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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.
Daisuke Kawasaki, Joonhong Ahn
Nuclear Technology | Volume 163 | Number 1 | July 2008 | Pages 137-146
Technical Paper | High-Level Radioactive Waste Management | doi.org/10.13182/NT08-A3977
Articles are hosted by Taylor and Francis Online.
A method that utilizes a time-domain random-walk model with residence time distributions (RTDs) for radionuclides in a compartment has been developed and applied to a safety assessment model for geologic disposal of high-level radioactive wastes. By choosing a proper RTD, which can be determined by a detailed model for radionuclide transport in a compartment, the present compartment model can simulate radionuclide transport through a repository region without numerical dispersion due to coarse discretization. The method has been demonstrated and illustrated for the case that the physical transport processes in a compartment and the corresponding RTD are known. For an actual performance assessment for a geologic repository, in which multiple waste packages are placed in an array configuration, it is considered that the repository-scale transport simulation can be greatly modularized and simplified by obtaining an RTD around a single package.