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OECD NEA project gets ahead of AI use in nuclear industry
The OECD Nuclear Energy Agency’s International Regulatory Laboratory (RegLab) Project, which brings together experts from across the nuclear field to examine the potential impact of emerging technologies, has released a report on its first cycle that details the outcomes of a RegLab focused on the use of artificial intelligence in real-time monitoring of nuclear power plants.
Participants started out with an initial problem/opportunity statement, from which they developed a use case and a mock safety, security, safeguards, and environmental protection (SSSE) case. Then, over the course of two workshops, participants considered these cases in depth.
Ryota Katano, Akito Oizumi, Masahiro Fukushima, Cheol Ho Pyeon, Akio Yamamoto, Tomohiro Endo
Nuclear Science and Engineering | Volume 198 | Number 6 | June 2024 | Pages 1215-1234
Research Article | doi.org/10.1080/00295639.2023.2246779
Articles are hosted by Taylor and Francis Online.
In this study, we have demonstrated that data assimilation (DA) using lead and bismuth sample reactivities measured in the Kyoto University Critical Assembly A-core can successfully reduce the uncertainty of the coolant void reactivity in accelerator-driven systems (ADSs) derived from inelastic scattering cross sections of lead and bismuth. We reevaluated and highlighted the experimental uncertainties and correlations of the sample reactivities for the DA formula. We used the MCNP6.2 code to evaluate the sample reactivities and their uncertainties and performed DA using the reactor analysis code system MARBLE. The high-sensitivity coefficients of the sample reactivities to lead and bismuth allowed us to reduce the cross-section–induced uncertainty of the void reactivity of the ADS from 6.3% to 4.8%, achieving a provisional target accuracy of 5% in this study. Furthermore, we demonstrated that the uncertainties arising from other dominant factors, such as minor actinides and steel, can be effectively reduced by using integral experimental data sets for the unified cross-section dataset ADJ2017.