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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.
F. J. Arias
Nuclear Science and Engineering | Volume 178 | Number 2 | October 2014 | Pages 240-249
Technical Paper | doi.org/10.13182/NSE13-88
Articles are hosted by Taylor and Francis Online.
The phenomenology for a particular behavior of packed beds in heavy liquid metal (HLM) fast reactors during postaccident heat removal is proposed. Because of the similar densities of the fuel and the HLM, an inherent passive safety self-removal feedback mechanism due to buoyancy forces is developed, which propels the packed bed away from the wall, thus preventing temperatures that can jeopardize the vessel’s structural integrity and also reducing the recriticality potential by limiting the allowable bed depth. This identified mechanism will have somewhat compensatory tendencies in the self-leveling behavior of debris beds, which are crucial for sodium-cooled reactors, but unfortunately, it is not operative for HLMs because of the absence of boiling of the coolant. By means of a simplified geometrical model, a preliminary analysis of the potentiality of the phenomenon has been performed.