Plasma parameter probe-microwave cooperative diagnosis system

Abstract

The application discloses a kind of plasma parameter probe-microwave collaborative diagnosis system, it includes diagnostic probe, reflecting antenna, vector network analyzer and computer terminal.The application provides global and local information to plasma parameters by diagnostic probe and reflecting antenna, inverts plasma electron density of S11 data and the monitoring point where diagnostic probe is located by trained neural network model, obtains the density distribution of plasma and plasma collision frequency, completes the collaborative diagnosis to plasma, can quickly and accurately obtain the density distribution of plasma and plasma collision frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of plasma diagnosis, and in particular to a plasma parameter probe-microwave collaborative diagnosis system. Background Art

[0002] When a spacecraft flies at hypersonic speeds between 20 and 100 kilometers above the ground, its nose surface rubs violently against the air, forming a plasma sheath. This attenuates and phase-shifts electromagnetic waves, severely impacting communication quality and even causing radio outages, a phenomenon known as "blackout." To address this phenomenon, it's necessary to diagnose plasma sheath parameters, typically including electron density and collision frequency.

[0003] Microwave diagnostics is one of the main methods currently used for plasma diagnosis, and they can be categorized as both invasive and non-invasive. Invasive microwave diagnostics primarily utilize probes, including microwave resonance probes, plasma impedance probes, and plasma absorption probes. Non-invasive microwave diagnostics can be further categorized as microwave reflectometry and microwave transmission. Microwave reflectometry uses the amplitude and phase of electromagnetic waves reflected from the plasma interface to diagnose plasma parameters, while microwave transmission utilizes the attenuation of electromagnetic waves through the plasma to infer electron density and collision frequency.

[0004] Existing plasma diagnostic technology has the following disadvantages:

[0005] 1. Current plasma diagnostic methods are mainly divided into two categories: invasive diagnostic methods and non-invasive diagnostic methods. Invasive diagnostic methods mainly include electrostatic probes and microwave probes. Invasive diagnostic methods will cause certain contamination to the plasma sheath to be tested.

[0006] 2. When the probe is contaminated by impurities, its volt-ampere characteristics will be affected, and the diagnostic accuracy will be greatly affected by the measurement environment. In addition, the probe will damage the aerodynamic shape of the aircraft, affecting its normal flight.

[0007] 3. Traditional microwave reflectometry, derived from radar technology, exploits the cutoff phenomenon of electromagnetic waves propagating below the plasma's characteristic frequency. The electron density at the point of total reflection is calculated using the incident wave frequency. While traditional microwave reflectometry can diagnose electron density distribution, it requires a distinct reflection interface and cannot simultaneously diagnose collision frequency.

[0008] 4. The diagnostic equipment is relatively complex because it requires measuring the arrival time of the reflected signal. The plasma sheath on the surface of a hypersonic vehicle generally has a high collision frequency and lacks a clear reflection cross-section, so using traditional microwave reflection methods can result in significant diagnostic errors. Summary of the Invention

[0009] The plasma parameter probe-microwave cooperative diagnosis system provided by the application solves the problem that the existing plasma diagnosis technology cannot accurately diagnose the density distribution and plasma collision frequency of plasma at the same time in an actual scene.

[0010] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the application is:

[0011] The application provides a plasma parameter probe-microwave cooperative diagnosis system, which comprises a diagnosis probe, a reflector antenna, a vector network analyzer and a computer terminal.

[0012] The reflector antenna and the vector network analyzer are used to obtain S11 data caused by plasma.

[0013] The diagnosis probe is used to obtain probe current caused in a plasma environment.

[0014] The computer terminal is used to calculate plasma electron temperature according to the probe current, calculate plasma electron density of a monitoring point where the diagnosis probe is located according to the plasma electron temperature, load a trained neural network model, and obtain the density distribution and the plasma collision frequency of the plasma by inverting the S11 data and the plasma electron density of the monitoring point where the diagnosis probe is located through the trained neural network model, so as to complete the cooperative diagnosis of the plasma.

[0015] Further, the diagnosis probe and the reflector antenna are coplanar.

[0016] Further, the expression for calculating the plasma electron temperature according to the probe current is:

[0017]

[0018] where T e is the plasma electron temperature, e is the electronic charge, V p is the shell point, I is the probe current, I i0 is the saturated ion current, U is the voltage, ln(.) represents the logarithmic function, and d(.) represents the differential.

[0019] Further, the expression for calculating the plasma electron density of the monitoring point where the diagnosis probe is located according to the plasma electron temperature is:

[0020]

[0021] where n iis the plasma electron density at the monitoring point where the diagnostic probe is located; S is the collection area of ​​the diagnostic probe; k is the Boltzmann constant; m i It is the plasma quality at the monitoring point where the diagnostic probe is located.

[0022] Furthermore, the diagnostic probe includes a metal rod and a ceramic tube sleeved on the outer surface of the metal rod, and the upper end of the metal rod passes through the ceramic tube; the material of the metal rod is tungsten, molybdenum or tantalum.

[0023] Furthermore, the neural network model adopts a fully connected neural network model.

[0024] Furthermore, the training process of the neural network model is:

[0025] A simulation model was constructed using simulation software. Different plasma parameters were set during the simulation process. The S11 data caused by the plasma was obtained through a reflection antenna and a vector network analyzer. The probe current caused in the plasma environment was obtained through a diagnostic probe. The plasma electron density corresponding to the data obtained by the diagnostic probe was calculated. The plasma collision frequency was obtained by querying the NRL manual. The S11 data and plasma electron density measured during the simulation were used as inputs to the neural network model. The density distribution parameters of the actual plasma and the plasma collision frequency during the simulation were used as labels to train the neural network model until the neural network model converged to obtain a trained neural network model.

[0026] Furthermore, the trained neural network model is used to invert the S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located. The specific method for obtaining the plasma density distribution and plasma collision frequency is as follows:

[0027] The S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located are used as the input of the trained neural network model. The plasma collision frequency and electron density distribution parameters are obtained by inversion of the trained neural network model. The electron density distribution parameters are substituted into the double Gaussian distribution model to obtain the plasma density distribution. The electron density distribution parameters include peak position, decrease coefficient, increase coefficient and electron density peak value.

[0028] The beneficial effects of the present invention are:

[0029] 1. The present invention provides global and local information on plasma parameters through diagnostic probes and reflective antennas, and uses a neural network model to complete in-situ diagnosis of plasma profiles, which can quickly and accurately obtain plasma density distribution and plasma collision frequency.

[0030] 2. The radiation aperture of the reflective antenna of the present invention is within 100 mm, the operating bandwidth can cover the 4-26.5 GHz frequency band, has high gain within the frequency band, and can withstand high temperatures. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the structural diagram of the system;

[0032] Figure 2 Schematic diagram of the potential distribution around the electrostatic diagnostic probe and reference electrode;

[0033] Figure 3 Provide a schematic diagram to explain the characteristic curve of the electrostatic diagnostic probe;

[0034] Figure 4 Design a flow chart for a neural network model;

[0035] Figure 5 It is the software interface of plasma parameter probe-microwave collaborative diagnosis system;

[0036] Figure 6 is a schematic cross-sectional view of the plasma sheath;

[0037] Figure 7 Schematic diagram of the reflector antenna structure. DETAILED DESCRIPTION

[0038] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0039] like Figure 1 As shown, the plasma parameter probe-microwave collaborative diagnosis system includes a diagnostic probe, a reflective antenna, a vector network analyzer and a computer terminal; the diagnostic probe and the reflective antenna are arranged on the surface of the aircraft; the reflective antenna is connected to the signal input end of the vector network analyzer; the output ends of the vector network analyzer and the diagnostic probe are respectively connected to the computer terminal; the diagnostic probe and the reflective antenna are arranged coplanar;

[0040] Reflection antenna and vector network analyzer to obtain S11 data caused by plasma;

[0041] a diagnostic probe for acquiring a probe current induced in the plasma environment;

[0042] The computer terminal is used to calculate the plasma electron temperature based on the probe current; calculate the plasma electron density at the monitoring point where the diagnostic probe is located based on the plasma electron temperature; load the trained neural network model, and use the trained neural network model to invert the S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located to obtain the plasma density distribution and plasma collision frequency, thereby completing the collaborative diagnosis of the plasma.

[0043] In the specific implementation process, the vector network analyzer can be connected via a microwave cable, and the collected data can be transmitted to the computer terminal in real time for processing, including setting, reading, and displaying the S11 data of the vector network analyzer. The specific process is as follows:

[0044] 1. Set the start frequency, end frequency and sampling interval frequency:

[0045] Use the LabVIEW Front Panel to create Numeric Controls named "StartFrequency", "StopFrequency", and "FrequencyInterval". These controls will be used to input the start frequency, stop frequency, and sampling interval frequency.

[0046] 2. Collect S11 data:

[0047] Use the VISA or GPIB library in LabVIEW to configure and communicate with the vector network analyzer. Send commands to the vector network analyzer to set the start frequency, cutoff frequency, and acquisition interval. Use the VISA Read function to read the S11 data returned from the vector network analyzer.

[0048] 3. Display amplitude and phase:

[0049] Use LabVIEW chart controls, such as Waveform Chart or Graph, to display the read S11 data on amplitude and phase charts.

[0050] When performing inversion, a computer terminal can use LabVIEW's System Exec VI to execute the external neural network model executable file (EXE). The collected S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located are passed as input parameters to the neural network model executable file. The inversion results of the plasma electron density distribution parameters are read from the neural network model executable file. The inversion results of the plasma electron density distribution parameters are introduced into the double Gaussian distribution model to obtain the plasma electron density distribution results. The obtained plasma electron density distribution results are displayed on a chart using LabVIEW's chart control, with the horizontal axis representing the cross-sectional distance.

[0051] In practice, the diagnostic probe consists of a refractory metal (tungsten, molybdenum, or tantalum) wire or rod, typically sheathed in a ceramic tube, leaving only a small section exposed to the plasma. Sometimes, a metal (stainless steel) tube is also added for electrostatic shielding. During operation, a DC voltage is applied to the probe core, while the other end is connected to a reference electrode within the plasma or to the conductive vacuum chamber wall and ground. If the vacuum chamber is insulator-proof, a reference electrode with a sufficient area must be used to maintain a stable potential. The applied voltage and current are measured, and the IV characteristic curve is obtained by scanning the applied voltage.

[0052] The voltage on the general characteristic curve is the voltage U applied in the experiment, not the potential V of the probe relative to the plasma. p U is V p The voltage U of the reference electrode connected to the probe circuit ground is R If the reference electrode is the wall of the container, it is generally negative to the plasma, U R Is the height of the plasma sheath. If V p When plotting the variable, the curve should be horizontally shifted. Based on this, the probe current I of the diagnostic probe is:

[0053]

[0054] Among them I e0 is the saturation electron current; T e is the plasma electron temperature; e is the electron charge; V p is the shell point; I i0 is the saturated ion current; exp(.) represents the exponential function.

[0055] Then the expression for calculating the plasma electron temperature based on the probe current is:

[0056]

[0057] Where U is the voltage; ln(.) represents the logarithmic function; d(.) represents the differential.

[0058] That is, the electron temperature is calculated from the slope of the middle part of the semi-logarithmic coordinate characteristic curve. This calculation does not require knowing the zero point of the horizontal axis, so the voltage applied in the experiment is used to obtain the electron temperature. Figure 2 The characteristic curve data shown is sufficient.

[0059] The intersection of the characteristic curve and the horizontal axis, that is, the voltage when the circuit is open, is the floating potential, recorded as V FIt is different from the plasma sheath between the plasma potential. Due to the size shape and other reasons, this sheath is also not equal to the sheath on the reference electrode, so the floating potential of the open circuit probe is generally not equal to the reference electrode potential. The probe characteristic curve on the left corresponds to the case where the voltage is very negative, which belongs to pure ion saturation flow, and the ion flow is flowing to the probe surface at the ion sound speed. The corresponding ion current is shown in Figure 3 On this basis, the expression for calculating the plasma electron density at the monitoring point of the diagnostic probe according to the plasma electron temperature is:

[0060]

[0061] Where n i is the plasma electron density at the monitoring point of the diagnostic probe; S is the collection area of the diagnostic probe; k is the Boltzmann constant; m i is the plasma mass at the monitoring point of the diagnostic probe.

[0062] In this embodiment, a complex nonlinear fitting function is learned according to the S11 data obtained in advance and the plasma electron density at the monitoring point of the diagnostic probe, so that the plasma parameter information can be predicted. This problem belongs to a typical regression problem, and a fully connected neural network is very effective in classification and regression problems and is the most basic deep learning network. Therefore, in this embodiment, the neural network model adopts a fully connected neural network model. The training process of the neural network model is as follows:

[0063] A simulation model is constructed through a simulation software. Different plasma parameters (including plasma density distribution parameters) are set in the simulation process. The S11 data caused by the plasma is obtained through the reflector antenna and the vector network analyzer. The probe current caused by the plasma environment is obtained through the diagnostic probe. The plasma electron density corresponding to the data obtained by the diagnostic probe is calculated. The plasma collision frequency is obtained by querying the NRL manual. The S11 data and the plasma electron density measured in the simulation process are used as the input of the neural network model, and the actual plasma density distribution parameters and the plasma collision frequency in the simulation process are used as the label to train the neural network model until the neural network model converges, and the trained neural network model is obtained. The overall design flowchart of using the neural network model for parameter inversion is shown in Figure 4 .

[0064] The S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located are inverted by the trained neural network model to obtain the plasma density distribution and plasma collision frequency. The specific method is as follows: the S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located are used as the input of the trained neural network model, and the plasma collision frequency and electron density distribution parameters are obtained by inverting the trained neural network model; the electron density distribution parameters are introduced into the double Gaussian distribution model to obtain the plasma density distribution; the electron density distribution parameters include peak position, decrease coefficient, increase coefficient and electron density peak value.

[0065] like Figure 7 As shown, the reflector antenna uses a four-ridged horn. By adopting a tapered cavity structure to improve the matching of the waveguide section, the gap between the four-ridged structure is optimized to produce a capacitive effect, further improving the performance of the reflector antenna. Outside the transition section, the horn shell adopts a larger angle. Inside the transition section, the metal ridge adopts a linear change in the transition section except for the exponential section. Since antenna gain and beamwidth are related parameters, the design achieves a compromise between gain and beamwidth by controlling the length of the antenna aperture and the horn section. The gain of this reflector antenna is greater than 10dBi across the entire frequency band, achieving a half-power beamwidth of less than 30 degrees in the frequency band below 5GHz and less than 15 degrees in the frequency band above 5GHz.

[0066] In one embodiment of the present invention, the data of the diagnostic probe is collected by an acquisition card, and the probe data collected by the acquisition card and the antenna data collected by the vector network analyzer are transmitted to a computer terminal. The inversion results are obtained by processing the prototype software developed under the LabVIEW environment. Figure 5 The left side is the setting and operation area, where you can set frequency parameters, control the vector network analyzer acquisition process, and perform inversion operations; the right side is the display area, which can display the acquired S11 amplitude, phase, and inversion results.

[0067] In one embodiment of the present invention, the diagnostic results of the system for plasmas in different states are tested and recorded. The specific testing steps are as follows:

[0068] 1. Build a plasma parameter probe-microwave collaborative diagnosis system, connecting the reflection antenna and the vector network analyzer;

[0069] 2. Start up, preheat and vacuum all instruments and equipment in the system;

[0070] 3. Turn on the discharge plasma system to form a plasma sheath between the generated plasma beam and the antenna protection cover;

[0071] 4. Real-time on-line measurement of plasma density by plasma probe array system;

[0072] 5. Setting the start frequency, cut-off frequency and sampling frequency in the inversion software interface, collecting the reflection coefficient S11 and transmission coefficient S21 through the vector network analyzer; recording the sampling interval time as the time resolution of the diagnostic system;

[0073] 6. Collecting data through the computer terminal, inverting the plasma electron density distribution parameters and plasma collision frequency, and generating the plasma electron density distribution.

[0074] According to the collected test data, the index parameters of the diagnostic system are obtained.

[0075] The spatial resolution generally refers to the size or size of the smallest unit that can be distinguished in detail on the remote sensing image. In this embodiment, the spatial resolution is used to describe the minimum interval unit of the plasma electron density profile.

[0076] For the plasma sheath on the surface of the aircraft, the thickness is assumed to be z max The coordinate axis is established perpendicular to the surface of the aircraft outwardly, as shown in Figure 6 .

[0077] The diagnostic results obtained by the experiment are a group of data y = [ne1, ne2,..., ne j ,..., ne m ], corresponding to a group of points [z1, z2,..., z j ,..., z m ] on the coordinate axis, wherein z m =z max , ne j is the plasma electron density corresponding to the z j point on the coordinate axis. When obtaining the diagnostic results, the interval of the points on the z-axis is fixed, and the interval value is the spatial resolution of the diagnostic system.

[0078] The diagnostic results of the diagnostic probe and the diagnostic results of the collaborative diagnostic system can be obtained respectively in each experiment, and numerical simulation is performed using the two groups of results respectively. According to the diagnostic density distribution, the plasma model is established in the simulation software to simulate the transmission coefficient, the transmission coefficient S21 obtained by simulation is obtained, the simulation results are compared with the S21 obtained by actual measurement, and the accuracy of the diagnostic results of the two diagnostic methods is compared. In this process, the point-by-point statistical root mean square error is used as the accuracy judgment standard, and the calculation formula is:

[0079]

[0080] X sim,iis the simulation data; X exp,i is the measured data; n is the total number of data.

[0081] Perform multiple diagnosis on the plasma in the same state. Assume that the diagnosis is repeated n times. Each time the collaborative diagnosis system obtains a set of diagnosis results y i =[ne i1 ,ne i2 ,......,ne ij ,......,ne im ], where 1≤i≤n; 1≤j≤m, average the n groups of results to get y p =[ne p1 ,ne p2 ,......,ne pj ,......,ne pm ], for each diagnosis result y i ,definition The ER is the ratio of plasma electron density to mean value, which is used to verify whether the ER of each diagnosis is less than 20%. If it is less than 20%, the surface diagnosis is accurate.

[0082] In summary, the present invention provides global and local information on plasma parameters through diagnostic probes and reflective antennas, and uses a neural network model to complete in-situ diagnosis of plasma profiles, which can quickly and accurately obtain the plasma density distribution and plasma collision frequency.

Claims

1. A plasma parameter probe-microwave collaborative diagnosis system, characterized in that: The device comprises a diagnostic probe, a reflective antenna, a vector network analyzer and a computer terminal; the diagnostic probe and the reflective antenna are arranged on the surface of the aircraft; the reflective antenna is connected to the signal input end of the vector network analyzer; the output ends of the vector network analyzer and the diagnostic probe are respectively connected to the computer terminal; Reflection antenna and vector network analyzer to obtain S11 data caused by plasma; a diagnostic probe for acquiring a probe current induced in the plasma environment; The computer terminal is used to calculate the plasma electron temperature based on the probe current; calculate the plasma electron density at the monitoring point where the diagnostic probe is located based on the plasma electron temperature; load the trained neural network model, and use the trained neural network model to invert the S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located to obtain the plasma electron density distribution and plasma collision frequency, thereby completing the collaborative diagnosis of the plasma.

2. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The diagnostic probe is arranged coplanar with the reflective antenna.

3. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The expression for calculating the plasma electron temperature based on the probe current is: Where T e is the plasma electron temperature; e is the electron charge; V p is the shell point; I is the probe current; I i0 is the saturated ion current; U is the voltage; ln(.) represents the logarithmic function; d(.) represents the differential.

4. The plasma parameter probe-microwave collaborative diagnosis system according to claim 2, characterized in that: The expression for calculating the plasma electron density at the monitoring point where the diagnostic probe is located based on the plasma electron temperature is: where n i is the plasma electron density at the monitoring point where the diagnostic probe is located; S is the collection area of ​​the diagnostic probe; k is the Boltzmann constant; m i It is the plasma quality at the monitoring point where the diagnostic probe is located.

5. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The diagnostic probe includes a metal rod and a ceramic tube sleeved on the outer surface of the metal rod, and the upper end of the metal rod passes through the ceramic tube; the material of the metal rod is tungsten, molybdenum or tantalum.

6. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The neural network model adopts a fully connected neural network model.

7. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The training process of the neural network model is: A simulation model was constructed using simulation software. Different plasma parameters were set during the simulation process. The S11 data caused by the plasma was obtained through a reflection antenna and a vector network analyzer. The probe current caused in the plasma environment was obtained through a diagnostic probe. The plasma electron density corresponding to the data obtained by the diagnostic probe was calculated. The plasma collision frequency was obtained by querying the NRL manual. The S11 data and plasma electron density measured during the simulation were used as inputs to the neural network model. The density distribution parameters of the actual plasma and the plasma collision frequency during the simulation were used as labels to train the neural network model until the neural network model converged to obtain a trained neural network model.

8. The plasma parameter probe-microwave collaborative diagnosis system according to claim 1, characterized in that: The trained neural network model is used to invert the S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located. The specific method to obtain the plasma density distribution and plasma collision frequency is as follows: The S11 data and the plasma electron density at the monitoring point where the diagnostic probe is located are used as the input of the trained neural network model. The plasma collision frequency and electron density distribution parameters are obtained by inversion of the trained neural network model. The electron density distribution parameters are substituted into the double Gaussian distribution model to obtain the plasma density distribution. The electron density distribution parameters include peak position, decrease coefficient, increase coefficient and electron density peak value.

Images