Accelerated statistical failure analysis of multifidelity TRISO fuel models

Dhulipala, Somayajulu L.N.; Jiang, Wen; Spencer, Benjamin W.; Hales, Jason D.; Shields, Michael D.; Slaughter, Andrew E.; Prince, Zachary M.; Labouré, Vincent M.; Bolisetti, Chandrakanth; Chakroborty, Promit

doi:10.1016/j.jnucmat.2022.153604

Accelerated statistical failure analysis of multifidelity TRISO fuel models

Journal Article · Mon Feb 21 23:00:00 EST 2022 · Journal of Nuclear Materials

DOI:https://doi.org/10.1016/j.jnucmat.2022.153604· OSTI ID:1867181

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

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.

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); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1867181

Alternate ID(s):: OSTI ID: 1960564

Report Number(s):: INL/JOU-21-65126-Rev000

Journal Information:: Journal of Nuclear Materials, Journal Name: Journal of Nuclear Materials Journal Issue: - Vol. 563; ISSN 0022-3115

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Accelerated statistical failure analysis of multifidelity TRISO fuel models

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