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Nuclear Energy Strategy announced at CNA2026
At the Canadian Nuclear Association Conference (CNA2026) in Ottawa, Ontario, on April 29, Minister of Energy and Natural Resources Tim Hodgson announced that Natural Resources Canada (NRCan) is developing a new Nuclear Energy Strategy for the country. The strategy, which is slated to be released by the end of this year, will be based on four objectives: 1) enabling new nuclear builds across Canada, 2) being a global supplier and exporter of nuclear technology and services, 3) expanding uranium production and nuclear fuel opportunities, and 4) developing new Canadian nuclear innovations, including in both fission and fusion technologies.
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.