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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.
Katsuhiro Sakai, Satoru Sugawara, Hisashi Hishida, Tetsuo Kobori
Nuclear Science and Engineering | Volume 91 | Number 3 | November 1985 | Pages 262-278
Technical Paper | doi.org/10.13182/NSE85-A17303
Articles are hosted by Taylor and Francis Online.
A method to predict the probabilistic distribution of channel coolant flow rate was developed for a boiling-water-cooled, pressure-tube-type reactor. This method deals with the probabilistic deviation of core flow distribution and total coolant flow rate based on the characteristics of the correlation between two-phase pressure drop of a primary core cooling system and the characteristics of the recirculation pump Q-H. The effect of local and global uncertainties on the probabilistic variation of channel coolant flow rate is discussed in terms of coolant flow correlation among all of the pressure tube channels. The probabilistic deviation of channel coolant flow rate due to uncertainties in fabrication tolerances, experimental data, and physical properties has been evaluated for various operating conditions of the FUGEN reactor. Predicted channel flow deviations were in good agreement with the deviation of actual measured data in the FUGEN reactor.