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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.
Michio Murase, Yoichi Utanohara, Takayoshi Kusunoki, Yasunori Yamamoto, Dirk Lucas, Akio Tomiyama
Nuclear Technology | Volume 197 | Number 2 | February 2017 | Pages 140-157
Technical Paper | doi.org/10.13182/NT16-96
Articles are hosted by Taylor and Francis Online.
We proposed prediction methods for countercurrent flow limitation (CCFL) in horizontal and slightly inclined pipes with one-dimensional (1-D) computations and uncertainty of computed CCFL. In this study, we applied the proposed methods to a full-scale pressurizer surge line [inclination angle θ = 0.6 deg, diameter D = 300 mm, and ratio of the length to the diameter (L/D) = 63] in a specific pressurized water reactor, performed 1-D computations and three-dimensional (3-D) numerical simulations, and found that uncertainties caused by effects of the diameter and fluid properties on CCFL were small. We also applied the proposed methods to experiments for hot-leg and surge line models (θ = 0 and 0.6 deg, D = 0.03 to 0.65 m, and L/D = 4.5 to 63) to generalize them, performed 1-D computations, and found that uncertainties caused by effects of θ and L on CCFL were large due to the setting error for θ and differences among experiments. This shows that a small-scale air-water experiment with the same θ and L/D as those in an actual plant is effective to reduce the uncertainty of CCFL prediction.