Method, device and equipment for determining working characteristic parameters of Hall thruster

Abstract

The invention provides a Hall thruster working characteristic parameter determination method, device and equipment, and relates to the technical field of Hall thruster control, and the method comprises the steps: obtaining a target working condition parameter of a Hall thruster; inputting the target working condition parameters into a pre-trained Hall thruster discharge oscillation prediction model, and predicting working parameter time sequence data of the Hall thruster under the target working condition parameters and space-time distribution data of plasma in a channel of the Hall thruster; wherein the Hall thruster discharge oscillation prediction model is obtained by training a plurality of pieces of historical simulation data; and analyzing the working parameter time sequence data and the spatial-temporal distribution data to obtain working characteristic parameters. According to the method, the working parameter time sequence data of the Hall thruster and the spatial and temporal distribution data of the plasma can be accurately and efficiently predicted, and accurate and reliable macroscopic and microscopic characteristic parameters are extracted.

Description

Technical Field

[0001] The present application relates to the technical field of Hall thruster control, and in particular to a method, device, and apparatus for determining operating characteristic parameters of a Hall thruster. Background Art

[0002] The existing models for simulating Hall thrusters mainly include one-dimensional quasi-neutral fluid model and its improved model and three-dimensional particle simulation model.

[0003] The one-dimensional quasi-neutral fluid model assumes that the plasma is electrically neutral on a macroscopic scale and that the dynamic behavior of charged particles and neutral particles is similar. It considers the plasma's continuity equation, momentum equation, and energy equation to describe the temporal and spatial variations of macroscopic physical quantities such as density, velocity, and temperature in the thruster operating environment. While the one-dimensional quasi-neutral fluid model can simulate changes in thruster operating parameters, including the effects of magnetic field, discharge voltage, fluid flow rate, and preionization rate on the amplitude and frequency of the discharge current's low-frequency oscillations, it is inherently limited by the fluid model and requires additional assumptions. This makes it impossible to accurately capture the plasma characteristics that cause discharge oscillations, the influence of operating parameters on the microscopic level, predict discharge parameters, and obtain plasma distribution data.

[0004] Improved one-dimensional quasi-neutral fluid models, however, treat the plasma as a continuous medium and fail to capture the detailed dynamics between microscopic particles. This results in inaccurate simulations at the microscale or when describing interparticle interactions. While this approach can explain the causes of breathing oscillations to a certain extent, it requires additional assumptions and cannot self-consistently resolve the breathing oscillation behavior of the plasma within the Hall thruster. Furthermore, the assumption of a Maxwellian distribution of electrons in the Hall thruster does not always hold true, resulting in significant errors and inaccurate results. The three-dimensional particle simulation model requires a large amount of computing resources to simulate the movement of particles, has high requirements for the simulation environment, and has a long simulation running time. At the same time, only one set of results can be obtained in one simulation, resulting in low simulation efficiency. Although it can provide higher-precision and higher-resolution simulation physical information results, it also introduces a lot of unnecessary complexity and consumes a lot of computer computing power and time costs. This is not worth the cost compared to the simulation effect of the Hall thruster's breathing oscillation behavior obtained by the model. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method, device, and apparatus for determining characteristic operating parameters of a Hall thruster, which solves the above-mentioned problems existing in the prior art and can efficiently, accurately, and quickly extract and predict time series data of Hall thruster operating parameters and spatiotemporal distribution data of plasma in the channel, and extract discharge oscillation characteristic parameters.

[0006] In a first aspect, a method for determining operating characteristic parameters of a Hall thruster is provided. The method may include: Obtain target operating parameters of the Hall thruster; Inputting the target operating parameters into a pre-trained Hall thruster discharge oscillation prediction model to predict operating parameter time series data of the Hall thruster under the target operating parameters and spatiotemporal distribution data of plasma in a channel of the Hall thruster; The Hall thruster discharge oscillation prediction model is trained using multiple historical simulation data; any historical simulation data includes: historical operating parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data; The working parameter time series data and the spatiotemporal distribution data are analyzed to obtain working characteristic parameters.

[0007] In an optional implementation, the target operating condition parameters include: channel configuration, channel size, and wall material type of the Hall thruster, as well as external electric field parameters, magnetic field parameters, cathode discharge current of the Hall thruster, and type and flow rate of neutral gas input into the Hall thruster; The working parameter time series data includes: discharge current time series data, thrust time series data and physical field time series data; The spatiotemporal distribution data of the plasma include: spatiotemporal distribution data of particle density, axial spatiotemporal distribution data of electron temperature, and axial spatiotemporal distribution data of ion temperature.

[0008] In an optional implementation, the operating parameter time series data and the spatiotemporal distribution data are analyzed to obtain operating characteristic parameters, including: Analyze the discharge current time series data and the thrust time series data to obtain a first operating characteristic parameter; wherein the first operating characteristic parameter includes: an oscillation period of the discharge oscillation; Extracting target physical field time series data and target spatiotemporal distribution data within one oscillation period from the physical field time series data and the spatiotemporal distribution data respectively; Analyzing the time series data of the target physical field to obtain a change trend of the physical field within an oscillation cycle; Analyzing the target spatiotemporal distribution data to obtain second operating characteristic parameters; wherein the second operating characteristic parameters include: a change trend of particle density within one oscillation cycle, a change trend of electron temperature within one oscillation cycle, and a change trend of ion temperature within one oscillation cycle; The first operating characteristic parameter, the change trend of the physical field within one oscillation period, and the second operating characteristic parameter are determined as operating characteristic parameters.

[0009] In an optional implementation, the discharge current time series data and the thrust time series data are analyzed to obtain the first operating characteristic parameter, including: Extracting a plurality of first peak values ​​and first valley values ​​from the discharge current time series data; determining an oscillation period of the discharge oscillation based on the extracted plurality of first peaks; extracting a plurality of second peak values ​​and second valley values ​​from the thrust time series data; The first peak value, the first valley value, the second peak value, the second valley value and the oscillation period are determined as first operating characteristic parameters.

[0010] In an optional implementation, before obtaining the target operating parameters of the Hall thruster, the method further includes: Construct a one-dimensional full-particle numerical simulation model of the Hall thruster.

[0011] In an optional implementation, the method for obtaining historical simulation data includes: Get multiple historical operating parameters of the configuration; For any historical operating condition parameter, the historical operating condition parameter is input into the one-dimensional full-particle numerical simulation model to perform discharge simulation, and historical operating parameter time series data and corresponding historical spatiotemporal distribution data corresponding to the historical operating condition parameter are obtained.

[0012] In an optional implementation, the Hall thruster discharge oscillation prediction model includes: Input layer, used to receive target operating parameters; A feature embedding layer is used to extract features of the input target operating condition parameters to obtain a feature vector sequence and a first oscillation period; A time series prediction layer, configured to predict a time series of particle counts and an ionization rate within a preset time window based on a sequence of feature vectors inputted by the feature embedding layer; wherein the length of the preset time window is greater than the first oscillation period; The spatiotemporal distribution prediction layer is used to predict the spatiotemporal distribution data of plasma within a preset time window based on the feature vector sequence and the time series of the particle number; The working parameter prediction layer is used to predict the working parameter time series data within a preset time window based on the feature vector sequence and ionization rate time series, as well as the particle density spatiotemporal distribution data; a period verification layer for extracting the second oscillation period, the third oscillation period, and the fourth oscillation period based on the time series of the number of particles within a preset time window, the spatiotemporal distribution data of the plasma, and the time series data of the operating parameters; The output layer is used to output the time series data of the operating parameters of the Hall thruster under the target operating parameters and the spatiotemporal distribution data of the plasma in the channel of the Hall thruster when the difference between the first oscillation period, the second oscillation period, the third oscillation period and the fourth oscillation period is less than a configured threshold.

[0013] In a second aspect, a device for determining operating characteristic parameters of a Hall thruster is provided, which may include: An acquisition unit, used for acquiring target operating parameters of the Hall thruster; a prediction unit, configured to input the target operating condition parameters into a pre-trained Hall thruster discharge oscillation prediction model, and predict operating parameter time series data of the Hall thruster under the target operating condition parameters and spatiotemporal distribution data of plasma in a channel of the Hall thruster; wherein the Hall thruster discharge oscillation prediction model is trained using a plurality of historical simulation data; any historical simulation data includes: historical operating condition parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data; The analyzing unit is used to analyze the working parameter time series data and the spatiotemporal distribution data to obtain working characteristic parameters.

[0014] In a third aspect, an electronic device is provided, the electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement any of the method steps described in the first aspect when executing a program stored in the memory.

[0015] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the method steps described in the first aspect is implemented.

[0016] This application utilizes a Hall thruster discharge oscillation prediction model to capture the nonlinear chaotic state within the thruster during operation, simulating the relationship between target operating parameters and the real-time plasma characteristics within the Hall thruster channel, thereby predicting the motion behavior of each particle within the thruster over a period of time in the future. The Hall thruster discharge oscillation prediction model of this application utilizes a deep neural network to simulate complex plasma dynamics, improve prediction accuracy, and reduce computational requirements. Furthermore, as the amount of data increases, the model can continuously learn and optimize based on experience to determine the effects of various influencing factors on the Hall thruster's axial breathing pattern, thereby predicting the motion state of each particle and thereby regulating the Hall thruster's operating parameters to improve the thruster's operating performance and stability.

[0017] The one-dimensional full-particle numerical simulation model of this application can self-consistently solve the motion state of the plasma in the channel, ensuring the accuracy and reliability of the simulation. At the same time, it provides an accurate and reliable data set, and uses this data set to train the Hall thruster discharge oscillation prediction model, while ensuring accuracy while solving the pain points of the particle model with high computing power requirements and long simulation time.

[0018] This application reveals the discharge oscillation mechanism and the influence mechanism of various operating parameters on the working characteristic parameters, and realizes the prediction of the plasma motion inside the thruster in the future period, which has good application prospects in the performance index prediction and real-time operation control of Hall thrusters.

[0019] The Hall thruster discharge oscillation prediction model of the present application predicts the working parameter time series data and the spatiotemporal distribution data of the plasma through multiple prediction layers, which is not only more accurate, but also keeps the microscopic characteristics and macroscopic features of the particles consistent, and the obtained prediction results are more real and reliable.

[0020] amplitude, period, peaks and valleys; The smaller the amplitude, the closer the peak and valley values ​​are to each other, the better; BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a method for determining operating characteristic parameters of a Hall thruster provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a device for determining operating characteristic parameters of a Hall thruster provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] The method for determining the characteristic operating parameters of a Hall effect thruster provided in the embodiments of the present application can be applied in a server or a terminal with strong computing capabilities. The server can be a physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital broadcast receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be connected directly or indirectly via wired or wireless communication methods, which is not limited in this application.

[0024] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0025] Figure 1 This is a flow chart of a method for determining the working characteristic parameters of a Hall thruster provided in an embodiment of the present application. Figure 1 As shown, the method may include: Step S110: acquiring target operating parameters of the Hall thruster; inputting the target operating parameters into a pre-trained Hall thruster discharge oscillation prediction model to predict the operating parameter time series data of the Hall thruster under the target operating parameters and the spatiotemporal distribution data of the plasma in the channel of the Hall thruster.

[0026] In the embodiment of the present application, the target operating parameters include: the channel configuration, channel size and wall material type of the Hall thruster, as well as the external electric field parameters, magnetic field parameters, cathode discharge current of the Hall thruster, and the type and flow rate of the neutral gas input into the Hall thruster.

[0027] In an embodiment of the present application, the Hall thruster discharge oscillation prediction model is trained using multiple historical simulation data; any historical simulation data includes: historical operating parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data.

[0028] In an embodiment of the present application, the working parameter time series data includes: discharge current time series data, thrust time series data and physical field time series data; the spatiotemporal distribution data of the plasma includes: particle density spatiotemporal distribution data, electron temperature axial spatiotemporal distribution data and ion temperature axial spatiotemporal distribution data; wherein, the particle density spatiotemporal distribution data includes: electron density spatiotemporal distribution data, ion density spatiotemporal distribution data and atomic density spatiotemporal distribution data.

[0029] In an embodiment of the present application, the Hall thruster discharge oscillation prediction model adopts a multi-task learning deep neural network, which can quickly simulate parameters such as the current under the thruster operating state according to the set environmental conditions and operating parameters, greatly improving the simulation efficiency and decoupling the complex relationship between thruster performance and various input parameters, solving the pain points of the full-particle model requiring high platform computing power and long simulation running time; at the same time, the multi-task learning deep neural network can simultaneously predict multiple types of data. By sharing the underlying feature input, it can help the Hall thruster discharge oscillation prediction model better extract common features across tasks and improve the generalization ability of the model.

[0030] In an embodiment of the present application, the Hall thruster discharge oscillation prediction model includes: Input layer, used to receive target operating parameters; A feature embedding layer is used to extract features of the input target operating condition parameters to obtain a feature vector sequence and a first oscillation period; A time series prediction layer, configured to predict a time series of particle counts and an ionization rate within a preset time window based on a sequence of feature vectors inputted by the feature embedding layer; wherein the length of the preset time window is greater than the first oscillation period; The spatiotemporal distribution prediction layer is used to predict the spatiotemporal distribution data of plasma within a preset time window based on the feature vector sequence and the time series of the particle number; The working parameter prediction layer is used to predict the working parameter time series data within a preset time window based on the feature vector sequence and ionization rate time series, as well as the particle density spatiotemporal distribution data; a period verification layer for extracting the second oscillation period, the third oscillation period, and the fourth oscillation period based on the time series of the number of particles within a preset time window, the spatiotemporal distribution data of the plasma, and the time series data of the operating parameters; The output layer is used to output the time series data of the operating parameters of the Hall thruster under the target operating parameters and the spatiotemporal distribution data of the plasma in the channel of the Hall thruster when the difference between the first oscillation period, the second oscillation period, the third oscillation period and the fourth oscillation period is less than a configured threshold.

[0031] In the embodiment of the present application, the periodic verification layer is further used to: When the difference between the first oscillation period, the second oscillation period, the third oscillation period and the fourth oscillation period is not less than the configured threshold, the average oscillation period is calculated according to the first oscillation period, the second oscillation period, the third oscillation period and the fourth oscillation period; feedback results are generated according to the differences between the first oscillation period, the second oscillation period, the third oscillation period, the fourth oscillation period and the average oscillation period; the feedback results are sent to the feature embedding layer, the time series prediction layer, the spatiotemporal distribution prediction layer and the working parameter prediction layer respectively, so that the feature embedding layer, the time series prediction layer, the spatiotemporal distribution prediction layer and the working parameter prediction layer respectively perform feature extraction or prediction based on the feedback results and the corresponding input to obtain new output data and new oscillation periods, until the difference between the obtained oscillation periods is less than the configured threshold.

[0032] In an embodiment of the present application, the period verification layer performs period comparison verification based on the oscillation period predicted by direct training of the feature embedding layer and the oscillation period predicted by the time series prediction layer, the spatiotemporal distribution prediction layer, and the working parameter prediction layer. Feedback adjustment is performed until the oscillation period obtained by the aforementioned prediction model reaches a certain accuracy range, thereby ensuring that the model prediction results are accurate and significantly improving the reliability and dynamic adaptability of the prediction model.

[0033] In an embodiment of the present application, based on the feature vector sequence extracted by the feature embedding layer, a time series of the number of particles of electrons, ions, and atoms is generated through the time series prediction layer, and a particle number sequence is extracted from the time series of the particle number (the time-explicit features are removed, and the training model extracts the implicit oscillation period through the number sequence to provide a judgment basis for the period comparison and verification layer), and the time series of the particle number and the feature vector sequence are used as input parameters of the spatiotemporal distribution prediction layer; through the spatiotemporal distribution prediction layer, a spatiotemporal distribution sequence of the particle density of electrons, ions, and atoms is generated, and a density space distribution sequence is extracted from the particle density spatiotemporal distribution sequence (the time-explicit features are removed, and the training model extracts the implicit oscillation period through the number density sequence to provide a judgment basis for the period comparison and verification layer), and the density space distribution sequence and the feature vector sequence are used as input parameters of the working parameter prediction layer.

[0034] In an embodiment of the present application, the Hall thruster discharge oscillation prediction model can output the time series data of the working parameters and the spatiotemporal distribution data of the plasma within one oscillation period, and can also output the time series data of the working parameters and the spatiotemporal distribution data of the plasma within multiple oscillation periods.

[0035] In an embodiment of the present application, the feature embedding layer can first perform appropriate embedding for different types of inputs; for example, for different neutral gas flows or electromagnetic field intensities, they can be mapped to a high-dimensional space through the embedding layer, so that the neural network can effectively process a variety of input features.

[0036] In this embodiment of the present application, since the discharge parameters of the Hall effect thruster change dynamically, a time series processing module is required to predict the time-varying parameters during the discharge process. Multiple LSTM / GRU layers are used to capture the time-varying patterns of the discharge process, and the output time series provides a foundation for subsequent analysis. Furthermore, the introduction of a temporal attention mechanism helps the network focus on time periods that have a greater impact on the prediction results. This helps improve the ability to model both short-term and long-term dependencies.

[0037] In this embodiment, for the spatiotemporal distribution of plasma data, considering the complex spatial distribution of plasma within the thruster channel, a convolutional neural network is used to extract spatial features and combine them with time series information to model spatiotemporal dynamics. By combining the LSTM / GRU of the time series module with the aforementioned spatial module, joint modeling of the spatiotemporal distribution of plasma is achieved.

[0038] In this embodiment of the present application, the operating parameter prediction layer post-processes the data obtained based on the predicted discharge parameters and plasma distribution to estimate the thruster's operating performance (such as thrust, specific impulse, efficiency, etc.). By extracting the aforementioned spatiotemporal features, a fully connected layer is used to map them into final operating performance evaluation data. This module can output various thruster performance indicators.

[0039] In the embodiments of this application, the Hall thruster current oscillation and discharge oscillation prediction model incorporates the physical model of the Hall thruster (such as the effects of electric and magnetic fields on particles) during training, introducing physical constraints into the network training process. These physical constraints can help the neural network avoid physically unreasonable results during training, thereby improving the accuracy and reliability of predictions. This is achieved by adding the residual term of the physical equation to the loss function, which can be expressed as: ; in, represents the forecast error term, Represents the physical constraint term, through the weight coefficient λ The two factors are balanced. The prediction error term measures the difference between the model's prediction and the actual observed value, often using the mean squared error (MSE) or mean absolute error (MAE). The physical constraint term (residual term) measures whether the model's predictions conform to physical laws. The residual is calculated using physical equations (such as the effects of electric and magnetic fields on particles) to ensure that the predictions are physically reasonable.

[0040] In an embodiment of the present application, a Hall effect thruster current oscillation and discharge oscillation prediction model employs an adaptive learning rate and meta-learning during training. Meta-learning can learn how to effectively update network parameters by training a single model. This meta-learning approach can better adapt to new input conditions for varying operating conditions, thereby improving the model's flexibility and robustness. Combined with an adaptive learning rate approach, this approach can accelerate the training process and reduce reliance on large amounts of data.

[0041] In an embodiment of the present application, the Hall thruster current oscillation prediction model is trained using multiple historical simulation data; specifically, any historical simulation data includes: historical operating parameter time series data and historical spatiotemporal distribution data of plasma characteristic parameters.

[0042] The present application uses a Hall thruster current oscillation prediction model to predict macroscopic performance parameters such as discharge and thrust and plasma microscopic characteristic parameters based on target operating parameters, which can effectively improve simulation efficiency while maintaining good prediction accuracy. At the same time, the Hall thruster discharge oscillation prediction model is trained using historical simulation data, so that the Hall thruster discharge oscillation prediction model can learn the correlation between historical operating parameters in the historical simulation data and the corresponding historical working parameter time series data and the historical spatiotemporal distribution data of plasma characteristic parameters, further improving prediction accuracy and efficiency.

[0043] In an embodiment of the present application, before obtaining the target operating parameters of the Hall thruster, it is necessary to construct a one-dimensional full-particle numerical simulation model of the Hall thruster; obtain multiple configured historical operating parameters; and for any historical operating parameter, input the historical operating parameter into the one-dimensional full-particle numerical simulation model to perform a discharge simulation, thereby obtaining historical operating parameter time series data and corresponding historical spatiotemporal distribution data corresponding to the historical operating parameter.

[0044] In practical applications, the basic idea of ​​plasma particle simulation is to achieve real-time simulation of the entire discharge development process by tracking the motion and forces acting on charged particles. To reduce the amount of computation, macroparticles are often used to represent multiple actual particles moving together. These particles share common coordinates and velocities, and their charge-to-mass ratio is equal to that of real particles. Particle simulation methods are based on first-principles equations and neglect less of the actual physical processes. Therefore, they can most realistically reflect the actual process of the interaction between charged particles and electromagnetic fields, directly simulate the microscopic motion of electrons and ions, capture the interactions and nonlinear effects between particles, achieve a self-consistent solution to the effects of electric and magnetic fields on plasma, and capture the coupling effects of electromagnetic fields and particle motion.

[0045] In an embodiment of the present application, a method for constructing a one-dimensional full-particle numerical simulation model of a Hall thruster includes: Obtain the channel configuration, channel dimensions, wall material type, external electric field parameters, magnetic field parameters, cathode discharge current, and the type and flow rate of the neutral gas input into the Hall thruster; construct an initial one-dimensional full-particle numerical simulation model of the Hall thruster based on the channel configuration, channel dimensions, and wall material type; and determine the magnetic field spatial distribution sequence data based on the magnetic field parameters; Based on the cathode discharge current, the number of electrons entering the discharge channel of the Hall thruster in each time step is calculated. Based on the type of neutral gas input into the Hall thruster, the collision cross-section data corresponding to the working fluid type is obtained from the configured database. Based on the flow rate of the neutral gas input into the Hall thruster, the number of atoms and atomic velocity entering the discharge channel in each time step are calculated. According to the magnetic field spatial distribution sequence data, external electric field parameters, electron number, collision cross-section data, atomic number and atomic velocity, the initial one-dimensional full-particle numerical simulation model is adjusted to obtain the one-dimensional full-particle numerical simulation model of the Hall thruster.

[0046] In another embodiment of the present application, the method for constructing a one-dimensional full-particle numerical simulation model of a Hall thruster may further include: Obtain the geometric configuration, magnetic field configuration, initial electric field distribution, cathode discharge current, working fluid type, and mass flow rate of the corresponding working fluid of the Hall thruster; construct an initial one-dimensional full-particle numerical simulation model of the Hall thruster based on the geometric configuration; determine the magnetic field spatial distribution sequence data based on the magnetic field configuration; and determine the electric field parameters based on the initial electric field distribution; Based on the cathode discharge current, the number of electrons entering the Hall thruster discharge channel in each time step is calculated. Based on the working fluid type, the collision cross-section data corresponding to the working fluid type is obtained from the configured database. Based on the mass flow rate of the working fluid, the number of atoms entering the discharge channel and the atomic velocity are calculated in each time step. According to the magnetic field spatial distribution sequence data, electric field parameters, electron number, collision cross-section data, atomic number and atomic velocity, the initial one-dimensional full-particle numerical simulation model is adjusted to obtain the one-dimensional full-particle numerical simulation model of the Hall thruster.

[0047] In the embodiment of the present application, after obtaining the one-dimensional full-particle numerical simulation model of the Hall thruster, a dynamic simulation is performed based on the input historical operating condition parameters, specifically including: Based on the historical cathode discharge current in the historical operating parameters, the number of electrons entering the discharge channel at each time step is calculated and distributed; based on the historical neutral gas type and historical flow rate in the input Hall thruster, the number and speed of atoms entering the discharge channel at each time step are calculated and distributed; The discharge simulation is performed according to the number of electrons, the number of atoms and the speed, and the corresponding physical quantities are counted and updated on the grid cells of the one-dimensional full-particle numerical simulation model to obtain the historical working parameter time series data and the corresponding historical spatiotemporal distribution data.

[0048] In the embodiments of the present application, the working fluid type is a specific type of gas or substance used in the Hall thruster, which can be effectively accelerated to generate thrust after ionization; the working fluid type is related to the collision cross section; it is used to calculate the probability of collision between particles, atomic mass and atomic radius.

[0049] In this embodiment of the present application, the magnetic field spatial distribution sequence data remains unchanged during thruster operation (i.e., a static magnetic field); this magnetic field spatial distribution sequence data includes, but is not limited to, magnetic field intensity, direction, and its three-dimensional spatial distribution within the discharge channel. The initial electric field distribution sequence data is the initial electric field distribution sequence data at the start of the Hall thruster; due to the dynamic behavior of electrons and ions, the initial electric field distribution changes in real time. Therefore, the initial electric field distribution sequence data only reflects the electric field state at thruster startup.

[0050] In the embodiment of the present application, there is a linear relationship between the cathode current and the number of electrons entering the discharge channel, so that the number of electrons in each time step can be accurately determined based on the cathode current.

[0051] In the embodiment of the present application, statistics and updates of corresponding physical quantities on the grid cells of the one-dimensional full-particle numerical simulation model include: Based on the collision cross section data, the plasma reaction is calculated to obtain the spatial and temporal distribution sequence of the number of atoms; Use Monte Carlo Collisions (MCC) methods to process collisions between electrons, ions, and neutral gases to calculate the probability and frequency of different types of collisions and update the velocity, energy, and other properties of the particles; By solving the Poisson equation with finite difference discretization, the spatial distribution sequence of electric potential is calculated, and the corresponding space-time distribution of electric field, i.e., the physical field time series data, is calculated based on the electric potential distribution; The particle-in-cell (PIC) method is used for discretization, and the leapfrog scheme (Boris) based on Newton's motion formula is used to push electrons, ions, and atoms and update their positions and velocities. Deposit the particle parameters onto the grid and obtain the spatial-temporal distribution of particle number density, axial spatial-temporal distribution of electron temperature, and axial spatial-temporal distribution of ion temperature corresponding to the current time step. The number of electrons reaching the anode boundary at each time step is counted to obtain the discharge current time series data; the number and velocity of ions reaching the cathode boundary at each time step are counted to obtain the thrust time series data.

[0052] In the embodiment of the present application, the number and velocity of ions reaching the cathode boundary at each time step are counted to obtain thrust time series data, including: Under the action of the electric field, the velocity of the ions after acceleration is calculated; using self-consistent boundary conditions, the number of ions reaching the cathode boundary is counted at each time step; based on the velocity and number of ions, the thrust is calculated to obtain historical thrust time series data.

[0053] In the embodiment of the present application, the particle parameters are deposited onto the grid, and the spatiotemporal distribution of the particle number density, the axial spatiotemporal distribution of the electron temperature, and the axial spatiotemporal distribution of the ion temperature corresponding to the current time step are statistically obtained, including: Electrons, ions, and atoms are pushed based on the time step, using the frog leaping format (Boris) based on Newton's motion formula to calculate the acceleration of particles in the electric field and the deflection in the magnetic field. The velocity of each particle is calculated based on the force, and the position of the particle after one time step is obtained based on the particle velocity; the particle parameters are deposited on the grid through the particle cloud chamber (cloud-in-cell, CIC) method, and the particle number time series and particle number density spatiotemporal distribution series corresponding to the current time step are obtained; the speed of each grid electron corresponding to each time step in the calculation domain is statistically processed to obtain the historical axial spatiotemporal distribution data of the electron temperature; the speed of each grid ion corresponding to each time step in the calculation domain is statistically processed to obtain the historical axial spatiotemporal distribution data of the ion temperature.

[0054] In the embodiment of the present application, the Monte Carlo Collisions (MCC) method is used to process collisions between electrons, ions and neutral gases, including: Based on the collision cross-section data obtained by interpolation, the probability of occurrence of different types of collisions (such as elastic collisions, excitation collisions, ionization collisions, momentum exchange collisions, charge exchange collisions, etc.) in each grid cell in each time step is calculated; the collision frequency is obtained by counting the number of different types of collisions occurring in each time step; Use the Monte Carlo method for random sampling to determine whether a collision occurs and the specific type of collision; update the particle's velocity, energy, and other properties based on the collision type; Count the number of different types of collisions at each time step, especially ionization collisions, to obtain time series data of ionization rate; By counting the changes in the number of particles after the collision, the spatiotemporal distribution data of the particle number density of electrons, ions and neutral atoms are obtained; By statistically analyzing the velocity distribution of particles after the collision, the spatiotemporal distribution of electron temperature and ion temperature is calculated.

[0055] Step S120: Analyze the time series data and spatiotemporal distribution data of the working parameters to obtain working characteristic parameters.

[0056] In the embodiment of the present application, the oscillation period of the discharge oscillation, the first peak value, the first valley value, the second peak value, the second valley value, the changing trend of the particle density within one oscillation period, the changing trend of the electron temperature within one oscillation period, and the changing trend of the ion temperature within one oscillation period; specifically, the first peak value is the peak value of the discharge oscillation, and the first valley value is the valley value of the discharge oscillation; the second peak value is the peak value of the thrust, and the second valley value is the valley value of the thrust.

[0057] In the embodiment of the present application, the time series data and spatiotemporal distribution data of the working parameters are analyzed to obtain the working characteristic parameters, including: Extracting a plurality of first peak values ​​and a first valley value from the discharge current time series data; determining an oscillation period of the discharge oscillation based on the extracted plurality of first peak values; extracting a plurality of second peak values ​​and a second valley value from the thrust time series data; and determining the first peak value, the first valley value, the second peak value, the second valley value, and the oscillation period as a first operating characteristic parameter; Target physical field time series data and target spatiotemporal distribution data within an oscillation cycle are extracted from the physical field time series data and the spatiotemporal distribution data respectively; the target physical field time series data are analyzed to obtain a changing trend of the physical field within an oscillation cycle; the target spatiotemporal distribution data are analyzed to obtain a second working characteristic parameter; the first working characteristic parameter, the changing trend of the physical field within an oscillation cycle, and the second working characteristic parameter are determined as working characteristic parameters.

[0058] In an embodiment of the present application, the second operating characteristic parameter includes: a changing trend of particle density within one oscillation period, a changing trend of electron temperature within one oscillation period, and a changing trend of ion temperature within one oscillation period.

[0059] Corresponding to the above method, the embodiment of the present application also provides a device for determining the working characteristic parameters of a Hall thruster, such as Figure 2 As shown, the device for determining the operating characteristic parameters of the Hall thruster includes: An acquisition unit 210 is used to acquire target operating parameters of the Hall thruster; The prediction unit 220 is configured to input the target operating parameters into a pre-trained Hall thruster discharge oscillation prediction model to predict the operating parameter time series data of the Hall thruster under the target operating parameters and the spatiotemporal distribution data of the plasma in the channel of the Hall thruster. The Hall thruster discharge oscillation prediction model is trained using a plurality of historical simulation data. Any historical simulation data includes: historical operating parameters, corresponding historical operating parameter time series data, and corresponding historical spatiotemporal distribution data. The analysis unit 230 is used to analyze the time series data and the spatiotemporal distribution data of the operating parameters to obtain operating characteristic parameters.

[0060] The functions of the various functional units of the apparatus for determining the characteristic operating parameters of a Hall thruster provided in the above-described embodiments of the present application can be implemented through the above-described method steps. Therefore, the specific working processes and beneficial effects of the various units in the apparatus for determining the characteristic operating parameters of a Hall thruster provided in the embodiments of the present application are not further described here.

[0061] The present application also provides an electronic device, such as Figure 3 As shown, it includes a processor 310 , a communication interface 320 , a memory 330 and a communication bus 340 , wherein the processor 310 , the communication interface 320 , and the memory 330 communicate with each other via the communication bus 340 .

[0062] Memory 330, for storing computer programs; The processor 310 is configured to execute the program stored in the memory 330 by performing the following steps: Obtain target operating parameters of the Hall thruster; Inputting the target operating parameters into a pre-trained Hall thruster discharge oscillation prediction model to predict operating parameter time series data of the Hall thruster under the target operating parameters and spatiotemporal distribution data of plasma in a channel of the Hall thruster; The Hall thruster discharge oscillation prediction model is trained using multiple historical simulation data; any historical simulation data includes: historical operating parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data; The working parameter time series data and the spatiotemporal distribution data are analyzed to obtain working characteristic parameters.

[0063] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into address buses, data buses, and control buses. For ease of illustration, the figure uses only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0064] The communication interface is used for communication between the above electronic device and other devices.

[0065] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0066] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0067] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments to solve the problems can be found in Figure 1 The various steps in the embodiment shown are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0068] In another embodiment provided in the present application, a computer-readable storage medium is further provided. The computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the method for determining the operating characteristic parameters of a Hall thruster according to any of the above embodiments.

[0069] In another embodiment provided by the present application, a computer program product including instructions is also provided. When the computer program product is executed on a computer, the computer is caused to execute the method for determining the operating characteristic parameters of the Hall thruster according to any one of the above embodiments.

[0070] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the embodiments of the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0072] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0074] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0075] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims and their equivalents, the embodiments of the present application are also intended to include these modifications and variations.

Claims

1. A method for determining the operating characteristic parameters of a Hall thruster, characterized in that: The method comprises: Obtain target operating parameters of the Hall thruster; Inputting the target operating parameters into a pre-trained Hall thruster discharge oscillation prediction model to predict operating parameter time series data of the Hall thruster under the target operating parameters and spatiotemporal distribution data of plasma in a channel of the Hall thruster; The Hall thruster discharge oscillation prediction model is trained using multiple historical simulation data; any historical simulation data includes: historical operating parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data; The working parameter time series data and the spatiotemporal distribution data are analyzed to obtain working characteristic parameters.

2. The method according to claim 1, wherein The target operating parameters include: the channel configuration, channel dimensions, and wall material type of the Hall thruster, as well as the external electric field parameters, magnetic field parameters, cathode discharge current of the Hall thruster, and the type and flow rate of the neutral gas input into the Hall thruster; The working parameter time series data includes: discharge current time series data, thrust time series data and physical field time series data; The spatiotemporal distribution data of the plasma include: spatiotemporal distribution data of particle density, axial spatiotemporal distribution data of electron temperature, and axial spatiotemporal distribution data of ion temperature.

3. The method according to claim 2, wherein Analyzing the working parameter time series data and the spatiotemporal distribution data to obtain working characteristic parameters includes: Analyze the discharge current time series data and the thrust time series data to obtain a first operating characteristic parameter; wherein the first operating characteristic parameter includes: an oscillation period of the discharge oscillation; Extracting target physical field time series data and target spatiotemporal distribution data within one oscillation period from the physical field time series data and the spatiotemporal distribution data respectively; Analyzing the time series data of the target physical field to obtain a change trend of the physical field within an oscillation cycle; Analyzing the target spatiotemporal distribution data to obtain second operating characteristic parameters; wherein the second operating characteristic parameters include: a change trend of particle density within one oscillation cycle, a change trend of electron temperature within one oscillation cycle, and a change trend of ion temperature within one oscillation cycle; The first operating characteristic parameter, the change trend of the physical field within one oscillation period, and the second operating characteristic parameter are determined as operating characteristic parameters.

4. The method according to claim 3, wherein The discharge current time series data and the thrust time series data are analyzed to obtain the first operating characteristic parameters, including: Extracting a plurality of first peak values ​​and first valley values ​​from the discharge current time series data; determining an oscillation period of the discharge oscillation based on the extracted plurality of first peaks; extracting a plurality of second peak values ​​and second valley values ​​from the thrust time series data; The first peak value, the first valley value, the second peak value, the second valley value and the oscillation period are determined as first operating characteristic parameters.

5. The method according to claim 1, wherein Before obtaining the target operating parameters of the Hall thruster, the method further includes: Construct a one-dimensional full-particle numerical simulation model of the Hall thruster.

6. The method according to claim 5, wherein The method for obtaining historical simulation data includes: Get multiple historical operating parameters of the configuration; For any historical operating condition parameter, the historical operating condition parameter is input into the one-dimensional full-particle numerical simulation model to perform discharge simulation, and historical operating parameter time series data and corresponding historical spatiotemporal distribution data corresponding to the historical operating condition parameter are obtained.

7. The method according to claim 1, wherein The Hall thruster discharge oscillation prediction model includes: Input layer, used to receive target operating parameters; A feature embedding layer is used to extract features of the input target operating condition parameters to obtain a feature vector sequence and a first oscillation period; A time series prediction layer, configured to predict a time series of particle counts and an ionization rate within a preset time window based on a sequence of feature vectors inputted by the feature embedding layer; wherein the length of the preset time window is greater than the first oscillation period; The spatiotemporal distribution prediction layer is used to predict the spatiotemporal distribution data of plasma within a preset time window based on the feature vector sequence and the time series of the particle number; The working parameter prediction layer is used to predict the working parameter time series data within a preset time window based on the feature vector sequence and ionization rate time series, as well as the particle density spatiotemporal distribution data; a period verification layer for extracting the second oscillation period, the third oscillation period, and the fourth oscillation period based on the time series of the number of particles within a preset time window, the spatiotemporal distribution data of the plasma, and the time series data of the operating parameters; The output layer is used to output the time series data of the operating parameters of the Hall thruster under the target operating parameters and the spatiotemporal distribution data of the plasma in the channel of the Hall thruster when the difference between the first oscillation period, the second oscillation period, the third oscillation period and the fourth oscillation period is less than a configured threshold.

8. A device for determining the working characteristic parameters of a Hall thruster, characterized in that: The device comprises: An acquisition unit, used for acquiring target operating parameters of the Hall thruster; a prediction unit, configured to input the target operating condition parameters into a pre-trained Hall thruster discharge oscillation prediction model, and predict operating parameter time series data of the Hall thruster under the target operating condition parameters and spatiotemporal distribution data of plasma in a channel of the Hall thruster; wherein the Hall thruster discharge oscillation prediction model is trained using a plurality of historical simulation data; any historical simulation data includes: historical operating condition parameters and corresponding historical operating parameter time series data and corresponding historical spatiotemporal distribution data; The analyzing unit is used to analyze the working parameter time series data and the spatiotemporal distribution data to obtain working characteristic parameters.

9. An electronic device, characterized in that: The electronic device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method according to any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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