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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.
K. S. Smith, K. R. Rempe
Nuclear Science and Engineering | Volume 100 | Number 3 | November 1988 | Pages 324-331
Technical Paper | doi.org/10.13182/NSE88-A29046
Articles are hosted by Taylor and Francis Online.
The QPANDA nodal models, which are embodied in the SIMULA TE-3 code, have been extensively tested and benchmarked. Comparisons to quarter-core PDQ depletion calculations demonstrate the high degree of accuracy with which power distributions are predicted, even though SIMULA TE-3 contains no user-adjusted normalizations. The QPANDA pin power reconstruction model is introduced, and comparisons (versus CASMO colorset and PDQ quarter-core calculations) demonstrate that accurate pin power distributions are obtained by modulating the intranodal power distributions with single-assembly CASMO pin power distributions. Comparisons of SIMULATE-3 calculations to measured reactor fission rate integrals are presented. Also, the overall accuracy of the CASMO-3 cross sections, the QPANDA nodal model, and the QPANDA pin power reconstruction model is demonstrated.