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Discrete-Time Bayesian Networks Applied to Flexible Coping Strategies of Nuclear Power Plant Systems

Elvan Sahin, Victor C. Leite, Kyung M. Kim, Nick Burns, Juliana Pacheco Duarte

Nuclear Science and Engineering | Volume 197 | Number 11 | November 2023 | Pages 2800-2817

PSA 2021 Paper | doi.org/10.1080/00295639.2022.2151300

Received:May 16, 2022

Accepted:November 19, 2022

Published:October 2, 2023

Articles are hosted by Taylor and Francis Online.

The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risk in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by the Nuclear Energy Institute to manage the challenges of BDBEEs and enhance reactor safety. Due to the uniqueness of the FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. In this study, we investigate the effectiveness and applicability of both Bayesian networks (BNs) and discrete-time Bayesian networks in the reliability analysis of FLEX equipment. The study compares BNs with two other reliability assessment methods: fault tree and Markov chain. These methods are also shown to be capable of mapping into BNs to perform a reliability analysis of FLEX systems. A neutral dependency algorithm is used to simplify the conditional probability tables and reduce the complexity of the BNs. The results indicate that BNs are not only a powerful method for modeling FLEX strategies but are also effective techniques for inclusion of the dynamics of FLEX equipment in probabilistic risk analysis.