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The DOE’s plan for AI in NRC licensing
The Department of Energy announced the completion of a proof-of-concept demonstration of the use of Everstar’s AI tool to generate chapter 5 of an NRC license application from preliminary safety documents.
The 208-page document was created by the AI tool in approximately one day. According to the DOE, it would typically take a team of people between four and six weeks to complete this work.
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.