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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.
J. E. Kinsey
Fusion Science and Technology | Volume 48 | Number 2 | October 2005 | Pages 1060-1071
Technical Paper | DIII-D Tokamak - Achieving Reactor Quality Plasma Confinement | doi.org/10.13182/FST05-A1060
Articles are hosted by Taylor and Francis Online.
During the past decade, there has been significant progress made in our predictive understanding of turbulent transport in tokamaks. Theoretical advances have led to the development of comprehensive theoretical transport models based on drift wave physics. This paper summarizes the development of the GLF23 drift wave transport model, its application to modeling of DIII-D experiments, and burning plasma projections. The model predicts the transport due to ion temperature gradient, trapped electron, and electron temperature gradient modes and includes the effects of E × B shear flow and Shafranov shift stabilization. GLF23 has been successful in predicting the core profiles in a wide variety of discharges. Examples of published results are given along with a discussion of some outstanding physics issues.