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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.
Richard D. Peters, Urban P. Jenquin, Langdon K. Holton, Jr.
Nuclear Technology | Volume 90 | Number 1 | April 1990 | Pages 78-86
Technical Paper | Radioactive Waste Management | doi.org/10.13182/NT90-A34387
Articles are hosted by Taylor and Francis Online.
Measurement and prediction of outside surface exposure rates and measurement of gamma photon spectra from radioactive sources are described. The sources were 30-cm-diam canisters filled with borosilicate glass to a height of ∼100 cm. Each canister contained up to 237 kCi of 137 Cs and 159 kCi of 90Sr. Exposure rates on the outside surfaces ranged from 26 000 to 320 000 R/h, chiefly from decay of 137 Cs. The radiation field around the canisters was modeled using two codes based on point kernel theory (ISOSHLD-II and QAD-CG) and a transport theory code (ANISN). It was found that the point kernel codes overpredicted surface exposure rates for the radioactive canisters by a factor of ∼2. The surface exposure rates calculated by the transport theory code were ∼25% higher than the measurements. Spectral measurements indicate that most exposure is associated with gamma radiation in the 0.1- to 0.5-MeV range.