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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.
A. C. Kahler
Nuclear Science and Engineering | Volume 145 | Number 2 | October 2003 | Pages 213-224
Technical Paper | doi.org/10.13182/NSE03-A2377
Articles are hosted by Taylor and Francis Online.
Continuous-energy Monte Carlo eigenvalue calculations have been performed for a selection of HEU-MET-FAST, IEU-MET-FAST, HEU-SOL-THERM, LEU-COMP-THERM, and LEU-SOL-THERM benchmarks using ENDF/B (primarily VI.8), JEFF-3.0, and JENDL-3.3 cross sections. These benchmarks allow for testing the cross-section data for both common reactor nuclides such as 1H, 16O, and 235,238U and structural and shielding elements such as Al, Ti, Fe, Ni, and Pb. The latest cross-section libraries yield near-unity eigenvalues for unreflected or water-reflected HEU-SOL-THERM and LEU-SOL-THERM systems. Near-unity eigenvalues are also obtained for bare HEU-MET-FAST and IEU-MET-FAST systems, but small deviations from unity are observed in both FAST and THERM benchmarks as a function of nonhydrogenous reflector material and thickness. The long-standing problem of lower eigenvalues in water-reflected low-enriched-uranium fuel lattice systems remains, regardless of cross-section library.