A method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer

Abstract

The application relates to a method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer, which directly identifies the safety factor q=1 region by monitoring density fluctuation information of a plasma core region through a multi-channel poloidal correlation reflectometer. D An influence region of a fishbone mode can be identified, and the region is the plasma core q=1 region. Compared with a traditional safety factor distribution measurement method, the new method can directly identify the plasma core q=1 region, and is more simple, efficient and accurate.

Description

Technical Field

[0001] This invention belongs to the field of plasma diagnostics, specifically relating to a method for identifying the region with a safety factor of q=1 in the plasma core based on a multi-channel poloidal correlation reflectometer. Background Technology

[0002] Microwave reflectometers are important diagnostic tools for plasma density. Originating from radar technology, they utilize the reflection of microwaves in non-uniform plasmas. By measuring the phase delay of the reflected microwaves, the cutoff layer position of the probe microwave in the plasma is calculated. This cutoff layer position is related to the polarization and frequency of the incident microwave, the plasma electron density, and the magnetic field strength. Poletropic reflectometers are a specialized microwave reflectometer technology for measuring plasma density fluctuations. Their basic principle is to inject a probe microwave of a fixed frequency into the plasma and measure the phase fluctuations caused by perturbations at the cutoff layer position, thereby obtaining information on the plasma density fluctuations at that location. With careful design, when multiple probe microwaves of specific frequencies are simultaneously injected into the plasma, these microwaves will cut off at different positions within the plasma, thus obtaining the plasma density fluctuations in the desired detection area. Through time-domain or frequency-domain correlation analysis, the spatial structure information such as the frequency and amplitude of the density fluctuations can be obtained; this is the multi-channel poletropic reflectometer. Because poletropic reflectometers have minimal interference with the plasma, good local measurement characteristics, and strong radiation resistance, they have always been an important diagnostic tool in existing and future fusion devices both domestically and internationally.

[0003] The safety factor (q) distribution is an important parameter of the plasma in a tokamak device. It usually exhibits various characteristics such as (1) monotonic distribution, (2) q=1 and flat distribution in the core region, monotonic boundary, and (3) weak anti-shear. Among them, the plasma with the (2) type of safety factor distribution is usually called hybrid operation mode, which is an important type of advanced operation mode of tokamak device and a type of plasma discharge widely pursued by the fusion community at home and abroad. Therefore, the accurate measurement of the plasma safety factor distribution, especially the q=1 region, is of great significance. At present, the plasma current distribution can be obtained based on the EFIT equilibrium inversion of the kinetic distribution (see GQ Li et al "Kinetic equilibrium reconstruction on EASTtokamak", Plasma Phys. Control. Fusion 55 (2013) 125008). Furthermore, combining core current data measured using polarimeter-interferometer diagnostics (see JP Qian et al. "EAST equilibrium current profile reconstruction using polarimeter-interferometer internal measurement constraints" 2017 Nucl. Fusion 57 036008) and kinematic Stark effect diagnostics as additional constraints can yield a more accurate current distribution. However, the main drawback of this method is:

[0004] 1. EFIT equilibrium inversion depends on the accuracy of the kinetic distribution, which consists of the plasma's electron density distribution, electron temperature distribution, and ion temperature distribution. These distributions are measured by corresponding plasma diagnostic tools. Therefore, the accuracy of the kinetic distribution is affected by the validity of multiple diagnostic measurement data.

[0005] 2. Errors in the measurement results of polarization interferometer diagnosis and motion Stark effect diagnosis also introduce uncertainties into the inversion of plasma core current distribution.

[0006] 3. As conditions change under various fusion experimental scenarios, uncertainty is introduced into the error of the generated safety factor distribution results. Summary of the Invention

[0007] To address the aforementioned problems in calculating plasma safety factor distribution, this invention proposes a method for identifying the q=1 region of the plasma core safety factor based on a multi-channel poloidal correlation reflectometer, which can provide additional important reference information for plasma core current distribution inversion. The invention proposes the following technical solutions:

[0008] A method for identifying regions with a plasma core safety factor of q=1 based on a multi-channel poloidal correlation reflectometer includes:

[0009] Step 1: The magnetic confinement fusion device completes one discharge, the multi-channel poloidal correlation reflector works normally, emits multiple probe microwaves of different frequencies into the plasma, and simultaneously collects the reflected signals. The cutoff position of the probe microwaves is related to the microwave frequency, the electron density of the plasma, and the magnetic field strength. Multiple probe microwaves are cut off at different positions in the plasma.

[0010] Step 2: Extract the time series f of Doppler frequency shift from the reflected signal of the multi-channel poloidal correlation reflector using the weighted averaging method. D (t);

[0011] Step 3: Convert the Doppler frequency shift time series f D (t) A frequency domain correlation analysis was performed with the signal from soft X-ray diagnosis to calculate the correlation coefficient between the two. ;

[0012] Step 4: Extract the time series f of the Doppler frequency shift from the reflected signal of the microwave detected by the multi-channel poloidal correlator. D (t) Perform frequency domain correlation analysis with soft X-ray signals respectively, calculate all correlation coefficients, and draw the radial distribution diagram of frequency domain correlation coefficients;

[0013] Step 5: The area in the distribution map where the frequency domain correlation coefficient is more than twice the noise level is the area affected by the fishbone model activity.

[0014] Step 6: The affected area of ​​the fishbone model activity, i.e. the plasma core, is the region with q=1.

[0015] This invention proposes a method for identifying the q=1 safety factor region in the plasma core based on a multi-channel poloidal correlator. The core of this method is to directly identify the q=1 safety factor region by monitoring density fluctuations in the plasma core region using a multi-channel poloidal correlator. This method is based on the following physical principle: fishbone modes are important magnetohydrodynamic instabilities in the core of tokamak plasmas. The existence of a fishbone mode with m / n=1 / 1 implies the presence of a rational surface with a safety factor of q=1 in the plasma core, and the influence region of the fishbone mode usually coincides with the q=1 safety factor region. Here, m and n are the poloidal and circumferential modes of the fishbone mode in the plasma, respectively. When a fishbone mode appears, the spectrum of plasma electron density fluctuations measured by the poloidal correlator will be affected by the activity of the fishbone mode, thus appearing asymmetrical. This asymmetry can be expressed by the Doppler frequency shift f. DThe Doppler frequency shift is characterized by a larger fishbone mode amplitude, and a larger Doppler frequency shift value. When a multi-channel poloidal correlative reflectometer emits probe microwaves of multiple frequencies into the plasma core, the Doppler frequency shift f is monitored by observing whether the spectrum of the reflected signals of these probe microwaves produces a Doppler frequency shift. D The influence area of ​​the fishbone model can be identified, which is the plasma core region with q=1.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] (1) The present invention directly monitors the influence range of the fishbone model activity in the region symbolizing the safety factor q=1 to directly identify the region of safety factor q=1. This method is more direct and will be more accurate and faster.

[0018] (2) Compared with the aforementioned polarization interferometer and motion star diagnostic, the multichannel poloidal correlation reflectometer has several advantages: simple principle and technology, high system reliability, relatively low R&D cost; long-distance measurement has almost no interference with plasma, and strong radiation resistance, etc.

[0019] (3) The processing method of the present invention is simple, easy for staff to understand, and highly operable. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0021] Figure 2 This is an example of a plasma core safety factor q=1 region identified based on the method of this invention. Detailed Implementation

[0022] The following is in conjunction with the appendix Figure 1 and 2 The specific embodiments of the present invention are described in detail below. Those skilled in the art can understand the efficacy and advantages of the present invention based on the content disclosed in this specification. A method for identifying the q=1 region of a plasma core based on a multi-channel poloidal correlator directly identifies the q=1 region by monitoring density fluctuation information in the plasma core region using a multi-channel poloidal correlator. (Appendix) Figure 1 This is a flowchart illustrating the workflow, which includes the following steps:

[0023] Step 1: The magnetic confinement fusion device completes one discharge, the multi-channel poloidal correlation reflector works normally, emits seven probe microwaves of different frequencies into the plasma, and simultaneously collects the reflected signals. The cutoff position of these probe microwaves is related to the microwave frequency, the electron density of the plasma, the magnetic field strength, etc., so these seven probe microwaves will be cut off at different positions in the plasma.

[0024] Step 2: Extract the time series f of Doppler frequency shift from the reflected signal of the multi-channel poloidal correlation reflector using the weighted averaging method. D (t). The specific steps are as follows: First, perform a Fast Fourier Transform (FFT) on the reflected signal from the multi-channel poloidal reflector to obtain the bilateral power spectrum S(f). Then, calculate the Doppler frequency shift using weighted averages. , The signal frequency is used as the basis for calculation. Finally, by sliding an FFT over the time signal, the time series f representing the evolution of the Doppler frequency shift over time can be obtained. D (t).

[0025] Step 3: Extract the time series f of the Doppler frequency shift from the reflected signal of the multi-channel epipolar correlator in Step 3. D (t) A frequency domain correlation analysis was performed between the signal and the soft X-ray diagnostic signal to calculate the correlation coefficient between them. In this field, using soft X-ray diagnostic signals to identify the presence of fishbone models is existing technology. When the correlation coefficient... A time series f with a Doppler frequency shift more than twice the noise level can be considered to have a Doppler frequency shift. D (t) is affected by the activity of the fishbone model. The time series f of the Doppler frequency shift. D (t) and the signal from soft X-ray diagnosis are denoted as two discrete signals. and Here, for two discrete signals and Frequency domain correlation coefficient between The solution process will be explained here. This refers to the index of each function value in the sequence, i.e., the index of the time point. Specifically, it includes the following process:

[0026] Two signals and Divide them into M ensembles, then the first and second signals of these two signals are... The Fourier transforms of the ensembles are as follows:

[0027] ,in( ),

[0028] ,in( ),

[0029] Therefore, in the first... Within an ensemble, the autopower spectra of these two signals ( and ) and cross-power spectrum ( The following are respectively:

[0030] ,

[0031] ,

[0032] ,

[0033] After performing an ensemble average on the above, the statistically averaged auto-power spectrum and cross-power spectrum can be obtained as follows:

[0034] ,

[0035] ,

[0036] ,

[0037] In this way, the frequency-domain correlation coefficient between the two signals can be obtained:

[0038] ,

[0039] Step 4: Perform frequency-domain correlation analysis on the time series f D (t) of the Doppler frequency shift extracted from the reflected signals of the microwave detected by the multi-channel poloidal correlation reflectometer and the soft X-ray signal respectively, calculate all the correlation coefficients, and draw the radial distribution diagram of the frequency-domain correlation coefficients; as shown in the appendix Figure 2 . The abscissa is the cut-off positions (R(m)) of 7 detected microwaves in the plasma, and the ordinate is the frequency-domain correlation coefficient . Here, the poloidal correlation reflectometer emits 7 detected microwaves with different frequencies to the plasma. The reflected signals of these 7 detected microwaves are respectively used to extract the Doppler frequency shift signal f D (t) by the weighted average method, and then according to the method described in Step 4, the frequency-domain correlation coefficients D between these 7 Doppler frequency shift f signals and the soft X-ray signal are calculated.

[0040] Step 5: The region in the distribution diagram where the frequency-domain correlation coefficient is significantly higher than the noise level (>2 times) is the influence region of the fishbone mode activity. As shown in the appendix Figure 2 . The correlation coefficients of the 4 detected frequencies with cut-off positions in the shaded part (1.85m < R < 1.991m) are significantly higher than those of the other 3 detected frequencies. Therefore, the shaded part (1.85m < R < 1.991m) is the influence region of the fishbone mode activity. The dotted line represents the noise level. The conventional definition in this field is to define the value of the frequency-domain correlation coefficient of the high-frequency components (greater than 500 kHz) in the signal power spectrum as the noise level, and those higher than 2 times the noise level are significantly correlated.

[0041] Step 6: According to the physical principle that the area affected by the fishbone model's movement is the q=1 region, the shaded area (1.85m)

Claims

1. A method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer, characterized in that, It includes the following steps: Step 1: The magnetic confinement fusion device completes one discharge, the multi-channel poloidal correlation reflector works normally, emits multiple probe microwaves of different frequencies into the plasma, and simultaneously collects the reflected signals. The cutoff position of the probe microwaves is related to the microwave frequency, the electron density of the plasma, and the magnetic field strength. Multiple probe microwaves are cut off at different positions in the plasma. Step 2: Extract the time series f of Doppler frequency shift from the reflected signal of the multi-channel poloidal correlation reflector using the weighted averaging method. D (t); Step 3: Convert the Doppler frequency shift time series f D (t) A frequency domain correlation analysis was performed with the signal from soft X-ray diagnosis to calculate the correlation coefficient between the two. ; Step 4: Extract the time series f of the Doppler frequency shift from the reflected signal of the microwave detected by the multi-channel poloidal correlator. D (t) Perform frequency domain correlation analysis with soft X-ray signals respectively, calculate all correlation coefficients, and draw the radial distribution diagram of frequency domain correlation coefficients; Step 5: The area in the distribution map where the frequency domain correlation coefficient is more than twice the noise level is the area affected by the fishbone model activity. Step 6: The affected area of ​​the fishbone model activity, i.e., the plasma core, is the region with q=1. In step 3, the time series f of the Doppler frequency shift D (t) and the soft X-ray diagnostic signal are denoted as discrete signals. and For two discrete signals and Frequency domain correlation coefficient between The solution process is as follows, here The index is the sequence number of each function value in the sequence, i.e., the index of the time point. The process includes the following: Two signals and Divide them into M ensembles, then the first and second signals of these two signals are... The Fourier transforms of the ensembles are as follows: ,in , ,in , Therefore, in the first Within an ensemble, the autopower spectra of these two signals , and cross-power spectrum They are respectively: ， ， ， After performing ensemble averaging, the statistically averaged autopower spectrum and cross-power spectrum are obtained as follows: ， ， ， Thus, the frequency domain correlation coefficient between the two signals can be obtained: 。 2. The method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer according to claim 1, characterized in that, Step 2, the weighted averaging method specifically includes the following steps: First, perform a fast Fourier transform on the reflected signal from the multi-channel poloidal reflector to obtain the bilateral power spectrum S(f); then, calculate the Doppler frequency shift through weighted averaging. , The signal frequency is used as the basis for determining the time series f of the Doppler frequency shift evolution. Finally, by sliding an FFT over the time signal, the time series f of the Doppler frequency shift evolution is obtained. D (t); 。 3. The method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer according to claim 2, characterized in that, In step 5, the frequency domain correlation coefficient of the high-frequency components greater than 500kHz in the signal power spectrum is defined as the noise level.

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