TY - JOUR

T1 - Accelerated statistical failure analysis of multifidelity TRISO fuel models

AU - Dhulipala, Somayajulu L.N.

AU - Jiang, Wen

AU - Spencer, Benjamin W.

AU - Hales, Jason D.

AU - Shields, Michael D.

AU - Slaughter, Andrew E.

AU - Prince, Zachary M.

AU - Labouré, Vincent M.

AU - Bolisetti, Chandrakanth

AU - Chakroborty, Promit

N1 - Funding Information:
This research is supported through INL’s Laboratory Directed Research & Development (LDRD) program under DOE Idaho Operations Office Contract DE-AC07-05ID14517. This research made use of the resources of the High Performance Computing Center at INL, which is supported by the Office of Nuclear Energy of the U.S. DOE and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517.
Publisher Copyright:
© 2022 Elsevier B.V.

PY - 2022/5

Y1 - 2022/5

N2 - Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.

AB - Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.

KW - Bison

KW - Monte Carlo

KW - Nuclear fuel

KW - Reliability

KW - Risk

KW - Variance reduction

UR - http://www.scopus.com/inward/record.url?scp=85125278308&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f4c27594-6efb-3413-b703-89ee9452c3ee/

U2 - 10.1016/j.jnucmat.2022.153604

DO - 10.1016/j.jnucmat.2022.153604

M3 - Article

AN - SCOPUS:85125278308

SN - 0022-3115

VL - 563

JO - Journal of Nuclear Materials

JF - Journal of Nuclear Materials

M1 - 153604

ER -