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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.
R. A. Bonalumi, N. P. Kherani
Nuclear Science and Engineering | Volume 90 | Number 1 | May 1985 | Pages 47-59
Technical Paper | doi.org/10.13182/NSE85-A17430
Articles are hosted by Taylor and Francis Online.
A unique processing of in-core flux detector data is described and demonstrated, such that the detailed in-core power distribution can be derived with great accuracy by combining a specially “smoothed-out” set of in-core data with neutron diffusion theory. Rational Mapping (RAM) is designed in such a way that erratic detector signals are recognized very efficiently and can be eliminated from the experimental data set: This is achieved by modal expansion of the difference between theoretical fluxes and experimental fluxes at the detector sites. Sensitivity studies have shown that RAM is quite stable, does not absorb the “wild” detector errors in the mapping procedure, and results in mapped fluxes with errors about three times smaller than would be obtained by direct interpolation of detector readings. A new method is described to infer corrections to theoretical core parameters based on the difference between the RAM fluxes and the theoretical fluxes.