Time-frequency analysis for microwave reflectometry data processing in the HL-2A tokamak

Evaluation of the Abel inversion integral in O-mode plasma reflectometry using Chebyshev-Gauss quadrature

The Abel transform is often used to reconstruct plasma density profiles from O-Mode polarized reflectometry diagnostics. However, standard numerical trapezoidal evaluation of the Abel inversion integral can be computationally expensive for a large number of evaluation points, and an endpoint singularity exists on the upper-bound of the integral, which can result in an increased error. In this work, Chebyshev-Gauss quadrature is introduced as a new method to evaluate the Abel inversion integral for the problem of O-Mode plasma reflectometry. The method does not require numerical evaluation of an integral singularity and is shown to have similar accuracy compared to existing methods while being computationally efficient.

Application of random sample consensus method for parameter estimation of reflectometry density profile in toroidal plasma

Microwave reflectometry diagnostics have been widely used to measure density profiles in fusion plasma. However, the high sensitivity of the diagnostics to plasma turbulence often results in large radial deviations in the edge density profile and causes difficulty in profile evaluation. To improve the performance of profile evaluation, a modified RANdom SAmple Consensus (RANSAC) method has been applied to fit the density profiles measured by reflectometry on the experimental advanced superconducting tokamak. Compared with the traditional least-squares method, the modified RANSAC method is much more efficient and robust in fitting the experimental profiles. Furthermore, a combination of RANSAC and a genetic algorithm (GA-RANSAC) is used to further optimize the profile evaluation procedure. The results show that this GA-RANSAC method yields better performance and stabler convergence than the modified RANSAC alone.

Robust regression with CUDA and its application to plasma reflectometry

In many applications, especially those involving scientific instrumentation data with a large experimental error, it is often necessary to carry out linear regression in the presence of severe outliers which may adversely affect the results. Robust regression methods do exist, but they are much more computationally intensive, making it difficult to apply them in real-time scenarios. In this work, we resort to graphics processing unit (GPU)-based computing to carry out robust regression in a time-sensitive application. We illustrate the results and the performance gains obtained by parallelizing one of the most common robust regression methods, namely, least median of squares. Although the method has a complexity of O(n(3)logn), with GPU computing, it is possible to accelerate it to the point that it becomes usable within the required time frame. In our experiments, the input data come from a plasma diagnostic system installed at Joint European Torus, the largest fusion experiment in Europe, but the approach can be easily transferred to other applications.

Time-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform

An improved Hilbert-Huang transform method is developed to the time-frequency analysis of non-stationary signals in tokamak plasmas. Maximal overlap discrete wavelet packet transform rather than wavelet packet transform is proposed as a preprocessor to decompose a signal into various narrow-band components. Then, a correlation coefficient based selection method is utilized to eliminate the irrelevant intrinsic mode functions obtained from empirical mode decomposition of those narrow-band components. Subsequently, a time varying vector autoregressive moving average model instead of Hilbert spectral analysis is performed to compute the Hilbert spectrum, i.e., a three-dimensional time-frequency distribution of the signal. The feasibility and effectiveness of the improved Hilbert-Huang transform method is demonstrated by analyzing a non-stationary simulated signal and actual experimental signals in fusion plasmas.

Development of frequency modulated continuous wave reflectometer for electron density profile measurement on the HL-2A tokamak

The frequency modulated continuous wave reflectometer was developed for the first time on the HL-2A tokamak. The system utilizes a voltage controlled oscillator and an active multiplier for broadband coverage and detects as heterodyne mode. Three reflectometers have been installed and operated in extraordinary mode polarization on HL-2A to measure density profiles at low field side, covering the Q-band (33-50 GHz), V-band (50-75 GHz), and W-band (75-110 GHz). For density profile reconstruction from the phase shift of the probing wave, a corrected phase unwrapping method is introduced in this article. The effectiveness of the method is demonstrated. The density profile behavior of a fast plasma event is presented and it demonstrates the capability of the reflectometer. These diagnostics will be contributed to the routine density profile measurements and the plasma physics study on HL-2A.