No Arabic abstract
Evolution of electron temperature, electron density and its fluctuation with high spatial and temporal resolutions are presented for the cold pulse propagation induced by super-sonic molecular beam injection (SMBI) in ohmic plasmas in the EAST tokamak. The non-local heat transport occurs for discharges with plasma current $I_p$=450 kA ($q_{95}sim5.55$), and electron density $n_{e0}$ below a critical value of $(1.35pm0.25)times10^{19}~mathrm{m^{-3}}$. In contrary to the response of core electron temperature and electron density (roughly 10 ms after SMBI), the electron density fluctuation in the plasma core increases promptly after SMBI and reaches its maximum around 15 ms after SMBI. The electron density fluctuation in the plasma core begins to decrease before the core electron temperature reaches its maximum (roughly 30 ms). It was also observed that the turbulence perpendicular velocity close to the inversion point of the temperature perturbation changes sign after SMBI.
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.
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.
After the introduction of the ionization-injection scheme in Laser Wake Field Acceleration and of related high-quality electron beam generation methods as two-color or the Resonant Multi Pulse Ionization injection, the theory of thermal emittance by C. Schroeder et al, has been used to predict the beam normalised emittance obtainable with those schemes. In this manuscript we recast and extend such a theory, including both higher order terms in the polinomial laser field expansion and non polinomial corrections due to the onset of saturation effects in a single cycle. Also, a very accurate model for predicting the cycle-averaged $3D$ momentum distribution of the extracted electrons, including saturation and multi-process events, is proposed and tested. We show that our theory is very accurate for the selected processes of Kr$^{8^+rightarrow10^+}$ and Ar$^{8^+rightarrow10^+}$, resulting in a a maximum error below $1%$ even in deep saturation regime. This highly accurate prediction of the beam phase-space can be implemented e.g., in laser-envelope Particle in Cell (PIC) or hybrid PIC-fluid codes, to correctly mimic the cycle-averaged momentum distribution without the need of resolving the intra-cycle dynamics. Finally, we introduce further spatial averaging with Gaussian longitudinal and transverse laser profiles, obtaining expressions for the whole-beam emittance that fits with Monte Carlo simulations in a saturated regime, too.
We compare the statistical properties of J=1-0 13CO spectra observed in the Perseus Molecular Cloud with synthetic J=1-0 13CO spectra, computed solving the non-LTE radiative transfer problem for a model cloud obtained as solutions of the three dimensional magneto-hydrodynamic (MHD) equations. The model cloud is a randomly forced super-Alfvenic and highly super-sonic turbulent isothermal flow. The purpose of the present work is to test if idealized turbulent flows, without self-gravity, stellar radiation, stellar outflows, or any other effect of star formation, are inconsistent or not with statistical properties of star forming molecular clouds. We present several statistical results that demonstrate remarkable similarity between real data and the synthetic cloud. Statistical properties of molecular clouds like Perseus are appropriately described by random super-sonic and super-Alfvenic MHD flows. Although the description of gravity and stellar radiation are essential to understand the formation of single protostars and the effects of star formation in the cloud dynamics, the overall description of the cloud and of the initial conditions for star formation can apparently be provided on intermediate scales without accounting for gravity, stellar radiation, and a detailed modeling of stellar outflows. We also show that the relation between equivalent line width and integrated antenna temperature indicates the presence of a relatively strong magnetic field in the core B1, in agreement with Zeeman splitting measurements.
The effect of momentum injection on the temperature gradient in tokamak plasmas is studied. A plausible scenario for transitions to reduced transport regimes is proposed. The transition happens when there is sufficient momentum input so that the velocity shear can suppress or reduce the turbulence. However, it is possible to drive too much velocity shear and rekindle the turbulent transport. The optimal level of momentum injection is determined. The reduction in transport is maximized in the regions of low or zero magnetic shear.