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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.
William L. Barr
Fusion Science and Technology | Volume 19 | Number 3 | May 1991 | Pages 498-502
Technical Paper | Plasma Engineering | doi.org/10.13182/FST91-A29390
Articles are hosted by Taylor and Francis Online.
A physics model is developed for estimating the principal parameters of the edge plasma in a large tokamak with a poloidal divertor. The model is essentially one-dimensional, but it includes transverse scale lengths that are derived from power balance. The model allows highly elongated magnetic configurations with either a double or a single null. The power flowing into the edge plasma, the power radiated from the edge plasma, and the power incident on the divertor are all assumed to be known. The plasma density at the separatrix is also assumed to be known. Equations developed from the model give the plasma temperature at the midplane separatrix, the plasma temperature and density at the divertor, and the transverse scale length for power flow in the edge plasma. The scaling relations for the plasma parameters and an expression for the peak heat flux at the divertor are derived. The basic assumption made in developing the model is that the transverse scale lengths can be mapped from one region in the edge plasma to another by the conservation of magnetic flux.