Magnetohydrodynamics Physics Basis for ARC
ORAL
Abstract
The future fusion power plant ARC has to operate with high performance without macroscopic magnetohydrodynamic (MHD) instabilities. This work characterizes the intrinsic MHD stability of ARC and informs the design of error field correction coils. Simulations of vertical displacement events show that the poloidal shaping coils can be used to control vertical stability and inform power supply requirements. Stability analysis of the ARC baseline scenario with RDCON shows that it is stable to ideal kink modes and linearly stable to tearing modes at the m/n=2/1 and 3/2 surfaces. The marginally stable width of neoclassical tearing modes depends strongly on the internal inductance and is found to be approximately 0.02 of the normalized poloidal flux. An empirical cross-machine model of the n=1 error field leading to a disruption predicts a critical error field similar to SPARC and ITER. 3D coils can be designed with GPEC based on a model that calculates the maximum correctable error field that is limited by the neoclassical toroidal viscosity torque. Broad scans of coil geometries identify a set of 2 rows of off-midplane coils to be a suitable solution. These 2 coil arrays would also be able to correct n=2 error fields and create strong enough edge resonant perturbations to suppress edge-localized modes. Overall, the planned ARC scenario is projected to be operable with controlled vertical stability, and without naturally occurring tearing modes or error field induced locked modes.
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Publication: Planned paper in Journal of Plasma Physics: MHD Physics basis for ARC
Presenters
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Nils Leuthold
Columbia University
Authors
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Nils Leuthold
Columbia University
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Nikolas C Logan
Columbia University
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Daniel Alexander Burgess
Columbia University
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Andrew O Nelson
Columbia University
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Stuart Royce Sands Benjamin
Massachusetts Institute of Technology
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Arunav Kumar
Massachusetts Institute of Technology
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Christopher J Hansen
Columbia University
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Carl Friedrich Benedikt F Zimmermann
Columbia University
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Francesco Carpanese
Neural Concept
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Alex J Creely
Commonwealth Fusion Systems
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Jon C Hillesheim
Commonwealth Fusion Systems
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marco muraca
Massachusetts Institute of Technology
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Carlos Alberto Paz-Soldan
Columbia University