Method for identifying plasma core safety factor q = 1 region based on multi-channel polar correlation reflectometer

Abstract

The invention relates to a method for identifying a safety factor q = 1 area of a plasma core based on a multi-channel polar correlation reflectometer. The safety factor q = 1 area is directly identified by monitoring density fluctuation information of the plasma core area through the multi-channel polar correlation reflectometer. When the multi-channel polar correlation reflectometer emits detection microwaves with multiple frequencies to the plasma core part, the influence area of the fishbone model can be identified by monitoring whether the frequency spectrum of the reflection signals of the detection microwaves generates Doppler frequency shift fD, and the area is the area of the plasma core part q = 1. Compared with a traditional safety factor distribution measurement method, the novel method can directly identify the area of the plasma core q = 1, and is simpler, more efficient and more accurate.

Description

Technical Field

[0001] The invention belongs to the field of plasma diagnosis, and in particular relates to a method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer. Background Art

[0002] Microwave reflectometer is an important plasma density diagnostic tool. It originated from radar technology, that is, it uses the reflection phenomenon of microwaves in non-uniform plasmas to calculate the cutoff position of the detection microwave in the plasma by measuring the phase delay of the reflected microwaves. This cutoff position is related to the polarization mode, frequency, plasma electron density and magnetic field strength of the incident microwave. Poloidal correlation reflectometer is a microwave reflectometer technology that specifically measures plasma density fluctuations. Its basic principle is to inject a fixed-frequency detection microwave into the plasma, and obtain the plasma density fluctuation information at the cutoff position by measuring the phase fluctuation caused by the disturbance at the cutoff position. After careful design, when multiple detection microwaves of specific frequencies are injected into the plasma at the same time, these microwaves will be cut off at different positions in the plasma, so that the plasma density fluctuations in the desired detection area can be obtained. Through time domain or frequency domain correlation analysis, the spatial structure information such as frequency and amplitude of density fluctuations can be obtained, which is a multi-channel pole-related correlation reflectometer. Because the pole-related correlation reflectometer has little interference to the plasma, has good local measurement characteristics, and has strong system radiation resistance, it has always been an important diagnostic tool for existing and future fusion devices at home and abroad.

[0003] The safety factor (q) distribution is an important parameter of the plasma in a tokamak device. It usually presents multiple characteristics such as (1) monotonic distribution, (2) q=1 and flat distribution in the core area, monotonic boundary, and (3) weak anti-shear. The plasma with the safety factor distribution of type (2) is usually called the hybrid operation mode, which is an important type of advanced operation mode of the 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 in 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 the core current data measured by polarization interferometer diagnostics (see JP Qian et al "EAST equilibrium current profile reconstructionusing polarimeter-interferometer internal measurement constraints" 2017 Nucl.Fusion 57 036008) and kinematic Stark effect diagnostics as additional constraints can obtain a more accurate current distribution. However, the main disadvantages of this method are:

[0004] 1. EFIT equilibrium inversion relies on the accuracy of the kinetic distribution, which consists of the electron density distribution, electron temperature distribution and ion temperature distribution of the plasma, which are measured by the corresponding plasma diagnostic tools. Therefore, the accuracy of the kinetic distribution will be affected by the validity of multiple diagnostic measurement data.

[0005] 2. The errors in the measurement results of polarization interferometer diagnosis and kinematic Stark effect diagnosis also bring uncertainty to the inversion of the current distribution in the plasma core.

[0006] 3. As conditions change in various fusion experimental scenarios, there is uncertainty in the errors in the generated safety factor distribution results. Summary of the invention

[0007] In order to solve the above problems in the calculation of plasma safety factor distribution, the present invention proposes a method for identifying the plasma core safety factor q=1 region based on a multi-channel polar correlation reflectometer, which can provide additional important reference information for the inversion of plasma core current distribution. The present invention proposes the following technical solutions:

[0008] A method for identifying a plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer, comprising:

[0009] Step 1, the magnetic confinement fusion device completes a discharge, and the multi-channel poloidal correlation reflectometer works normally, emitting multiple detection microwaves of different frequencies to the plasma, and synchronously collecting reflected signals, wherein the cutoff position of the detection microwaves is related to the microwave frequency, the electron density of the plasma, and the magnetic field intensity, and the multiple detection microwaves are cut off at different positions in the plasma;

[0010] Step 2: Use the weighted average method to extract the time series of Doppler frequency shift in the reflected signal of the multi-channel polar correlation reflectometer D (t);

[0011] Step 3: Convert the Doppler frequency shift time series f D (t) Perform frequency domain correlation analysis with the signal of soft X-ray diagnosis and calculate the correlation coefficient between the two ;

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

[0013] Step 5: The area in the distribution diagram 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 area affected by the fishbone model activity, i.e., the plasma core, is the q=1 area.

[0015] The present invention proposes a method for identifying the plasma core safety factor q=1 region based on a multi-channel poloidal correlation reflectometer. The core of the method is to directly identify the safety factor q=1 region by monitoring the density fluctuation information of the plasma core region through a multi-channel poloidal correlation instrument. The method is based on the following physical principles: the fishbone mode is an important magnetohydrodynamic instability in the core of the tokamak plasma. The existence of the m / n=1 / 1 fishbone mode means that there is a rational surface with a safety factor q=1 in the plasma core, and the influence area of ​​the fishbone mode is usually consistent with the area with a safety factor q=1, where m and n are the poloidal and toroidal modes of the fishbone mode in the plasma, respectively. When the fishbone mode appears, the frequency spectrum of the plasma electron density fluctuation measured by the poloidal correlation reflectometer will be affected by the activity of the fishbone mode and appear asymmetric. The degree of this asymmetry can be expressed by the Doppler frequency shift f DWhen the multi-channel polar correlation reflectometer transmits multiple frequency detection microwaves to the plasma core, the Doppler frequency shift f is generated by monitoring the spectrum of the reflected signal of these detection microwaves. D The influence area of ​​the fishbone mode can be identified, which is the q=1 area in the plasma core.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

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

[0018] (2) Compared with the polarization interferometer and kinematic Stark diagnostics mentioned above, the multi-channel polar correlation reflectometer has several advantages: simple principle and technology, high system reliability, and relatively low R&D cost; long-distance measurement has almost no interference to the plasma and has strong radiation resistance, etc.

[0019] (3) The processing method of the present invention is simple, easy for staff to understand, and highly operable. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 2 This is an example of the plasma core safety factor q=1 region identified based on the method of the present invention. DETAILED DESCRIPTION

[0022] The following is combined with Figure 1 and 2 The specific implementation methods of the present invention are described in detail, and those skilled in the art can understand the effects and advantages of the present invention based on the contents disclosed in this specification. A method for identifying the q=1 region in the plasma core based on a multi-channel polar correlation reflectometer, the core of which is to directly identify the safety factor q=1 region by monitoring the density fluctuation information of the plasma core region through a multi-channel polar correlation instrument. Figure 1 This is a workflow diagram, which includes the following steps:

[0023] Step 1: The magnetic confinement fusion device completes a discharge, and the multi-channel polar correlation reflectometer works normally, emitting 7 detection microwaves of different frequencies to the plasma and synchronously collecting reflected signals. The cutoff positions of these detection microwaves are related to the microwave frequency, the electron density of the plasma, the magnetic field strength, etc., so these 7 detection microwaves will be cut off at different positions in the plasma;

[0024] Step 2: Use the weighted average method to extract the time series of Doppler frequency shift in the reflected signal of the multi-channel polar correlation reflectometer D (t). The specific steps are as follows: First, the multi-channel polar correlation reflectometer reflection signal is subjected to fast Fourier transform (FFT) to obtain the bilateral power spectrum S(f). Then, the Doppler frequency shift is obtained by weighted calculation. , is the signal frequency. Finally, by sliding the FFT on the time signal, we can get the time series of the Doppler shift evolution over time. D (t).

[0025] Step 3: The time series of Doppler frequency shift extracted from the reflected signal of the multi-channel polar correlation reflectometer in step 3 is converted into D (t) Perform frequency domain correlation analysis with the soft X-ray diagnostic signal and calculate the correlation coefficient between the two In this field, the use of soft X-ray diagnostic signals to identify whether fishbone molds appear belongs to the prior art. If the Doppler frequency shift time series f is more than twice the noise level, it can be considered that D (t) is affected by the fishbone model activity. The time series of Doppler frequency shift f D (t) and the signal of soft X-ray diagnosis are recorded as two discrete signals and , here for two discrete signals and The frequency domain correlation coefficient between The solution process is explained here. is the sequence number of each function value in the sequence, that is, the index of the time point. The specific process includes the following:

[0026] The two signals and are divided into M ensembles respectively, then the first The Fourier transforms of the ensembles are:

[0027] ,in( ),

[0028] ,in( ),

[0029] It can be obtained that in In the ensemble, the autopower spectra of these two signals ( and ) and the cross power spectrum ( ) are:

[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 map 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 between these 7 Doppler frequency shifts f D (t) signals and the soft X-ray signal are calculated .

[0040] Step 5: The region where the frequency-domain correlation coefficient in the distribution map 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 area (1.85m < R < 1.991m) are significantly higher than those of the other 3 detected frequencies. Therefore, the shaded area (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, the area affected by the fishbone model is the q=1 area, so the shaded area (1.85m

Claims

1. A method for identifying the plasma core safety factor q=1 region based on a multi-channel polar correlation reflectometer, characterized in that: The following steps are included: Step 1, the magnetic confinement fusion device completes a discharge, the multi-channel poloidal correlation reflectometer works normally, emits multiple detection microwaves of different frequencies to the plasma, and synchronously collects reflected signals, the cutoff position of the detection microwave is related to the microwave frequency, the electron density of the plasma, and the magnetic field strength, and the multiple detection microwaves are cut off at different positions in the plasma; Step 2: Use the weighted average method to extract the time series of Doppler frequency shift in the reflected signal of the multi-channel polar correlation reflectometer D (t); Step 3: Convert the Doppler frequency shift time series f D (t) Perform frequency domain correlation analysis with the signal of soft X-ray diagnosis and calculate the correlation coefficient between the two ; Step 4: extract the Doppler frequency shift time series f from the reflected signal of the microwave detected by the multi-channel polar correlation reflectometer D (t) Perform frequency domain correlation analysis with soft X-ray signals, calculate all correlation coefficients, and draw radial distribution diagrams of frequency domain correlation coefficients; Step 5: The area in the distribution diagram 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 area affected by the fishbone model activity, i.e., the plasma core, is the q=1 area.

2. The method for identifying the plasma core safety factor q=1 region based on a multi-channel polar correlation reflectometer according to claim 1, characterized in that: In step 2, the weighted average method specifically includes the following steps: first, the multi-channel polar correlation reflectometer reflection signal is subjected to fast Fourier transform to obtain the bilateral power spectrum S(f), and then the Doppler frequency shift is obtained by weighted calculation. , is the signal frequency. Finally, the time series of Doppler shift evolution over time is obtained by sliding FFT on the time signal. D (t).

3. The method for identifying the plasma core safety factor q=1 region based on a multi-channel polar correlation reflectometer according to claim 3, characterized in that: In step 3, the time series of Doppler frequency shift f D (t) and the soft X-ray diagnostic signal are recorded as discrete signals and , for two discrete signals and The frequency domain correlation coefficient between The solution process is as follows. is the sequence number of each function value in the sequence, that is, the index of the time point. The process includes: The two signals and are divided into M ensembles respectively, then the first The Fourier transforms of the ensembles are: ,in( ), ,in( ), It can be obtained that in In the ensemble, the autopower spectra of these two signals ( and ) and the cross power spectrum ( ) are: ， ， ， After ensemble averaging the above, we can obtain the statistically averaged autopower spectrum and cross-power spectrum as follows: ， ， ， In this way, the frequency domain correlation coefficient between the two signals can be obtained: 。 4. The method for identifying the plasma core safety factor q=1 region based on a multi-channel polar correlation reflectometer according to claim 3, characterized in that: In step 5, the value of the frequency domain correlation coefficient of the high frequency components greater than 500 kHz in the signal power spectrum is defined as the noise level.

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