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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.
Ki-Seob Sim, Ho Chun Suk, Young Ku Yoon
Nuclear Technology | Volume 99 | Number 3 | September 1992 | Pages 351-365
Technical Paper | Nuclear Fuel Cycle | doi.org/10.13182/NT92-A34719
Articles are hosted by Taylor and Francis Online.
The KFGR-T computer model has been developed to predict transient fission gas release from UO2fuel with an emphasis on the nonequilibrium behavior of fission gas bubbles. It takes into account the relevant physical processes generally considered by other workers, as well as migration of fission gas bubbles through channels formed by the extension of dislocations to grain boundaries during the transient heatup stage, grain growth/grain-boundary sweeping during the isothermal annealing stage at high temperatures, and gas release through intergranular cracking. This computer model is applied to calculate transient fission gas releases, and the calculated values are compared with the results of out-of-pile experiments performed with UO2 fuel base-irradiated to burnups in the range of 18 to 35 MW·d/kg U. The absolute values and the trends of the fission gas releases calculated with the KFGR-T model are in good agreement with the experimental data. A parametric study is also done to investigate the sensitivity of the model to variables such as initial grain size, heating rate, temperature gradient, and initial gas concentration, and these results are compared with the sensitivity of other models.