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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.
Anil K. Prinja, Erin D. Fichtl
Nuclear Science and Engineering | Volume 155 | Number 3 | March 2007 | Pages 441-448
Technical Paper | Mathematics and Computation, Supercomputing, Reactor Physics and Nuclear and Biological Applications | doi.org/10.13182/NSE07-A2675
Articles are hosted by Taylor and Francis Online.
An iterative solution of coupled standard model equations arising in electron transport in binary statistical mixtures is considered. Convergence degradation is observed in certain energy groups and is attributed to chunk sizes appearing optically thin in the higher energy groups. Fourier analysis shows that the spectral radius approaches unity for the zero wave-number error mode as the chunk sizes become vanishingly small. It is shown that the atomic mix model accurately approximates transport under these circumstances and moreover provides a suitable low-order approximation to the iteration error. Fourier analysis and numerical implementation confirm that atomic mix acceleration is unconditionally effective for the application considered here. Our computations also demonstrate the inaccuracy of the atomic mix model for electron dose, especially for materials with strongly contrasting physical properties.