MHD stability and disruptions in the SPARC tokamak. (October 2020)

• Record Type: Journal Article
• Authors: Sweeney, R.; Creely, A. J.; Doody, J.; Fülöp, T.; Garnier, D. T.; Granetz, R.; Greenwald, M.; Hesslow, L.; Irby, J.; Izzo, V. A.; La Haye, R. J.; Logan, N. C.; Montes, K.; Paz-Soldan, C.; Rea, C.; Tinguely, R. A.; Vallhagen, O.; Zhu, J.
• Is Part Of: Journal of plasma physics. Volume 86:Number 5(2020)
• Publication Date: 2020-10
• Subjects: fusion plasma, -- plasma instabilities, -- runaway electrons; Plasma (Ionized gases) -- Periodicals; 530.4405
• DOI: 10.1017/S0022377820001129
• Languages: English
• ISSNs: 0022-3778

Abstract

Abstract : SPARC is being designed to operate with a normalized beta of $\beta _N=1.0$, a normalized density of $n_G=0.37$ and a safety factor of $q_{95}\approx 3.4$, providing a comfortable margin to their respective disruption limits. Further, a low beta poloidal $\beta _p=0.19$ at the safety factor $q=2$ surface reduces the drive for neoclassical tearing modes, which together with a frozen-in classically stable current profile might allow access to a robustly tearing-free operating space. Although the inherent stability is expected to reduce the frequency of disruptions, the disruption loading is comparable to and in some cases higher than that of ITER. The machine is being designed to withstand the predicted unmitigated axisymmetric halo current forces up to 50 MN and similarly large loads from eddy currents forced to flow poloidally in the vacuum vessel. Runaway electron (RE) simulations using GO+CODE show high flattop-to-RE current conversions in the absence of seed losses, although NIMROD modelling predicts losses of ${\sim }80$ %; self-consistent modelling is ongoing. A passive RE mitigation coil designed to drive stochastic RE losses is being considered and COMSOL modelling predicts peak normalized fields at the plasma of order $10^{-2}$ that rises linearly with a change in the plasma current. Massive material injection is planned to reduce the disruption loading. A data-driven approach to predict an oncoming disruption and trigger mitigation is discussed.