Simulations and reduced models for microtearing modes in the Tokamak pedestals

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2022-07-26

Authors

Curie, Max Tian

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Abstract

Renewable energy can not only help to clean the environment but also create a more peaceful world. Fusion has the potential to provide clean energy with abundant resources. The high energy density (per-footprint) nature of fusion makes it appealing in highly urbanized areas, such as Singapore, which complements wind and solar power. Magnetic confinement fusion (MCF) is one of the most promising routes to thermonuclear fusion energy. Among the prospective MCF configurations, the Tokamak is the most widely implemented scheme. A host of instabilities are suppressed in H-mode (high-confinement mode) plasmas in Tokamaks due to high flow shear and/or steep density gradients in the pedestal (the edge of the plasma). This produces higher confinement and thus better performance than L-mode (low-confinement mode) operation. Transport and instabilities in the pedestal of the plasma are studied more intensively using gyrokinetic simulations thanks to the improvement of computational and experimental capabilities. Recent studies show that the magnetic fluctuations from microtearing modes (MTM) can be commonly observed in magnetic spectrograms [1– 12]. and contribute significant electron heat transport [13–15]. This thesis further investigates MTMs in the pedestal through 3 projects: • Direct comparison between nonlinear gyrokinetic simulations (GENE) and a newly installed magnetic diagnostic Faradayeffect Radial Interferometer-Polarimeter (RIP) [2, 16, 17]. Such a comparison provides strong evidence of the MTM’s importance in the Tokamak pedestal. • A package based on a global reduced model for MTM stability [18, 19] called the slab-like MTM (SLiM) package [20]. This model provides a tool for rapid MTM stability assessment. Applications of its usage will be described in the thesis: determining the stability of MTM, poloidal mode numbers, and equilibrium reconstruction. • Equilibrium reconstruction based on the SLiM model. Neural networks were trained for faster MTM stability assessment. This allows for extensive variations of nominal equilibrium quantities in order to better match the experimentally-observed magnetic frequencies in discharges and hopefully produce more accurate equilibrium reconstructions.

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