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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.
Sergey S. Gorodkov
Nuclear Science and Engineering | Volume 172 | Number 2 | October 2012 | Pages 193-201
Technical Paper | doi.org/10.13182/NSE11-105
Articles are hosted by Taylor and Francis Online.
Significant underprediction bias in uncertainties of neutron flux is observed in Monte Carlo criticality calculations of large cores. It is universally recognized that this underprediction is closely associated with the ratio of the second-largest eigenvalue to the largest eigenvalue, or the dominance ratio, of the fission kernel. In this paper a close analogy is presumed between neutron flux autocorrelations in Monte Carlo calculations and flux variances due to stochastic uncertainties of the properties of fuel assemblies within the manufacturing tolerance limits. Interesting consequences following from this analogy are confirmed in quite realistic calculations. A useful expression is derived for fast evaluation of the minimal number of histories to be modeled to achieve preset confidence limits of flux distribution in large cores.