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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.
Jesson D. Hutchinson, John D. Bess
Nuclear Science and Engineering | Volume 163 | Number 3 | November 2009 | Pages 285-290
Technical Paper | doi.org/10.13182/NSE163-285
Articles are hosted by Taylor and Francis Online.
Subcritical measurements were conducted with an -phase plutonium sphere reflected by nickel hemishells using the 252Cf source-driven noise analysis method to provide criticality safety benchmark data. Measured configurations included a bare plutonium sphere as well as the plutonium sphere reflected by the following nickel thicknesses: 1.27, 2.54, 3.81, 5.08, and 7.62 cm. A certain ratio of spectral quantities was measured for each configuration, which varies linearly with the keff of the system under small perturbations. In addition, two types of Monte Carlo calculations were employed: a modified version of MCNP to calculate the ratio of spectral quantities and a KCODE calculation. From the measured and computed quantities, the effective multiplication factor of each configuration can be approximated. The inferred keff for all six configurations compared well with computed values. A comprehensive uncertainty analysis was then performed that includes uncertainties in the geometry and materials present in the system in addition to the uncertainties in the method and nuclear data.