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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.
E. E. Bende, A. H. Hogenbirk, J. L. Kloosterman, H. van Dam
Nuclear Science and Engineering | Volume 133 | Number 2 | October 1999 | Pages 147-162
Technical Paper | doi.org/10.13182/NSE99-A2078
Articles are hosted by Taylor and Francis Online.
An analytical expression was derived for the average Dancoff factor of a fuel kernel (Cfk) in a pebble of a high-temperature gas-cooled reactor. This Dancoff factor accounts for the probability that a neutron escaping from a fuel kernel enters another fuel kernel, in the same pebble or in other pebbles, without colliding with a moderator nucleus in between. If the fuel zone of the pebble is thought to be of infinite dimensions, the Dancoff factor becomes equal to the so-called infinite-medium Dancoff factor Cfk. The Cfk has been determined by the evaluation of three existing analytical expressions and by two Monte Carlo calculations performed with the MCNP-4A code, for various coated-particle densities. The Dancoff factor Cfk can be written as Cfk times a correction factor. The latter has been calculated for different fuel zone radii and pebble shell thicknesses. For the standard pebble, Cfk as a function of the number of coated particles has been calculated both analytically and with MCNP. The results of both methods are in good agreement. The analytical calculation method is preferred because it consumes practically no CPU time and obviates the building of MCNP models.