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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.
Ulrich Fischer, Y. Oyama, F. Maekawa, C. Konno, M. Wada, C. Ichihara, Y. Makita, A. Takahashi, K. Ueki, K. Kosako, K. Hayashi, M. Youssef, H. Hunter, C. Slater, U. Fischer, F. Kappler, E. Stein, H. Tsige-Tamirat, E. Wiegner, Karlsruhe, P. Batistoni, L. Petrizzi, V. Rado, L. Benmansour, A. Santamarina, K. Seidel, A. Blokhin, S. P. Simakov, V. Sinitsa, D. Markovskij
Fusion Science and Technology | Volume 30 | Number 3 | December 1996 | Pages 1093-1100
Neutronics Experiments and Analyses | doi.org/10.13182/FST96-A11963096
Articles are hosted by Taylor and Francis Online.
A co-ordinated international benchmark validation task has been conducted to validate the Fusion Evaluated Nuclear Data Library FENDL-1 through data tests against integral 14-MeV neutron experiments. The main objective of this task was to qualify the FENDL-1 working libraries for fusion applications and to elaborate recommendations for further data improvements. A large variety of existing integral 14 MeV benchmark experiments has been analysed with the FENDL-1 working libraries for continuous energy Monte Carlo and multigroup discrete ordinates calculations. This paper reviews the FENDL-1 benchmark analyses and gives an overview on the major findings, conclusions and recommendations for further data improvements to be included in the forthcoming FENDL-2 data library.