Plume process simulation method, device, electronic equipment and storage medium

Abstract

The present application provides a plume process simulation method, device, electronic device and storage medium, which relate to the field of aerospace simulation test technology. The method includes: obtaining a target model and a calculation domain corresponding to a target thruster; calculating the condition information of the target ions generated in the target model; dividing the target model and the calculation domain based on the condition information, and obtaining a target grid area, wherein the target grid area includes multiple target grid points; calculating the plume process of the target ions in the target grid area, and obtaining the simulation result of the plume process. The present application can simulate the entire process of ion plume emission in a thruster, and by calculating the conditions of the ions in the thruster model, it effectively improves the simulation accuracy of the plume process, and improves the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, providing a methodological basis for the thruster industry optimization design.

Description

Technical Field

[0001] The present application relates to the field of aerospace simulation testing technology, and in particular to a plume process simulation method, device, electronic equipment and storage medium. Background Art

[0002] Ionic liquid electrospray thruster is an electrostatic propulsion device that separates ionic liquid with a certain conductivity into charged ions or droplets through an electrostatic field. These ions or droplets are accelerated and ejected in the electrostatic field to generate thrust. Ionic liquid electrospray thruster has the advantages of high specific impulse, high thrust accuracy, low thrust noise, low power consumption and low structural mass, and is very suitable for deep space exploration missions.

[0003] The plume produced by ionic liquid electrospray thrusters will produce mechanical and thermal effects, pollution effects, and charging and discharging effects on spacecraft, threatening the operational safety and service life of spacecraft. Therefore, the study of plume effect is one of the key issues in the application of ionic liquid electrospray thrusters. Numerical simulation and experimental research methods can usually be used to explore the plume effect of ionic liquid electrospray thrusters.

[0004] Existing numerical simulation studies on ionic liquid electrospray thruster plumes mainly focus on the thruster ion and droplet formation stage, using molecular dynamics simulation methods or hybrid particle cloud grid methods. However, due to the limitation of computational efficiency, the molecular dynamics simulation method cannot simulate the entire process of plume development in the area where the plume is fully developed; although the hybrid particle cloud grid method can simulate the plume development on the scale of the entire star, the fluid approximation is used in the calculation. The fluid approximation can improve the computational efficiency, but it also reduces the accuracy of the simulation. Therefore, in the prior art, it is impossible to perform efficient and high-precision simulation of the entire process of ionic liquid electrospray thruster plume development. Summary of the invention

[0005] In view of this, an object of the embodiments of the present application is to provide a plume process simulation method, device, electronic device and storage medium to improve the problem of low accuracy of ion plume process simulation in the prior art.

[0006] In order to solve the above problems, in a first aspect, the present application provides a plume process simulation method, comprising:

[0007] Obtain the target model and calculation domain corresponding to the target thruster;

[0008] Determining condition information of target ions generated in the target model;

[0009] Based on the condition information, the target model and the computational domain are divided to obtain a target grid area, wherein the target grid area includes a plurality of target grid points;

[0010] The plume process of the target ion in the target grid area is calculated to obtain a simulation result of the plume process.

[0011] In the above implementation, by modeling a determined thruster, the corresponding target model and computational domain can be obtained to simulate the plume process of ions in the target model and computational domain. By calculating the conditional information of the target ions generated in the target model, dividing according to the conditional information, simulating the motion process of the target ions in the divided target grid area, and thus simulating the plume process of ions, the simulation accuracy and integrity of the plume process can be effectively improved, thereby improving the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, and providing a methodological basis for the optimization design of the thruster industry.

[0012] Optionally, the condition information includes an entry condition; the condition information for determining the target ions generated in the target model includes:

[0013] Acquiring target ions generated in the target model;

[0014] Determining the entrance data corresponding to the target ion, wherein the entrance data includes type data, current data, voltage data of the target ion and area data of the target thruster;

[0015] Importing entrance data into the target model, determining the entrance conditions of the target ions, wherein the entrance conditions include flux distribution data and velocity distribution data.

[0016] In the above implementation, the condition information may include specific entrance conditions, and the entrance conditions include flux distribution data and velocity distribution data when ions move. When determining the entrance conditions, the target ions generated in the target model are acquired, and the target ions have certain weights and can represent a certain number of real ions. By acquiring the entrance data corresponding to the target ions, the entrance data can be used as calculation parameters, imported into the target model for calculation, and the corresponding entrance conditions are obtained. The acquisition efficiency and accuracy of the entrance conditions are effectively improved to limit the movement process of ions when entering or leaving the target model.

[0017] Optionally, the condition information includes boundary conditions, and the boundary conditions include: potential boundary conditions and ion boundary conditions; the condition information for determining the target ions generated in the target model includes:

[0018] Acquiring target ions generated in the target model;

[0019] Determining the potential boundary condition according to the potential value or electric field value between the target ion and the boundary;

[0020] The ion boundary condition is determined according to the interaction relationship between the target ion and the calculation domain.

[0021] In the above implementation, the condition information may also include boundary conditions, and the boundary conditions may include potential boundary conditions of potential solution and ion boundary conditions between ions and boundaries. When calculating the boundary conditions, the target ions generated in the target model are acquired, and the target ions have certain weights and can represent a certain number of real ions. The potential boundary conditions can be determined by the potential value or electric field value between the target ions and the boundary, and the ion boundary conditions can be determined by the interaction relationship between the target ions and the boundary of the calculation domain. The acquisition efficiency and accuracy of the boundary conditions are effectively improved, thereby limiting the movement process of the target ions at the boundary of the target model and the calculation domain.

[0022] Optionally, dividing the target model and the computational domain based on the condition information to obtain a target grid area includes:

[0023] The target model and the computing domain are configured based on the condition information to obtain a conditional model and a conditional computing domain;

[0024] Obtaining a target Debye length of the target ion;

[0025] Determining grid size data based on the target Debye length;

[0026] The conditional model and the conditional calculation domain are divided based on the grid size data to obtain a target grid area.

[0027] In the above implementation, in order to improve the simulation accuracy of the target ion's motion process in the target model and the computational domain, the target model and the computational domain are configured based on the conditional information to obtain the configured conditional model and conditional computational domain, so as to define the entrances and exits and boundaries of the target model and the computational domain. Then, the Debye length of the target ion is obtained, and the appropriate grid size data is determined based on the obtained target Debye length. Based on the conditional model and the conditional computational domain, the grid size data is divided, and the conditional model and the conditional computational domain are divided into a target grid area with multiple grids and target grid points, so as to facilitate the subsequent simulation of the motion of the target ion.

[0028] Optionally, the simulation result includes charge interpolation data, potential data, electric field interpolation data and particle data of the target ion; and the plume process of the target ion in the target grid area is calculated to obtain the simulation result of the plume process, including:

[0029] Calculating a mapping process of the charge in the target grid area from the target ions to the target grid points to obtain the charge interpolation data in the plume process;

[0030] Calculating the potential distribution structure of the potential of the target ion in the target grid area to obtain the potential data in the plume process;

[0031] Calculating a mapping process of mapping the electric field of the target grid area from the target grid point to the target ion to obtain the electric field interpolation data in the plume process;

[0032] The position information and velocity information of the target ion in the target grid area are updated, and the updated position information and updated velocity information are used as the particle data in the plume process.

[0033] In the above implementation, when simulating the plume process of the target ion in the target grid area, multiple aspects of the movement process of the target ion can be calculated to simulate the overall plume process of the target ion and improve the integrity of the plume process. The simulation results of the plume process can include the charge interpolation data, potential data, electric field interpolation data and particle data of the target ion. By calculating the mapping relationship between the charge in the target grid area and the target ion, the corresponding charge interpolation data can be obtained; by calculating the TV distribution structure of the potential of the target ion in the target grid area, the corresponding potential data can be obtained; by calculating the mapping relationship between the electric field in the target grid area and the target ion, the corresponding electric field interpolation data can be obtained; by updating the position information and velocity information of the target ion, the corresponding particle data can be obtained. Thus, the plume process of the target ion is completely simulated from four aspects: charge density distribution, potential distribution, electric field distribution and particle propulsion.

[0034] Optionally, the method further comprises:

[0035] Recording the simulation results to obtain recorded data;

[0036] Drawing is performed based on the recorded data to obtain image data of the plume process.

[0037] In the above implementation, after obtaining the simulation results of the plume process of the target ion, the simulation results can also be recorded to record the movement process of the four aspects of charge density distribution, potential distribution, electric field distribution and particle propulsion, and obtain the corresponding recorded data, so that the simulation results can be processed and saved to provide a basis for subsequent operations. On the basis of the recorded data, line graphs, scatter plots, contour maps and other images are drawn to obtain the image data corresponding to the plume process, and the simulation results can be displayed in the form of images so that users can view and understand the plume process.

[0038] Optionally, the acquiring a target model and a calculation domain corresponding to the target thruster includes:

[0039] Obtain the size information and calculation domain information corresponding to the target thruster;

[0040] According to the size information, obtaining a target model corresponding to the target thruster;

[0041] According to the calculation domain information, a calculation domain corresponding to the target thruster is obtained.

[0042] In the above implementation, when acquiring the target model and the calculation domain, due to the different thruster models, the corresponding target model can be established according to the size information of the target thruster used for simulation, and the corresponding calculation domain can be established according to the calculation domain information of the calculation domain size corresponding to the target thruster, so as to acquire the target model and the calculation domain used in the simulation, so as to make the simulation more in line with the actual situation, improve the accuracy of the simulation, and can also be applicable to various models of thrusters to meet a variety of application scenarios.

[0043] In a second aspect, the present application also provides a plume process simulation device, the device comprising:

[0044] A modeling module, used to obtain a target model and a calculation domain corresponding to a target thruster;

[0045] A condition module, used to determine condition information of target ions generated in the target model;

[0046] A partitioning module, used for partitioning the target model and the computational domain based on the condition information to obtain a target grid area;

[0047] The simulation module is used to calculate the plume process of the target ion in the target grid area to obtain a simulation result of the plume process.

[0048] In the above implementation, the modeling model of the thruster is obtained through the modeling module to obtain the corresponding target model and calculation domain; the conditional information of the target ions generated in the target model is calculated through the conditional module; the target model and calculation domain are divided based on the conditional information through the partitioning module; the motion process of the target ions is simulated in the divided target grid area through the simulation module to obtain the simulation result of the plume process. The ability to simulate the plume process of ions effectively improves the simulation accuracy and integrity of the plume process, thereby improving the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, and providing a methodological basis for the optimization design of the thruster industry.

[0049] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps in any implementation of the above-mentioned plume process simulation method.

[0050] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the steps in any implementation of the above-mentioned plume process simulation method are executed.

[0051] In summary, the present application provides a plume process simulation method, device, electronic device and storage medium, which can simulate the entire process of ion plume emission in a thruster, and limit the ion plume process according to condition information, effectively improving the simulation accuracy and integrity of the plume process. This improves the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, and provides a methodological basis for the thruster industrial optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. 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 related drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of a flow chart of a plume process simulation method provided in an embodiment of the present application;

[0054] Figure 2 A detailed flow chart of step S1 provided in an embodiment of the present application;

[0055] Figure 3 A detailed flow chart of step S2 provided in an embodiment of the present application;

[0056] Figure 4 A detailed flowchart of another step S2 provided in an embodiment of the present application;

[0057] Figure 5 A detailed flow chart of step S3 provided in an embodiment of the present application;

[0058] Figure 6 A detailed flow chart of step S4 provided in an embodiment of the present application;

[0059] Figure 7 A schematic flow chart of another plume process simulation method provided in an embodiment of the present application;

[0060] Figure 8 A schematic diagram of image data of a plume process provided in an embodiment of the present application;

[0061] Fig. 9 A comparison chart of simulation results provided in an embodiment of the present application;

[0062] Fig.10 A schematic diagram of the module structure of a plume process simulation device provided in an embodiment of the present application.

[0063] Icons: 700 - plume process simulation device; 710 - modeling module; 720 - condition module; 730 - partition module; 740 - simulation module. DETAILED DESCRIPTION

[0064] The technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the embodiments of the present application.

[0065] An embodiment of the present application provides a plume process simulation method, which is applied to a server. The server can be a personal computer (PC), a tablet computer, a smart phone, a personal digital assistant (PDA), or other electronic device with logical computing functions, and can simulate the plume process of ions efficiently, completely, and with high precision.

[0066] See also Figure 1 , Figure 1 A schematic flow chart of a plume process simulation method provided in an embodiment of the present application, the method may include the following steps:

[0067] Step S1, obtaining a target model and a calculation domain corresponding to a target thruster.

[0068] Among them, the target thruster is an electrostatic propulsion device, which can be a variety of ionic liquid electrospray thrusters. The ionic liquid electrospray thruster separates ionic liquid with a certain conductivity into charged ions or droplets through an electrostatic field. These ions or droplets are accelerated and ejected in the electrostatic field to generate thrust. When simulating, since the actual sizes and dimensions of various types of thrusters are different, the target thruster used in the simulation is first determined, the target model and calculation domain corresponding to the target thruster are obtained, and the simulation is performed based on the target model and calculation domain.

[0069] Optionally, see Figure 2 , Figure 2 A detailed flow chart of step S1 provided in an embodiment of the present application, step S1 may also include steps S11-S13:

[0070] Step S11, obtaining the size information and calculation domain information corresponding to the target thruster.

[0071] After the target thruster is determined, the size information of the target thruster can be obtained. For example, the acquisition method can be to measure the target thruster to obtain the size information; or to search in a database according to the model information of the target thruster to obtain the size information. The corresponding calculation domain information can also be obtained according to the size information of the target thruster. The calculation domain information is information used to determine the size of the calculation domain in the established model. For example, the calculation domain information can be selected according to the size information, or matched in the database according to the model information of the target thruster.

[0072] Step S12: acquiring a target model corresponding to the target thruster according to the size information.

[0073] According to the size information of the target thruster, a target model of the target thruster is established to obtain a target model for simulation. The target model can be constructed and obtained for thrusters of different sizes, which is applicable to various types of thrusters and meets various application scenarios.

[0074] For example, the size information of the target thruster may be 0.01 m×0.1 m.

[0075] Step S13: acquiring the calculation domain corresponding to the target thruster according to the calculation domain information.

[0076] Among them, the calculation domain information can be data such as the size, boundary, shape, etc. of the calculation domain. On the basis of the calculation domain information, a corresponding calculation domain can be established. The calculation domain is a fictitious regular space corresponding to the physical domain of the target model. For example, the calculation domain can be a square calculation domain or a rectangular calculation domain established based on the calculation domain information.

[0077] For example, the calculation domain information may be a square calculation range of 5m×5m, etc.

[0078] exist Figure 2 In the illustrated embodiment, by establishing and acquiring the target model and the calculation domain used in the simulation, the simulation is made more consistent with the actual situation and the simulation accuracy is improved.

[0079] After executing step S1, continue to execute step S2.

[0080] Step S2, determining condition information of the target ions generated in the target model.

[0081] Among them, by calculating the condition information of the target ions, the movement process of the target ions can be limited based on the condition information, thereby improving the simulation accuracy of the target ion plume process.

[0082] Optionally, see Figure 3 , Figure 3 A detailed flow chart of step S2 provided in an embodiment of the present application, the condition information may include an entry condition, and step S2 may further include steps S21-S23:

[0083] Step S21, obtaining target ions generated in the target model.

[0084] Among them, the target thruster will generate a large number of ions during propulsion. During the simulation, the large number of ions generated in the target model can be processed into multiple target ions. The acquired target ions have a certain weight and can represent a certain number of real ions in the target thruster, thereby simulating the plume process of real ions.

[0085] Step S22, determining the entry data corresponding to the target ion.

[0086] The entrance data includes target ion type data, current data, voltage data and area data of the target thruster. For example, the entrance data may include: ion type, emission current data, acceleration voltage data, exit area data and entrance area data of the target thruster and other data.

[0087] Step S23, importing the entrance data into the target model to determine the entrance conditions of the target ions.

[0088] Among them, the inlet conditions include flux distribution data and velocity distribution data. Various data such as ion type, emission current data, acceleration voltage data, exit area data and inlet area data of the target thruster are used as calculation parameters and imported into the target model for calculation to obtain the corresponding inlet conditions.

[0089] Optionally, when calculating the flux distribution data, the calculation method may include:

[0090] τ=n i v i S;

[0091]

[0092] Among them, n i is the target ion number density determined from the emission current data obtained in the experiment, v i is the beam velocity determined based on the acceleration voltage data, S is the exit area of ​​the target thruster, and τ is the total flux data obtained by calculation. Among them, based on the total flux data, the flux distribution data f of the target ion τ (y) obeys Gaussian distribution.

[0093] Optionally, when calculating the velocity distribution data, the calculation method may include:

[0094]

[0095] Where v is the initial velocity of the target ion, f(v)dv is the velocity distribution data corresponding to v, f(v)dv obeys the Maxwell-Boltzmann distribution, T is the temperature of the target ion, generally T = 10 eV, μ is the average velocity, μ can be related to v i The values ​​of are the same, is the standard deviation, m i is the mass of the target ion.

[0096] exist Figure 3 In the illustrated embodiment, the acquisition efficiency and accuracy of the entrance conditions are effectively improved to define the movement process of the target ions when entering or leaving the target model.

[0097] Optionally, see Figure 4 , Figure 4 Another detailed flow diagram of step S2 provided in an embodiment of the present application, the condition information may further include boundary conditions, the boundary conditions may further include potential boundary conditions and ion boundary conditions, and step S2 may further include steps S24-S26:

[0098] Step S24, obtaining target ions generated in the target model.

[0099] The acquisition method is the same as that in step S21 and will not be described in detail.

[0100] Step S25, determining the potential boundary condition according to the potential value or electric field value between the target ion and the boundary.

[0101] Among them, the potential boundary conditions obtained based on the potential solution include the Dirichlet boundary condition and the Neumann boundary condition. When the potential value of the boundary where the target ion is located is a constant, the potential boundary condition is the Dirichlet boundary condition, that is, the first-class boundary condition; when the electric field value of the boundary where the target ion is located is a constant, the potential boundary condition is the Neumann boundary condition.

[0102] Step S26, determining the ion boundary condition according to the interaction relationship between the target ion and the calculation domain.

[0103] Among them, the ion boundary conditions between the ion and the boundary can include absorption boundaries and reflection boundaries. The ion boundary conditions can be determined based on the interaction relationship between the target ion and the boundary of the calculation domain. For example, if the target ion is deleted from the calculation domain after reaching the boundary of the calculation domain, the ion boundary condition is an absorption boundary; after the target ion reaches the boundary of the calculation domain, the target ion moves in the opposite direction along the normal velocity of the boundary, and the tangential velocity along the boundary remains unchanged, then the ion boundary condition is a reflection boundary.

[0104] exist Figure 4 In the illustrated embodiment, the efficiency and accuracy of obtaining boundary conditions can be improved, thereby limiting the movement process of target ions at the boundary of the target model and the calculation domain.

[0105] After executing step S2, continue to execute step S3.

[0106] Step S3: based on the condition information, divide the target model and the calculation domain to obtain a target grid area.

[0107] Among them, the target grid area includes multiple target grid points. Since the target model is a complete and larger area for the computational domain, the simulation accuracy is low when it is directly simulated in it. Therefore, the target model and the computational domain can be discretely divided into orthogonal grids based on the acquired conditional information to achieve division in size and direction, and divide the target model and the computational domain into a target grid area with multiple target grids and target grid points.

[0108] Optionally, the target grid corresponds to the target grid point, and the target grid may be a grid of various shapes such as a square grid or a rectangular grid.

[0109] Optionally, see Figure 5 , Figure 5 A detailed flow chart of step S3 provided in an embodiment of the present application, step S3 may also include steps S31-S34:

[0110] Step S31, configuring the target model and the computing domain based on the condition information to obtain a conditional model and a conditional computing domain.

[0111] Among them, the entrance conditions and boundary conditions in the conditional information can be used as conditional parameters and substituted into the target model and the computational domain, and the target model and the computational domain can be conditionally configured to obtain the conditional model and the conditional computational domain to limit the entrances, exits and boundaries of the target model and the computational domain.

[0112] For example, the flux distribution data and velocity distribution data in the inlet conditions can be imported into the target model, and the potential boundary conditions can be set to zero Newman boundary conditions, that is, the boundary electric field is 0, and the ion boundary conditions are determined to be absorption boundaries, and the potential boundary conditions and ion boundary conditions are also imported into the target model.

[0113] Step S32, obtaining a target Debye length of the target ion.

[0114] Since the types of target ions are different, the target Debye length corresponding to each target ion is also different. For example, the target Debye length of the target ion can be 10 -2 The length of the order of m can be obtained by measuring or looking up the target Debye length corresponding to the target ion.

[0115] Step S33: determining grid size data based on the target Debye length.

[0116] The grid size data may include data such as the size of the grid, the number of grids in the target model, and the number of grids in the computational domain. The grid size data is determined based on the target Debye length, combined with the size information of the target thruster and the computational domain information.

[0117] For example, the target thruster size information is 0.01m×0.1m, the calculation domain information is a square calculation range of 5m×5m, and the target Debye length of the target ion is 10 -2 When the length is of the order of m, the grid size can be 1×10 - 2 m, the number of grids in the target model can be 1×10, and the number of grids in the computational domain can be 500×500.

[0118] Step S34, dividing the conditional model and the conditional calculation domain based on the grid size data to obtain a target grid area.

[0119] The conditional model and the conditional calculation domain after the configuration condition information are divided according to the acquired grid size data. The division method can be based on orthogonal grid discrete division, dividing the conditional model and the conditional calculation domain into multiple target grids to obtain a target grid area composed of multiple target grids.

[0120] exist Figure 5 In the illustrated embodiment, the target model and the computational domain are divided into a target grid region having a plurality of grids and target grid points, so as to facilitate the subsequent simulation of the movement of the target ions.

[0121] After executing step S3, continue to execute step S4.

[0122] Step S4, calculating the plume process of the target ion in the target grid area to obtain a simulation result of the plume process.

[0123] Among them, when simulating the plume process of the target ion in the target grid area, the model can calculate many aspects of the movement process of the target ion to simulate the overall plume process of the target ion, improve the integrity of the plume process, and obtain a complete simulation result.

[0124] Optionally, see Figure 6 , Figure 6 A detailed flow chart of step S4 provided in an embodiment of the present application, step S4 may also include steps S41-S44:

[0125] Step S41 , calculating a mapping process of the charge in the target grid area from the target ions to the target grid points to obtain the charge interpolation data in the plume process.

[0126] Among them, based on the selected area weight method, the charge interpolation in the plume process can be calculated to obtain charge interpolation data that can represent the process of mapping the charge density from the continuous position distribution of the target ions to multiple discrete grid points in the target grid area.

[0127] Optionally, the charge interpolation data may be calculated by:

[0128]

[0129]

[0130]

[0131]

[0132] Among them, ρ i,j , i+1,j , i,j+1 , i+1,j+1 is the charge density of the target ion at multiple target grid points, ρ p is the ratio of the target ion charge to the target grid area, (x0, y0) is the difference between the target ion coordinates and the target grid point (i, j) coordinates, and the charge interpolation data is calculated based on the charge mapping process from the target ion to the target grid point. The above interpolation method can improve the accuracy of the charge interpolation data compared to the classic nearest neighbor interpolation algorithm.

[0133] Step S42, calculating the potential distribution structure of the potential of the target ion in the target grid area to obtain the potential data in the plume process.

[0134] Among them, the SuperLU algorithm can be selected to solve the calculation method of the discretized Poisson equation linear equation system to calculate the potential data, which may include:

[0135]

[0136] Where E is the self-consistent electric field generated by the charged target ion, ρ is the charge density, ε is the dielectric constant of the medium, the electric field E and the potential The following relationship exists:

[0137]

[0138] Therefore, we can get:

[0139]

[0140] Based on the above formula, the potential solution equation based on Cartesian grid is established, using the finite difference discretization format, which can include:

[0141]

[0142] Among them, (i, j) are the indexes in the x and y directions respectively, and (Δx, Δy) are the grid sizes in the two directions respectively. Based on the above discretization format, a sparse linear system can be obtained:

[0143] AX = B;

[0144] Among them, A is the coefficient matrix, X is the electric potential, and B is determined by the charge density distribution. In the potential solver, the coefficient matrix A is determined by the boundary conditions. Then the sparse linear system is solved using the Gauss-Seidel iteration method, LU decomposition, SuperLU algorithm and multigrid method to obtain the potential distribution results. The potential data is:

[0145]

[0146]

[0147] Step S43, calculating a mapping process of mapping the electric field of the target grid area from the target grid points to the target ions, and obtaining the electric field interpolation data in the plume process.

[0148] Among them, the area weight method can also be selected to calculate the electric field interpolation in the plume process to obtain electric field interpolation data that can represent the process of mapping the electric field from multiple discrete grid points in the target grid area to multiple continuous target ions. The calculation method is the same as the calculation method of the charge interpolation data, and will not be repeated here.

[0149] Step S44, updating the position information and velocity information of the target ion in the target grid area, and using the updated position information and updated velocity information as the particle data in the plume process.

[0150] The frog leaping algorithm or the Euler algorithm may be used to update the position information and velocity information of the target ions to obtain particle data representing the particle propulsion process of the target ions.

[0151] For example, when the leapfrog algorithm is used for updating, the calculation method may include:

[0152]

[0153]

[0154] Among them, m is the mass information of the target ion, v is the velocity information of the target ion; t is the initial time information, Δt is the changing time information, x is the target grid point; F(t) is the updated position information of the target ion, and F(t) is the updated velocity information of the target ion.

[0155] exist Figure 6 In the illustrated embodiment, the plume process of the target ions can be completely simulated from four aspects: charge density distribution, potential distribution, electric field distribution, and particle propulsion.

[0156] See also Figure 7 , Figure 7A schematic flow chart of another plume process simulation method provided in an embodiment of the present application. After step S4, the method may further include steps S5-S6:

[0157] Step S5, recording the simulation results to obtain recorded data.

[0158] The simulation results can be recorded to record the movement process of charge density distribution, potential distribution, electric field distribution and particle propulsion, and the position information, velocity information, ion mass information, ion charge information and ion type information of the target ions after movement can be recorded to obtain the corresponding recorded data. The simulation results can be processed and saved to provide a basis for subsequent operations.

[0159] Step S6, performing drawing based on the recorded data to obtain image data of the plume process.

[0160] Among them, on the basis of the recorded data, the simulation results of the target ions can be plotted as line graphs, scatter graphs, contour graphs and other images to obtain image data corresponding to the plume process, so as to display the charge density distribution, potential distribution, electric field distribution and other conditions through image data, so that users can view and understand the plume process.

[0161] For example, see Figure 8 , Figure 8 A schematic diagram of image data of a plume process provided in an embodiment of the present application, after plotting the recorded data, the potential obtained by normalizing the ion temperature at the plume moment in the target thruster can be obtained, for example, a distribution cloud diagram of the dimensionless potential at 60μs. Wherein, the x-axis and the y-axis represent the dimensionless coordinates in the calculation domain, respectively, representing the ratio of the length of the calculation domain to the target Debye length of the target ion.

[0162] Optionally, see Fig. 9 , Fig. 9 A comparison chart of simulation results provided in an embodiment of the present application. Among them, the thrust and specific impulse of the target thruster can be calculated by integration based on the speed and position information recorded in the recorded data, and compared with multiple historical experimental results in the database, the simulation error of the plume process can be calculated. For example, in Fig. 9 In the illustrated embodiment, the thrust error and the specific impulse error in the simulation results can be calculated, and the maximum thrust error obtained is 5.79%, and the maximum specific impulse error is 4.55%.

[0163] exist Figure 1In the illustrated embodiment, by calculating the conditional information of the target ions generated in the target model, dividing the target model according to the conditional information, simulating the movement process of the target ions in the divided target grid area, and thus simulating the plume process of the ions, the simulation accuracy and completeness of the plume process can be effectively improved, thereby improving the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, and providing a methodological basis for the thruster industrial optimization design.

[0164] See also Fig.10 , Fig.10 A schematic diagram of the module structure of a plume process simulation device provided in an embodiment of the present application, the plume process simulation device 700 includes:

[0165] A modeling module 710 is used to obtain a target model and a calculation domain corresponding to a target thruster;

[0166] A condition module 720, used to determine condition information of target ions generated in the target model;

[0167] A partitioning module 730 is used to partition the target model and the computational domain based on the condition information to obtain a target grid area;

[0168] The simulation module 740 is used to calculate the plume process of the target ion in the target grid area to obtain a simulation result of the plume process.

[0169] In an optional implementation, the condition information may include an entry condition, and the condition module 720 may further include an acquisition submodule, a determination submodule, and an import submodule;

[0170] An acquisition submodule, used for acquiring target ions generated in the target model;

[0171] A determination submodule, used to determine the entrance data corresponding to the target ion, wherein the entrance data includes the type data, current data, voltage data of the target ion and the area data of the target thruster;

[0172] The import submodule is used to import the entrance data into the target model to determine the entrance conditions of the target ions, wherein the entrance conditions include flux distribution data and velocity distribution data.

[0173] In an optional implementation, the condition information may further include boundary conditions, and the boundary conditions may include: potential boundary conditions and ion boundary conditions, and the condition module 720 may further include an acquisition submodule, a potential submodule, and an ion submodule;

[0174] An acquisition submodule, used for acquiring target ions generated in the target model;

[0175] A potential submodule, used to determine the potential boundary condition according to the potential value or electric field value between the target ion and the boundary;

[0176] The ion submodule is used to determine the ion boundary condition according to the interaction relationship between the target ion and the calculation domain.

[0177] In an optional implementation, the division module 730 may also include a configuration submodule, a length submodule, a size submodule, and a division submodule;

[0178] A configuration submodule, configured to configure the target model and the computing domain based on the condition information to obtain a conditional model and a conditional computing domain;

[0179] A length submodule, used for obtaining a target Debye length of the target ion;

[0180] A size submodule, for determining grid size data based on the target Debye length;

[0181] The division submodule is used to divide the conditional model and the conditional calculation domain based on the grid size data to obtain a target grid area.

[0182] In an optional embodiment, the simulation result may include charge interpolation data, potential data, electric field interpolation data and particle data of the target ion; the simulation module 740 may also include a charge submodule, a potential distribution submodule, an electric field submodule and a particle submodule;

[0183] A charge submodule, used for calculating a mapping process of the charge in the target grid area from the target ion to the target grid point, to obtain the charge interpolation data in the plume process;

[0184] A potential distribution submodule, used for calculating the potential distribution structure of the potential of the target ion in the target grid area to obtain the potential data in the plume process;

[0185] An electric field submodule, used for calculating a mapping process of mapping the electric field of the target grid area from the target grid point to the target ion, and obtaining the electric field interpolation data in the plume process;

[0186] The particle submodule is used to update the position information and velocity information of the target ion in the target grid area, and use the updated position information and updated velocity information as the particle data in the plume process.

[0187] In an optional embodiment, the plume process simulation device 700 may further include a recording module and a drawing module;

[0188] A recording module, used for recording the simulation results to obtain recorded data;

[0189] A drawing module is used to draw based on the recorded data to obtain image data of the plume process.

[0190] In an optional embodiment, the modeling module 710 may also include an information submodule and a building submodule;

[0191] An information submodule is used to obtain the size information and calculation domain information corresponding to the target thruster;

[0192] A submodule is established to obtain a target model corresponding to the target thruster according to the size information; and a calculation domain corresponding to the target thruster according to the calculation domain information.

[0193] Since the principle of solving the problem by the plume process simulation device in the embodiment of the present application is similar to that of the aforementioned embodiment of the plume process simulation method, the implementation of the device in this embodiment can refer to the description in the embodiment of the aforementioned method, and the repeated parts will not be repeated.

[0194] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps of any one of the plume process simulation methods provided in this embodiment.

[0195] It should be understood that the electronic device may be a personal computer, a tablet computer, a smart phone, a personal digital assistant, or other electronic device with logic computing capabilities.

[0196] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of any one of the methods for simulating the plume process provided in the present embodiment are executed.

[0197] In summary, the embodiments of the present application provide a plume process simulation method, device, electronic device and storage medium, which can simulate the entire process of ion plume emission in a thruster, and limit the ion plume process according to condition information, effectively improving the simulation accuracy and integrity of the plume process. This improves the cognitive accuracy of the plume effect in the thruster at the microscopic mechanism level, and provides a methodological basis for the thruster industrial optimization design.

[0198] In several embodiments provided in the present application, it should be understood that the disclosed device can also be implemented in other ways. The device embodiments described above are merely schematic, for example, the block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices according to the multiple embodiments of the present application. In this regard, each box in the block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram, and the combination of the block diagrams, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0199] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0200] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Therefore, this embodiment also provides a computer program instruction stored in a readable storage medium, and when the computer program instruction is read and executed by a processor, the steps in any method described in the block data storage method are executed. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RanDom Access Memory), disk or optical disk and other media that can store program codes.

[0201] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0202] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

[0203] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A plume process simulation method, characterized in that: include: Obtain the target model and calculation domain corresponding to the target thruster; Determining condition information of target ions generated in the target model; Based on the condition information, the target model and the computational domain are divided to obtain a target grid area, wherein the target grid area includes a plurality of target grid points; Calculating the plume process of the target ion in the target grid area to obtain a simulation result of the plume process; Wherein, the condition information includes an entrance condition; the condition information of determining the target ions generated in the target model includes: acquiring the target ions generated in the target model; determining the entrance data corresponding to the target ions, wherein the entrance data includes the type data, current data, voltage data of the target ions and the area data of the target thruster; importing the entrance data into the target model to determine the entrance condition of the target ions, wherein the entrance condition includes flux distribution data and velocity distribution data; Wherein, the condition information includes boundary conditions, and the boundary conditions include: potential boundary conditions and ion boundary conditions; the condition information for determining the target ions generated in the target model includes: obtaining the target ions generated in the target model; determining the potential boundary conditions according to the potential value or electric field value between the target ions and the boundary; determining the ion boundary conditions according to the interaction relationship between the target ions and the calculation domain; The method of dividing the target model and the computational domain based on the conditional information to obtain a target grid area includes: configuring the target model and the computational domain based on the conditional information to obtain a conditional model and a conditional computational domain; obtaining a target Debye length of the target ion; determining grid size data based on the target Debye length; and dividing the conditional model and the conditional computational domain based on the grid size data to obtain a target grid area.

2. The method according to claim 1, characterized in that The simulation result includes charge interpolation data, potential data, electric field interpolation data and particle data of the target ion; the plume process of the target ion in the target grid area is calculated to obtain the simulation result of the plume process, including: Calculating a mapping process of the charge in the target grid area from the target ions to the target grid points to obtain the charge interpolation data in the plume process; Calculating the potential distribution structure of the potential of the target ion in the target grid area to obtain the potential data in the plume process; Calculating a mapping process of mapping the electric field of the target grid area from the target grid point to the target ion to obtain the electric field interpolation data in the plume process; The position information and velocity information of the target ion in the target grid area are updated, and the updated position information and updated velocity information are used as the particle data in the plume process.

3. The method according to claim 2, characterized in that The method further comprises: Recording the simulation results to obtain recorded data; Drawing is performed based on the recorded data to obtain image data of the plume process.

4. The method according to claim 1, characterized in that: The step of obtaining the target model and calculation domain corresponding to the target thruster includes: Obtain the size information and calculation domain information corresponding to the target thruster; According to the size information, obtaining a target model corresponding to the target thruster; According to the calculation domain information, a calculation domain corresponding to the target thruster is obtained.

5. A plume process simulation device, characterized in that: The device comprises: A modeling module, used to obtain a target model and a calculation domain corresponding to a target thruster; A condition module, used to determine condition information of target ions generated in the target model; A partitioning module, used for partitioning the target model and the computational domain based on the condition information to obtain a target grid area; A simulation module, used for calculating the plume process of the target ion in the target grid area to obtain a simulation result of the plume process; Wherein, the condition information includes an entrance condition; the condition module is specifically used to: obtain the target ions generated in the target model; determine the entrance data corresponding to the target ions, wherein the entrance data includes the type data, current data, voltage data of the target ions and the area data of the target thruster; import the entrance data into the target model to determine the entrance condition of the target ions, wherein the entrance condition includes flux distribution data and velocity distribution data; Wherein, the condition information includes boundary conditions, and the boundary conditions include: potential boundary conditions and ion boundary conditions; the condition module is specifically used to: obtain the target ions generated in the target model; determine the potential boundary conditions according to the potential value or electric field value between the target ions and the boundary; determine the ion boundary conditions according to the interaction relationship between the target ions and the calculation domain; The division module is specifically used to: configure the target model and the calculation domain based on the condition information to obtain the conditional model and the conditional calculation domain; obtain the target Debye length of the target ion; determine the grid size data based on the target Debye length; divide the conditional model and the conditional calculation domain based on the grid size data to obtain the target grid area.

6. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores program instructions, and when the processor runs the program instructions, the steps in the method according to any one of claims 1 to 4 are executed.

7. A computer-readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the steps in the method according to any one of claims 1 to 4 are executed.

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