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NRC unveils Part 53 final rule
The Nuclear Regulatory Commission has finalized its new regulatory framework for advanced reactors that officials believe will accelerate, simplify, and reduce burdens in the new reactor licensing process.
The final rule arrives more than a year ahead of an end-of-2027 deadline set in the Nuclear Energy Innovation and Modernization Act (NEIMA), the 2019 law that formally directed the NRC to develop a new, technology-inclusive regulatory approach. The resulting rule—10 CFR Part 53, “Risk-Informed, Technology-Inclusive Regulatory Framework for Advanced Reactors”—is commonly referred to as Part 53.
Mohammad Albati, Tatsuya Sakurahara, Seyed Reihani, Ernie Kee, Jaemin Yang, Terry von Thaden, Richard Kesler, Farzaneh Masoud, Zahra Mohaghegh
Nuclear Science and Engineering | Volume 199 | Number 3 | March 2025 | Pages 445-464
Research Article | doi.org/10.1080/00295639.2024.2366735
Articles are hosted by Taylor and Francis Online.
In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation.
To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model.
Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions.
The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.