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Denver, CO|Sheraton Denver
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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.
M. W. Mickael
Nuclear Science and Engineering | Volume 119 | Number 1 | January 1995 | Pages 34-43
Technical Paper | doi.org/10.13182/NSE95-A24069
Articles are hosted by Taylor and Francis Online.
A fast automated method is developed to estimate particle importance in the Los Alamos Monte Carlo code MCNP. It provides an automated and efficient way of predicting and setting up an important map for the weight windows technique. A short analog simulation is first performed to obtain effective group parameters based on the input description of the problem. A solution of the multigroup time-dependent adjoint diffusion equation is then used to estimate particle importance. At any point in space, time, and energy, the particle importance is determined, based on the calculated parameters, and used as the lower limit of the weight window. The method has been tested for neutron, photon, and coupled neutron-photon problems. Significant improvement in the simulation efficiency is obtained using this technique at no additional computer time and with no prior knowledge of the nature of the problem. Moreover, time and angular importance that are not available yet in MCNP are easily implemented in this method.