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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.
Seongchan Kim, Han Gyu Joo
Nuclear Science and Engineering | Volume 197 | Number 8 | August 2023 | Pages 1564-1583
Technical papers from: PHYSOR 2022 | doi.org/10.1080/00295639.2022.2144083
Articles are hosted by Taylor and Francis Online.
The capability and performance of the hexagonal version of the nTRACER direct whole-core calculation code are enhanced for VVER applications by extending the geometry-handling features and also by implementing assemblywise parallelization of the planar method of characteristics (MOC) calculation with higher-order scattering. The geometry-handling methods for the VVER hexagonal geometry having various special constituents are presented with detailed illustrations. The assemblywise domain decomposition (ADD) scheme is established under the hexagonal coarse-mesh finite difference formulation, which is exploited to update the incoming angular flux needed for the ADD parallelization. The solution accuracy and parallel performance are assessed for various hexagonal core problems, including the VVER benchmarks. It is shown that the hexagonal geometry solutions of nTRACER match with the reference Monte Carlo solutions within about 50 pcm in reactivity and 1% in pin power distribution and that the hexagonal ADD can reduce the computing time of the planar MOC calculation by up to 53% when compared to the anglewise parallelization.