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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.
Adam Rau, James Turso (Penn State)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 1352-1360
Maintaining system stability is an important task for operators of nuclear reactors. Automatic control can be a valuable tool in achieving this, however many modern control methods require a model of the system. These models may not account for changes in system parameters resulting from continued use or different operating points. Robust control techniques can account for such uncertainties, guaranteeing stability provided that plant parameters and dynamics remain within specified uncertainty bounds. The present work applies robust control methods to design a controller for the Penn State Breazeale Reactor (PSBR). The impact of wear on the control rod drive mechanism and changes resulting from different reactor power set points are considered. A Simulink model of the PSBR from [1] was linearized and simplified for the purpose of controller design. The model uses the normalized point-kinetics equations and core-averaged thermal-hydraulics. H? control synthesis methods were used to accommodate changes in power level and wear in the control rod drive mechanisms. System stability was verified on a Simulink model, and controller performance was compared to a proportional controller.