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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.
Hiroshi Tochihara, Eiji Ochiai, Tadashi Hasegawa
Nuclear Technology | Volume 58 | Number 2 | August 1982 | Pages 310-317
Technical Paper | Analyse | doi.org/10.13182/NT82-A32939
Articles are hosted by Taylor and Francis Online.
The ex-core detector response in pressurized water reactors (PWRs) depends on not only power level but also core power distribution. Therefore, it is important to precisely calculate the assembly-wise spatial weighting factors for the ex-core detectors. Usually these factors are calculated with the one-dimensional transport code and point kernel calculational method, in which the neutron scattering effect outside of reactor vessel is neglected. But when the scattering effect is estimated to be rather big, we calculate the assembly-wise spatial weighting factors using the two-dimensional transport code, which includes the scattering effect. Consequently, we found that the weighting factors of peripheral assemblies that are remote from the detector but close to reactor vessel are rather big in comparison with the previous results. When we calculate the detector response during one control rod insertion test of three-loop PWR core using these weighting factors, the agreement between calculation and measurement is very good. A simple point kernel calculational method developed instead of the two-dimensional transport calculation that consumes much computer time.