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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.
Tohru Mitsutake, Shigeaki Tsunoyama, Shigeru Kanemoto, Hideaki Namba, Shirley A. Sandoz
Nuclear Technology | Volume 65 | Number 3 | June 1984 | Pages 365-373
Technical Paper | Fission Reactor | doi.org/10.13182/NT84-A33391
Articles are hosted by Taylor and Francis Online.
The multivariable autoregressive (AR) model identification technique has been applied in the study of the boiling water reactor core stability test analysis. It has been demonstrated through the analysis of core stability tests performed at the Peach Bottom-2 reactor, so that the AR model technique is effective in estimating core stability performance. Neutron flux to dome pressure open-loop stability performance is estimated by two methods, the ordinary correlation method and the AR model technique. Results obtained by both methods are in good agreement. The AR model technique can provide closed-loop decay ratios. This kind of decay ratio is considered to represent the actual core stability characteristic. Based on these test analysis results, the closed-loop in-reactor characteristic is more stable than the open-loop characteristic, which is usually considered to be the stability index for the reactor core. It was attempted to evaluate error in the AR model technique through indirect ways. It has been concluded that the AR model technique for the stability test data analysis is quantitatively highly effective in identifying and evaluating the core stability characteristics.