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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.
S. D. Clarke, S. A. Pozzi, E. Padovani, T. J. Downar
Nuclear Science and Engineering | Volume 160 | Number 3 | November 2008 | Pages 370-377
Technical Paper | doi.org/10.13182/NSE160-370
Articles are hosted by Taylor and Francis Online.
The most recent release of photonuclear interaction data for Monte Carlo applications is the ENDF/B-VII library. While this current version offers several improvements over its predecessors, it does not address the observed, sometimes quite significant variance in the measured data. For instance, for 238U, the cross-section data in the ENDF/B-VII library is consistently larger than all measurements except for those by Caldwell et al., occasionally by as much as 20%. The objective of the work performed here was to investigate the sensitivity of photoneutron production to perturbations in photonuclear cross-section data. The effect of these perturbations on experimental observables in a common setup was assessed using the MCNPX/MCNP-PoliMi code system. A new methodology was developed and implemented to evaluate the sensitivity of commonly measured parameters to perturbations in photonuclear cross-section data. The results of the analysis show that the maximum variation applied to the cross section (20%) results in an integral detector response change that in general varies between 6 and 8% for the exact configuration considered here. However, the methodology is general and may be readily applied to any source-target configuration.