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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.
Jin Li, Volkan Seker, Andrew Ward, Thomas Downar
Nuclear Science and Engineering | Volume 199 | Number 5 | May 2025 | Pages 772-792
Research Article | doi.org/10.1080/00295639.2024.2397621
Articles are hosted by Taylor and Francis Online.
Monte Carlo codes have become increasingly popular for generating homogenized few-group cross-section data, especially for advanced reactor designs that have complex geometries and nontraditional compositions. However, the stochastic nature of Monte Carlo processes has the potential to introduce additional statistical uncertainties in the overall uncertainty in the prediction of core behavior. The work performed in this research quantified the additional uncertainty introduced by the use of Monte Carlo multigroup cross sections into the analysis of graphite-moderated pebble bed reactors. In this research, the objective was achieved by performing uncertainty quantification for the key output parameters in deterministic steady-state and transient safety calculations. The results show that when the homogenized multigroup cross sections are generated with a sufficient number of neutron histories in the Monte Carlo calculation, the uncertainties in the subsequent deterministic simulations caused by the Monte Carlo cross-section uncertainty are negligible compared to the contributions from the uncertainties of other input parameters.