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Title: Shape and Divertor Control in Tokamaks
Authors: Wai, Josiah
Advisors: Kolemen, Egemen
Contributors: Mechanical and Aerospace Engineering Department
Keywords: Divertor
Feedback control
Neural nets
Nuclear fusion
Shape control
Subjects: Plasma physics
Mechanical engineering
Applied physics
Issue Date: 2023
Publisher: Princeton, NJ : Princeton University
Abstract: As tokamak fusion reactors increase in size and power, robust and high performance technologies will be needed to perform tasks of magnetic control and high heat flux power exhaust. Advanced divertor strategies such as the X-divertor, snowflake, and X-point target divertor have been proposed as candidate solutions for mitigating the enormous power levels experienced in reactors. These advanced divertors are created in part through modification of the magnetic field structure to create and control additional magnetic field nulls. Additionally, the startup, shaping, and vertical control tasks of future reactors will be pushed to hardware limits as machine size and complexity increase. Control techniques that can effectively use the available hardware and optimize performance while satisfying constraints are necessary. Measurement and estimation of various parameters will be more difficult suggesting the usage of models that use inputs from multiple diagnostics or employ neural networks. This thesis explores the physics and engineering challenges of these magnetic control and estimation problems in detail for applications of shape and divertor control in tokamaks.
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Mechanical and Aerospace Engineering

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