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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.
Taro Ueki
Nuclear Science and Engineering | Volume 171 | Number 3 | July 2012 | Pages 220-230
Technical Paper | doi.org/10.13182/NSE11-35
Articles are hosted by Taylor and Francis Online.
An orthonormally weighted standardized time series (OWSTS) was investigated for the statistical error estimation of local tallies in Monte Carlo criticality calculation. Unlike the original implementation of a standardized time series, the computation of standard deviation via OWSTS can be made free of the grouping of iteration cycles into batches. The characteristic aspect of OWSTS is the application of an arbitrary number of weighting functions to a standardized series of tallies such that asymptotically independent and unbiased estimates are produced based on the statistics of Brownian bridge. In the present work, a trigonometric set of weighting functions is extended and applied to local power tallies in the three-dimensional model of a pressurized water reactor core. Numerical results demonstrate that the OWSTS error estimation is unbiased for a sufficiently large number of iteration cycles.