Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation

Dhulipala, Som Lakshmi Narasimha; Shields, Michael D.; Chakroborty, Promit; Jiang, Wen; Spencer, Benjamin W.; Hales, Jason D.; Laboure, Vincent M.; Prince, Zachary M.; Bolisetti, Chandrakanth; Che, Yifeng

doi:10.1016/j.ress.2022.108693

Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation

Journal Article · Wed Jul 06 00:00:00 EDT 2022 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2022.108693· OSTI ID:1887585

^[1]; Shields, Michael D. ^[2]; Chakroborty, Promit ^[2]; ^[1]; ^[1]; ^[1]; ^[1]; Prince, Zachary M. ^[1]; ^[1]; Che, Yifeng ^[1]

Idaho National Lab. (INL), Idaho Falls, ID (United States)
Johns Hopkins Univ., Baltimore, MD (United States)

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. Here we used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Office of Environment, Health, Safety and Security (AU), Office of Nuclear Safety; USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1887585

Report Number(s):: INL/JOU-21-65400-Rev000

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: 10 Vol. 226; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (46)

3.07 - TRISO-Coated Particle Fuel Performance Petti, D. A.; Demkowicz, P. A.; Maki, J. T. Comprehensive Nuclear Materials https://doi.org/10.1016/B978-0-08-056033-5.00055-0	book	February 2012
Estimation of small failure probabilities in high dimensions by subset simulation Au, Siu-Kui; Beck, James L. Probabilistic Engineering Mechanics, Vol. 16, Issue 4 https://doi.org/10.1016/S0266-8920(01)00019-4	journal	October 2001
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
Multifidelity importance sampling Peherstorfer, Benjamin; Cui, Tiangang; Marzouk, Youssef Computer Methods in Applied Mechanics and Engineering, Vol. 300 https://doi.org/10.1016/j.cma.2015.12.002	journal	March 2016
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
Ceramic nuclear fuel fracture modeling with the extended finite element method Jiang, Wen; Spencer, Benjamin W.; Dolbow, John E. Engineering Fracture Mechanics, Vol. 223 https://doi.org/10.1016/j.engfracmech.2019.106713	journal	January 2020
Novel algorithm using Active Metamodel Learning and Importance Sampling: Application to multiple failure regions of low probability Razaaly, Nassim; Congedo, Pietro Marco Journal of Computational Physics, Vol. 368 https://doi.org/10.1016/j.jcp.2018.04.047	journal	September 2018
Multifidelity probability estimation via fusion of estimators Kramer, Boris; Marques, Alexandre Noll; Peherstorfer, Benjamin Journal of Computational Physics, Vol. 392 https://doi.org/10.1016/j.jcp.2019.04.071	journal	September 2019
A generalized approximate control variate framework for multifidelity uncertainty quantification Gorodetsky, Alex A.; Geraci, Gianluca; Eldred, Michael S. Journal of Computational Physics, Vol. 408 https://doi.org/10.1016/j.jcp.2020.109257	journal	May 2020
The challenges associated with high burnup, high temperature and accelerated irradiation for TRISO-coated particle fuel Maki, John T.; Petti, David A.; Knudson, Darrell L. Journal of Nuclear Materials, Vol. 371, Issue 1-3 https://doi.org/10.1016/j.jnucmat.2007.05.019	journal	September 2007
Multidimensional multiphysics simulation of nuclear fuel behavior Williamson, R. L.; Hales, J. D.; Novascone, S. R. Journal of Nuclear Materials, Vol. 423, Issue 1-3 https://doi.org/10.1016/j.jnucmat.2012.01.012	journal	April 2012
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
TRISO particle fuel performance and failure analysis with BISON Jiang, Wen; Hales, Jason D.; Spencer, Benjamin W. Journal of Nuclear Materials, Vol. 548 https://doi.org/10.1016/j.jnucmat.2021.152795	journal	May 2021
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
Improvement of the BISON U 3 Si 2 modeling capabilities based on multiscale developments to modeling fission gas behavior Gamble, K. A.; Pastore, G.; Cooper, M. W. D. Journal of Nuclear Materials, Vol. 555 https://doi.org/10.1016/j.jnucmat.2021.153097	journal	November 2021
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
Accelerated statistical failure analysis of multifidelity TRISO fuel models Dhulipala, Somayajulu L. N.; Jiang, Wen; Spencer, Benjamin W. Journal of Nuclear Materials, Vol. 563 https://doi.org/10.1016/j.jnucmat.2022.153604	journal	May 2022
MCMC algorithms for Subset Simulation Papaioannou, Iason; Betz, Wolfgang; Zwirglmaier, Kilian Probabilistic Engineering Mechanics, Vol. 41 https://doi.org/10.1016/j.probengmech.2015.06.006	journal	July 2015
REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis Zhang, Xufang; Wang, Lei; Sørensen, John Dalsgaard Reliability Engineering & System Safety, Vol. 185 https://doi.org/10.1016/j.ress.2019.01.014	journal	May 2019
An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation Zhang, Jinhao; Xiao, Mi; Gao, Liang Reliability Engineering & System Safety, Vol. 188 https://doi.org/10.1016/j.ress.2019.03.002	journal	August 2019
Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses Menz, Morgane; Gogu, Christian; Dubreuil, Sylvain Reliability Engineering & System Safety, Vol. 196 https://doi.org/10.1016/j.ress.2019.106771	journal	April 2020
Reliability analysis with stratified importance sampling based on adaptive Kriging Xiao, Sinan; Oladyshkin, Sergey; Nowak, Wolfgang Reliability Engineering & System Safety, Vol. 197 https://doi.org/10.1016/j.ress.2020.106852	journal	May 2020
Formulation of A Risk Assessment Framework Capable of Analyzing Nuclear Power Multiunit Accident Scenarios Cai, Yinan; Golay, Michael W. Reliability Engineering & System Safety, Vol. 202 https://doi.org/10.1016/j.ress.2020.107040	journal	October 2020
Extension of AK-MCS for the efficient computation of very small failure probabilities Razaaly, Nassim; Congedo, Pietro Marco Reliability Engineering & System Safety, Vol. 203 https://doi.org/10.1016/j.ress.2020.107084	journal	November 2020
Fragility analysis and probabilistic performance evaluation of nuclear containment structure subjected to internal pressure Jin, Song; Gong, Jinxin Reliability Engineering & System Safety, Vol. 208 https://doi.org/10.1016/j.ress.2020.107400	journal	April 2021
Systematic approach and mathematical development for conditional core damage probabilities under station blackout of a nuclear power plant Kim, Man Cheol Reliability Engineering & System Safety, Vol. 217 https://doi.org/10.1016/j.ress.2021.107969	journal	January 2022
Seismic fragility analysis of nuclear power plants considering structural parameter uncertainty Zhao, Yan-Gang; Qin, Miao-Jun; Lu, Zhao-Hui Reliability Engineering & System Safety, Vol. 216 https://doi.org/10.1016/j.ress.2021.107970	journal	December 2021
AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method Ameryan, Ala; Ghalehnovi, Mansour; Rashki, Mohsen Reliability Engineering & System Safety, Vol. 217 https://doi.org/10.1016/j.ress.2021.108036	journal	January 2022
A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis Yi, Jiaxiang; Cheng, Yuansheng; Liu, Jun Reliability Engineering & System Safety, Vol. 219 https://doi.org/10.1016/j.ress.2021.108247	journal	March 2022
Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method Zhou, Tong; Peng, Yongbo Reliability Engineering & System Safety, Vol. 220 https://doi.org/10.1016/j.ress.2021.108283	journal	April 2022
Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs Xiong, Qingwen; Du, Peng; Deng, Jian Reliability Engineering & System Safety, Vol. 221 https://doi.org/10.1016/j.ress.2022.108337	journal	May 2022
An adaptive parallel learning dependent Kriging model for small failure probability problems Zhan, Hongyou; Xiao, Ning-Cong; Ji, Yuxiang Reliability Engineering & System Safety, Vol. 222 https://doi.org/10.1016/j.ress.2022.108403	journal	June 2022
Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations Zheng, Xiaoyu; Tamaki, Hitoshi; Sugiyama, Tomoyuki Reliability Engineering & System Safety, Vol. 223 https://doi.org/10.1016/j.ress.2022.108503	journal	July 2022
MOOSE: Enabling massively parallel multiphysics simulation Permann, Cody J.; Gaston, Derek R.; Andrš, David SoftwareX, Vol. 11 https://doi.org/10.1016/j.softx.2020.100430	journal	January 2020
AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation Echard, B.; Gayton, N.; Lemaire, M. Structural Safety, Vol. 33, Issue 2 https://doi.org/10.1016/j.strusafe.2011.01.002	journal	March 2011
Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation Huang, Xiaoxu; Chen, Jianqiao; Zhu, Hongping Structural Safety, Vol. 59 https://doi.org/10.1016/j.strusafe.2015.12.003	journal	March 2016
Hamiltonian Monte Carlo methods for Subset Simulation in reliability analysis Wang, Ziqi; Broccardo, Marco; Song, Junho Structural Safety, Vol. 76 https://doi.org/10.1016/j.strusafe.2018.05.005	journal	January 2019
AK-MSS: An adaptation of the AK-MCS method for small failure probabilities Xu, Chunlong; Chen, Weidong; Ma, Jingxin Structural Safety, Vol. 86 https://doi.org/10.1016/j.strusafe.2020.101971	journal	September 2020
Rare Event Estimation Using Polynomial-Chaos Kriging Schöbi, R.; Sudret, B.; Marelli, S. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 3, Issue 2 https://doi.org/10.1061/AJRUA6.0000870	journal	June 2017
BISON: A Flexible Code for Advanced Simulation of the Performance of Multiple Nuclear Fuel Forms Williamson, Richard L.; Hales, Jason D.; Novascone, Stephen R. Nuclear Technology https://doi.org/10.1080/00295450.2020.1836940	journal	March 2021
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Perdikaris, P.; Raissi, M.; Damianou, A. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 473, Issue 2198 https://doi.org/10.1098/rspa.2016.0751	journal	February 2017
On weakest link theory and Weibull statistics Zok, Frank W. Journal of the American Ceramic Society, Vol. 100, Issue 4 https://doi.org/10.1111/jace.14665	journal	January 2017
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization Peherstorfer, Benjamin; Willcox, Karen; Gunzburger, Max SIAM Review, Vol. 60, Issue 3 https://doi.org/10.1137/16M1082469	journal	January 2018
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions Bichon, B. J.; Eldred, M. S.; Swiler, L. P. AIAA Journal, Vol. 46, Issue 10 https://doi.org/10.2514/1.34321	journal	October 2008
Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection Ranftl, Sascha; Melito, Gian Marco; Badeli, Vahid Entropy, Vol. 22, Issue 1 https://doi.org/10.3390/e22010058	journal	December 2019
Cladding Profilometry Analysis of Experimental Breeder Reactor-II Metallic Fuel Pins with HT9, D9, and SS316 Cladding Paaren, Kyle M.; Lybeck, Nancy; Mo, Kun Energies, Vol. 14, Issue 2 https://doi.org/10.3390/en14020515	journal	January 2021

Similar Records

Active learning with multifidelity modeling for efficient rare event simulation

Journal Article · Fri Jul 29 00:00:00 EDT 2022 · Journal of Computational Physics · OSTI ID:1887215

Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel

Conference · Sun Feb 12 23:00:00 EST 2023 · OSTI ID:2433833

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

Conference · Fri Jun 23 00:00:00 EDT 2023 · OSTI ID:2396330

Related Subjects

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS
97 MATHEMATICS AND COMPUTING
Gaussian process
Monte Carlo
TRISO fuel
active learning
deep neural networks
nuclear fuel
reliability
variance reduction

Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation

Citation Formats

References (46)

Similar Records

Related Subjects