ford, Eric B.; Payne, Matthew J. The Astrophysical Journal Supplement Series, Vol. 210, Issue 1 https://doi.org/10.1088/0067-0049/210/1/11	journal	December 2013
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability Arendt, Paul D.; Apley, Daniel W.; Chen, Wei Journal of Mechanical Design, Vol. 134, Issue 10 https://doi.org/10.1115/1.4007390	journal	September 2012
Ensemble samplers with affine invariance Goodman, Jonathan; Weare, Jonathan Communications in Applied Mathematics and Computational Science, Vol. 5, Issue 1 https://doi.org/10.2140/camcos.2010.5.65	journal	January 2010

Similar Records

Unpacking model inadequacy: The quantification of silver release from TRISO fuel by considering empirical and mechanistic approaches

Journal Article · Thu Apr 03 20:00:00 EDT 2025 · Journal of Nuclear Materials · OSTI ID:2583246

Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches

Conference · Tue Apr 08 20:00:00 EDT 2025 · OSTI ID:3028803

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

Technical Report · Sun Jun 30 20:00:00 EDT 2024 · OSTI ID:2438438

Related Subjects

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS
97 MATHEMATICS AND COMPUTING
TRISO fuel
forward uncertainty quantification
inverse uncertainty quantification
model inadequacy
parallelized Bayesian inference
parametric uncertainty

Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Citation Formats

References (24)

Similar Records

Related Subjects