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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.
Toshikazu Takeda, Kazuhisa Matsumoto
Nuclear Science and Engineering | Volume 114 | Number 1 | May 1993 | Pages 64-75
Technical Paper | doi.org/10.13182/NSE93-A24015
Articles are hosted by Taylor and Francis Online.
A formula for the uncertainty of core performance parameters based on a combination of the cross-section adjustment and bias factor methods has been derived. The formula is compared with those derived from the cross-section adjustment method and the bias factor method used separately. When the method error correlation is strong between the critical assemblies and a target core, the combined method is superior to the cross-section adjustment method used alone. The combined method is, in general, superior to the bias factor method used alone. Numerical results are presented for the uncertainties of keff, the control rod worth, and the power distribution of a large fast reactor. The combined method yields a smaller uncertainty for the control rod worth calculated in dollar units than the cross-section adjustment method used alone.