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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.
Felix C. Difilippo
Nuclear Science and Engineering | Volume 142 | Number 2 | October 2002 | Pages 140-149
Technical Paper | doi.org/10.13182/NSE02-A2294
Articles are hosted by Taylor and Francis Online.
The analysis of the fluctuations of signals coming from detectors in the vicinity of a subcritical assembly of fissile materials is commonly used for the control and safeguard of nuclear materials and might be used for the surveillance of an accelerator driven system. One of the stochastic techniques is the measurement of the probability distributions of counts in time intervals t (gates); the departure of the ratio of the variance and the mean value with respect to 1 (the correlation) is directly related to the amount of fissile material and its subcriticality. The measurement of this correlation is affected by dead-time effects due to count losses because of the finite-time resolution of the detection system. We present a theory that allows (a) the calculation of the probability of losing n counts (P(n)) in gate t, (b) the definition of experimental conditions under which P(2) << P(1), and (c) a methodology to correct the measured correlation because of losing one count in any gate. The theory is applied to the analysis of experiments performed in a highly enriched subcritical assembly.