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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.
Edward P. Ficaro, David K. Wehe
Nuclear Science and Engineering | Volume 117 | Number 3 | July 1994 | Pages 158-176
Technical Paper | doi.org/10.13182/NSE94-A28531
Articles are hosted by Taylor and Francis Online.
The KENO-NR Monte Carlo code was developed to simulate the measurement of R(ω) = G*12(ω)G13(ω)/G11(ω)G23(ω), a ratio of spectral densities measured by the 252Cf source-driven noise analysis (CSDNA) method for determining subcriticality. From a direct comparison of simulated and measured R(ω), cross sections and the physical system model can be benchmarked and then used in standard criticality codes for determining keff for a multiplying system. This procedure eliminates the dependence of the CSDNA method on the point-kinetics model and allows cross-section and geometry models to be validated for noncritical configurations. For a set of uranium cylinders (93.2 wt% 235sU and 17.7-cm outer diameter) of varying height, the simulated and the measured R(ω) values in the low-frequency limit and the prompt neutron decay constant a agreed to within 10%. These results indicate that the approach of validating a simulation of the direct experimental data should lead to improved neutronic parameters for fissile systems.
