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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.
J. C. Robinson, F. Shahrokhi, R. C. Kryter
Nuclear Technology | Volume 40 | Number 1 | August 1978 | Pages 47-51
Technical Paper | Reactor | doi.org/10.13182/NT78-A26698
Articles are hosted by Taylor and Francis Online.
A method was developed for quantitative inference of core barrel motion from the following statistical descriptors using an analytically derived scale factor (SF): cross-power spectral density (CPSD), auto-power spectral density, and amplitude probability density (APD). The proper frequency band over which to compute the statistical descriptors is conveniently determined from the coherence and phase information from two ex-core power range neutron monitors positioned diametrically across the reactor vessel Core barrel motion can then be quantified from the integral of the band-limited CPSD of two diametrically opposed excore monitors or, if the coherence between the pair is ≳0.7, from a properly band-limited APD function. Wide-band APD and CPSD functions were both demonstrated to yield erroneous estimates for the magnitude of core barrel motion.