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Growth beyond megawatts
Hash Hashemianpresident@ans.org
When talking about growth in the nuclear sector, there can be a somewhat myopic focus on increasing capacity from year to year. Certainly, we all feel a degree of excitement when new projects are announced, and such announcements are undoubtedly a reflection of growth in the field, but it’s important to keep in mind that growth in nuclear has many metrics and takes many forms.
Nuclear growth—beyond megawatts—also takes the form of increasing international engagement. That engagement looks like newcomer countries building their nuclear sectors for the first time. It also looks like countries with established nuclear sectors deepening their connections and collaborations. This is one of the reasons I have been focused throughout my presidency on bringing more international members and organizations into the fold of the American Nuclear Society.
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.