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Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Shigeki Shiba, Daiki Iwahashi, Tsuyoshi Okawa
Nuclear Technology | Volume 209 | Number 8 | August 2023 | Pages 1154-1163
Research Article | doi.org/10.1080/00295450.2023.2191588
Articles are hosted by Taylor and Francis Online.
From the viewpoint of criticality management in the fuel debris retrieval operation at the Fukushima Daiichi Nuclear Power Station, it is important in criticality safety analyses to consider the behavior of fuel debris particles as they fall into the water, given that the neutron moderation condition of the fuel debris can dramatically change. In this study, we evaluated a reactivity insertion while fuel debris particles dropped into the water. Specifically, we considered the effects of the fuel debris particle-size distribution in either an erroneous operation or a postulated accident in the fuel debris retrieval operation. Three types of fuel debris particle-size distribution were assumed: monodisperse, uniform, and Rosin-Rammler. The behaviors of the fuel debris particles during sedimentation were evaluated using the coupled Distinct Element Method–Moving Particle Simulation (DEM-MPS) code. The multiplication factors corresponding to the behaviors of the falling fuel debris were calculated by a continuous-energy Monte Carlo code MVP3.0 with JENDL-4.0. Consequently, the multiplication factors changed with the particle motions during the sedimentation, and the trends of the multiplication factors differed between the particle-size distributions. Especially, the 2-cm monodisperse particle-size distribution showed the highest multiplication factor during sedimentation, the trend of which differed from the others in the fuel debris particles dispersing and piled-up phases in the water.