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Register a new userSpatial distributions of Balmer series emissions in divertor plasmas with tungsten targets in different magnetic configurations on the EAST superconducting tokamak
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The characteristics of wall recycling with different divertor configurations were investigated in this study, focusing on the observations of the spatial distributions of deuterium atomic emissions in the Balmer series (D_{alpha}, D_{beta}, D_{gamma}, and D_{delta}) with different magnetic field configurations in the Experimental Advanced Superconducting Tokamak. The observed D_{alpha} and D_{beta} emissions were primarily relatively close to the divertor targets, while the D_{gamma} and D_{delta} emissions were primarily relatively close to the X-point. The distributions of the emissions close to the divertor targets and X-point changed differently depending on the divertor configuration. These experimental results indicate that the linear comparison of parameters based on an assumption of similarity of profile shapes in different configurations is insufficient for understanding particle recycling in divertor plasmas. This is because the shape of the density profile of the recycled deuterium atoms and/or the electron density and temperature may change when the magnetic configuration is altered.
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The optimum scheme for geometric phase measurement in EAST Tokamak is proposed in this paper. The theoretical values of geometric phase for the probe beams of EAST Polarimeter-Interferometer (POINT) system are calculated by path integration in parameter space. Meanwhile, the influences of some controllable parameters on geometric phase are evaluated. The feasibility and challenge of distinguishing geometric effect in the POINT signal are also assessed in detail.
Nonlinear gyrokinetic simulations have been conducted to investigate turbulent transport in tokamak plasmas with rotational shear. At sufficiently large flow shears, linear instabilities are suppressed, but transiently growing modes drive subcritical turbulence whose amplitude increases with flow shear. This leads to a local minimum in the heat flux, indicating an optimal E x B shear value for plasma confinement. Local maxima in the momentum fluxes are also observed, allowing for the possibility of bifurcations in the E x B shear. The sensitive dependence of heat flux on temperature gradient is relaxed for large flow shear values, with the critical temperature gradient increasing at lower flow shear values. The turbulent Prandtl number is found to be largely independent of temperature and flow gradients, with a value close to unity.
A model for tokamak discharge through deep learning has been done on a superconducting long-pulse tokamak (EAST). This model can use the control signals (i.e. Neutral Beam Injection (NBI), Ion Cyclotron Resonance Heating (ICRH), etc) to model normal discharge without the need for doing real experiments. By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$. Comparing the similar methodology, we use Machine Learning techniques to develop the data-driven model for discharge modeling rather than disruption prediction. Up to 95% similarity was achieved for $W_{mhd}$. The first try showed promising results for modeling of tokamak discharge by using the data-driven methodology. The data-driven methodology provides an alternative to physical-driven modeling for tokamak discharge modeling.
The stochastic layer formation by the penetration of the resonant magnetic perturbation (RMP) field has been considered as a key mechanism in the RMP control of the edge localized mode (ELM) in tokamak plasmas. Difficulty in quantifying the stochasticity has limited the assessment of the stochastic field effect in the pedestal transport to ambiguous inference from the pressure profile change. Here, we suggest the rescaled complexity for an effective measure of the stochasticity in the fluctuation and transport. This could be used to identify the narrow and localized stochastic layer around the pedestal top and estimate its width. We found that the fluctuation and transport in the edge plasmas become more stochastic with the more penetration of the RMP field into the plasma, which supports the importance of the stochastic layer formation in the RMP ELM control experiment.
The Hall term has often been neglected in MHD codes as it is difficult to compute. Nevertheless setting it aside for numerical reasons led to ignoring it altogether. This is especially problematic when dealing with tokamak physics as the Hall term cannot be neglected as this paper shows.
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