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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.
Hyung Jin Shim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 177 | Number 2 | June 2014 | Pages 184-192
Technical Paper | doi.org/10.13182/NSE13-29
Articles are hosted by Taylor and Francis Online.
It is very time-consuming to obtain a high-precision Monte Carlo (MC) estimate of the fuel temperature reactivity coefficient (FTC) through direct subtraction of two reactivity values from MC calculations at two different fuel temperatures. As an alternative to the direct subtraction MC estimate of the FTC, this paper presents a new method based on the adjoint-weighted correlated sampling technique. The new method translates the change in fuel temperature as the corresponding changes in both the microscopic cross sections and the transfer probabilities in scattering kernels described by the free gas model. The effectiveness of the new method is examined through continuous-energy MC neutronics calculations for pressurized water reactor pin cell and CANDU pressurized heavy water reactor lattice problems. The isotope-wise and reaction-type–wise contributions to the FTCs in the two problems are examined for two free gas models: the constant-cross-section and the resonance-cross-section models. It is demonstrated that the new MC method can predict the reactivity change due to fuel temperature variation as accurately as the conventional, more time-consuming direct subtraction MC method.