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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.
A.R. Raffray, Z.R. Gorbis, M.A. Abdou
Fusion Science and Technology | Volume 19 | Number 3 | May 1991 | Pages 1525-1531
ITER | Proceedings of the Ninth Topical Meeting on the Technology of Fusion Energy (Oak Brook, Illinois, October 7-11, 1990) | doi.org/10.13182/FST91-A29558
Articles are hosted by Taylor and Francis Online.
Several transport mechanisms are involved in tritium transport in solid breeders—diffusion in the grain, diffusion along grain boundary, bulk adsorption on the grain boundary/pore interface, desorption to the pores, diffusion along interconnected porosities and convection by the purge flow. It is generally thought that two of the most rate controlling mechanisms are diffusion in the grain and desorption at the grain boundary/pore interface. However, depending on the breeder microstructure, diffusion through the pore can also significantly affect the overall tritium transport process. These three mechanisms are considered here, and the key parameters affecting the tritium transport rate by each mechanism are characterized. Grain diffusion and desorption are first compared, and multi-parameter plots showing regions of diffusion and desorption controls are derived for cases of purge flow with and without hydrogen addition. Grain diffusion is then compared to pore diffusion and the effect of the solid breeder microstructure on the pore diffusion coefficient is discussed. Finally, the resulting equations and plots are applied to experimental data from the LISA1 and TRIO experiments to evaluate the rate-controlling mechanisms.