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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.
João Moreira, John C. Lee
Nuclear Science and Engineering | Volume 98 | Number 3 | March 1988 | Pages 244-254
Technical Paper | doi.org/10.13182/NSE88-A22325
Articles are hosted by Taylor and Francis Online.
Control rod worth measurements through the inverse kinetics equation depend on accurate determination of the amplitude function from detector signals. The modal-local method introduced in a previous study estimates space-time changes in the flux or shape function so that the amplitude function can be determined accurately and efficiently. A simple thermal-hydraulic feedback model is included in the modal-local method for at-power reactivity analysis. The method is tested with two simulated rod worth measurements: a zero-power rod drop experiment and a differential rod worth measurement in a power reactor. The modal-local method reproduces the reactivity obtained with the FX2-TH time-dependent diffusion theory code with an overall accuracy of 1 to 2%, except for simulated detectors located in the immediate vicinity of the rod motion.