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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.
C. H. Lee, Y. J. Kim, J. W. Song, C. O. Park
Nuclear Science and Engineering | Volume 124 | Number 1 | September 1996 | Pages 160-166
Technical Paper | doi.org/10.13182/NSE96-A24231
Articles are hosted by Taylor and Francis Online.
The spectral history problem encountered in reconstructing local homogeneous power distributions is investigated. Because of difficulties in most nodal codes concerning spectral interactions between neighboring assemblies when rebuilding the local power distribution, nodal codes assume a constant spectrum or do not properly consider local spectrum variations within an assembly. A simple, fuel-type-independent method is presented to eliminate the spectrum-induced errors from local homogeneous powers within an assembly over the entire burnup range. The method, which is generalized for its application to any fuel type in the entire assembly burnup domain, uses the proportional relationship between macroscopic cross sections and average spectral history indices. Verification results through embedded calculations and an actual core calculation show that local homogeneous power errors are reduced to the same magnitude as flux errors. The error reduction is conspicuous in the cases of mixed-oxide and highly poisoned fuel assemblies.