Numerical simulation of ultrashort-pulse reflectometry(USPR)on EAST

The microwave reflectometer is a popular non-intrusive plasma density diagnostic instrument on tokamaks that provides centimeter and millisecond level resolution.The ultrashort-pulse reflectometer(USPR)achieves plasma density measurement by emitting a chirped wave containing a broadband signal and measuring the time of flight from different frequency components.A USPR system is currently being built on EAST(Experimental Advanced Superconducting Tokamak)to meet the needs of diagnostic of the pedestal density evolution,such as high-frequency small edge-localized modes.In order to predict the density reconstruction of the EAST USPR system,this work presents a numerical simulation study of the beam propagation of the chirped wave of extraordinary waves(X-mode)in the plasma based on Python.The electron density profile has been successfully reconstructed by the reflection signal interpretation.The small gap between the right-hand cut-off layer and the electron cyclotron resonance layer,due to the low plasma density on the plasma edge,causes unexpected leakage from the transmitting microwave beam to the pedestal and the core region.This kind of‘tunneling’effect will cause the reflected signal to have energy loss in the low-frequency band.The study also discusses the influence of the poloidal magnetic field on the reflected signal.The spatial variation of the poloidal magnetic field will lead to the conversion between extraordinary(X)waves and ordinary(O)waves,which leads to energy loss in the reflected signals.The simulation results show that the‘tunneling’effect and the O-X mode conversion effect have little effect on the EAST USPR system.Therefore,the currently designed transmit power meets the working requirements.

Progress of plasma density fluctuation measurements with phase contrast imaging on HL-2A tokamak

A CO2 laser-based phase contrast imaging(PCI) diagnostic has been developed on HL-2A tokamak.It can detect line integrated plasma density fluctuations by measuring the phase shift of laser beam after being scattered by the bulk plasma.The diagnosed radial region ranges from ρ≡r/a =0.625 to 0.7.32-channel HgCdTe detectors with alternative-current biased amplifiers are arranged in line at the imaging plane of the optical path.This PCI is able to diagnose density fluctuations with wavenumbers ranging from 2 to 15 cm-1 and the time resolution is better than 2 μs.The first experimental data were achieved in 2018 spring campaign of HL-2A tokamak.High performance is confirmed in different discharging configurations and makes it a keen tool in broadband turbulence investigations.

Observation and characterization of the effect of electron cyclotron waves on toroidal rotation in EAST L-mode discharges

The change in the toroidal rotation of plasma caused by electron cyclotron wave（ECW） injection has been observed in EAST. It is found that the response of the rotation is similar for all possible ECW toroidal injection angles. The core toroidal rotation velocity increases in the co-current direction along with a rise in the plasma temperature and stored energy. The profile of the electron temperature, ion temperature and toroidal rotation velocity gradually become peaked.The change in toroidal rotation in the core increases with the ECW injection power. Different behavior is observed when the ECWs are injected into low hybrid current drive（LHCD） target plasmas, where the electron temperature and rotation profile become peaked, while the ion temperature profile flattens after ECW injection, suggesting different transport characteristics in energy and momentum.

Fast estimation of ion temperature from EAST charge exchange recombination spectroscopy using neural network

Ion temperature, as one of the most critical plasma parameters, can be diagnosed by charge exchange recombination spectroscopy (CXRS). Iterative least-squares fitting is conventionally used to analyze CXRS spectra to identify the active charge exchange component, which is the result of local interaction between impurity ions with a neutral beam. Due to the limit of the time consumption of the conventional approach (~100 ms per frame), the Experimental Advanced Superconducting Tokamak CXRS data is now analyzed in-between shots. To explore the feasibility of real-time measurement, neural networks are introduced to perform fast estimation of ion temperature. Based on the same four-layer neural network architecture, two neural networks are trained for two central chords according to the ion temperature data acquired from the conventional method. Using the TensorFlow framework, the training procedures are performed by an error back-propagation algorithm with the regularization via the weight decay method. Good agreement in the deduced ion temperature is shown for the neural networks and the conventional approach, while the data processing time is reduced by 3 orders of magnitude (~0.1 ms per frame) by using the neural networks.