Multi-scale simulation method and device for dielectric breakdown of composite material

Abstract

The invention relates to the technical field of electrical engineering, in particular to a composite material dielectric breakdown multi-scale simulation method and device, and the method comprises the steps: calculating the electric field intensity of a grid based on a preset geometric structure of a nano composite material, and calculating the electric field intensity of the grid based on material parameters and structure parameters of the nano composite material; calculating the molecular interaction energy of the interface between the nanofiller and the matrix; calculating a target breakdown probability of the grid by utilizing the electric field intensity and molecular interaction energy, generating a target state parameter of the grid based on the target breakdown probability, determining a target breakdown strength value meeting a condition by utilizing the target state parameter, and performing Weber distribution fitting processing on the target breakdown strength value to obtain a target breakdown strength value; and determining a simulation result of the breakdown strength of the nano composite material. Therefore, the problems that most of breakdown simulation methods in the prior art are single-scale models, the interfacial effect is not fully considered, errors in the aspect of quantitative prediction of the breakdown strength are large, and the accuracy of breakdown simulation is reduced are solved.

Description

Technical Field

[0001] The present application relates to the field of electrical engineering technology, and in particular to a multi-scale simulation method and device for dielectric breakdown of composite materials. Background Art

[0002] Nanocomposites have become an important class of dielectric materials for the next generation of energy storage devices and advanced electrical systems. Breakdown strength, a key indicator of the dielectric performance of nanocomposites, reflects their dielectric reliability under extreme electric field conditions. Improving breakdown strength is a hot topic in nanocomposite dielectric research. The breakdown of nanocomposites is a complex process, and its physical mechanisms are difficult to fully understand through experimental studies alone. As the structural design of nanocomposites becomes increasingly complex, breakdown simulation has become an indispensable tool for predicting breakdown performance and exploring breakdown mechanisms, and is increasingly being applied to the design of high-performance nanocomposite dielectric materials.

[0003] In recent years, significant progress has been made in breakdown simulation methods, particularly stochastic models and phase-field models. Due to their advantages in simulating the evolution and expansion of breakdown paths, they have become the most widely used breakdown simulation methods. However, existing breakdown simulation methods still have some problems. First, in addition to the mesostructure of nanocomposites, the microscopic interface between the matrix and filler also has a significant impact on the breakdown performance of nanocomposites. Existing models lack consideration of interfacial effects or oversimplify their consideration. For example, in phase-field models, the interface is simply treated as an independent phase with a dielectric constant different from that of the matrix and filler phases. This setting makes it difficult to reflect the differences in microscopic interface properties (such as molecular interaction energy at the interface) when different material choices (different matrix and filler materials) and different filler parameters (different filler shapes and sizes, etc.) are selected. The mechanism by which the interface affects breakdown is very complex and difficult to fully describe at a single scale. Both stochastic models and phase-field models are single-scale models at the mesoscopic scale.

[0004] Secondly, random models and phase-field models usually estimate the breakdown strength based on the number of iterations or the length of the breakdown path, resulting in less than ideal quantitative prediction performance for the breakdown strength of nanocomposites. The length of the breakdown path is affected by multiple factors, including material structure, material properties, and random factors. Generally, the more complex the breakdown path, the longer the path length and the greater the number of iterations. Measuring the breakdown strength by the number of iterations or path length is equivalent to assuming that the breakdown strength is positively correlated with the complexity of the breakdown path. In materials with lower breakdown strength, complex and longer breakdown paths may also occur. Therefore, this method of estimating the breakdown strength is not reasonable, and the estimated breakdown strength value has a large error.

[0005] Therefore, the breakdown simulation methods in related technologies are mostly single-scale models, which do not fully consider the interface effect, resulting in large errors in the quantitative prediction of breakdown strength, reducing the accuracy of breakdown simulation, and failing to better guide the design of high-performance nanocomposite dielectric materials, which urgently needs to be solved. Summary of the Invention

[0006] The present application provides a multi-scale simulation method and device for dielectric breakdown of composite materials to solve the problem that the breakdown simulation methods in related technologies are mostly single-scale models, which do not fully consider the interface effect, resulting in large errors in the quantitative prediction of breakdown strength and reducing the accuracy of breakdown simulation.

[0007] The first aspect of the present application provides a multi-scale simulation method for dielectric breakdown of composite materials, comprising the following steps: calculating the electric field strength of each grid in the nanocomposite material based on a preset geometric structure of the nanocomposite material; calculating the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material based on the material parameters and structural parameters of the nanocomposite material; calculating the target breakdown probability of each grid using the electric field strength and the molecular interaction energy, and generating target state parameters for each grid based on the target breakdown probability of each grid, and determining a target breakdown strength value that meets preset conditions using the target state parameters, and performing Weibull distribution fitting processing on the target breakdown strength value to determine a simulation result of the breakdown strength of the nanocomposite material.

[0008] Optionally, in one embodiment of the present application, before calculating the electric field strength of each grid in the nanocomposite material, it also includes: based on a pre-constructed multi-scale breakdown simulation model, setting the material parameters and the structural parameters of the nanocomposite material, and setting the target simulation number of the multi-scale breakdown simulation model; judging whether the actual simulation number of the multi-scale breakdown simulation model is less than the target simulation number; if the actual simulation number is less than the target simulation number, determining the geometric structure of the nanocomposite material; if the actual simulation number is greater than or equal to the target simulation number, determining the corresponding number of breakdown strength values according to the actual simulation number; and performing the Weibull distribution fitting process on the corresponding number of breakdown strength values to generate a simulation result of the breakdown strength of the nanocomposite material.

[0009] Optionally, in one embodiment of the present application, the use of the target state parameters to determine the target breakdown strength value that meets preset conditions includes: determining a random number of at least one grid, and using the random number to determine the state parameter of at least one grid; judging whether the state parameter is in the target active state, wherein, if the state parameter is in the target active state, all adjacent grids in the target active state are connected to determine the active state grid; based on the active state grid, judging whether the nano-composite material undergoes breakdown behavior; if the nano-composite material undergoes the breakdown behavior, obtaining the current external electric field strength value of the nano-composite material, and using the current external electric field strength value as the target breakdown strength value.

[0010] Optionally, in one embodiment of the present application, the target breakdown probability of each grid is calculated as follows:

[0011]

[0012] Among them, E i The electric field strength of each grid, Q i The breakdown probability of each grid, U int is the molecular interaction energy at the interface, T is the temperature, v f is the volume fraction of the filler, k is the Boltzmann constant, a and b are adjustment coefficients, α Ei and β Ei are the Weibull distribution parameters of the material breakdown strength at the i-th grid, α Qi and β Qi are the Weibull distribution parameters of the mesh breakdown probability of the material at the i-th mesh.

[0013] The second aspect of the present application provides a multi-scale simulation device for dielectric breakdown of composite materials, including: a first calculation module, used to calculate the electric field strength of each grid in the nanocomposite material based on a preset geometric structure of the nanocomposite material; a second calculation module, used to calculate the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material based on the material parameters and structural parameters of the nanocomposite material; a simulation module, used to calculate the target breakdown probability of each grid using the electric field strength and the molecular interaction energy, and generate the target state parameters of each grid based on the target breakdown probability of each grid, and use the target state parameters to determine the target breakdown strength value that meets the preset conditions, and perform Weibull distribution fitting processing on the target breakdown strength value to determine the simulation result of the breakdown strength of the nanocomposite material.

[0014] Optionally, in one embodiment of the present application, it also includes: a setting module for setting the material parameters and the structural parameters of the nanocomposite material based on a pre-constructed multi-scale breakdown simulation model before calculating the electric field strength of each grid in the nanocomposite material, and setting the target simulation number of the multi-scale breakdown simulation model; a judgment module for judging whether the actual simulation number of the multi-scale breakdown simulation model is less than the target simulation number before calculating the electric field strength of each grid in the nanocomposite material; a first processing module for determining the geometric structure of the nanocomposite material if the actual simulation number is less than the target simulation number before calculating the electric field strength of each grid in the nanocomposite material; a second processing module for determining the corresponding number of breakdown strength values according to the actual simulation number if the actual simulation number is greater than or equal to the target simulation number before calculating the electric field strength of each grid in the nanocomposite material; a determination module for performing the Weibull distribution fitting processing on the corresponding number of breakdown strength values before calculating the electric field strength of each grid in the nanocomposite material to determine the simulation result of the breakdown strength of the nanocomposite material.

[0015] Optionally, in one embodiment of the present application, the simulation module includes: a determination unit, used to determine a random number of at least one grid, and use the random number to determine the state parameter of at least one grid; a first judgment unit, used to determine whether the state parameter is in the target active state, wherein, if the state parameter is in the target active state, all adjacent grids in the target active state are connected to determine the active state grid; a second judgment unit, used to determine whether the nano-composite material undergoes breakdown behavior based on the active state grid; a processing unit, used to obtain the current external electric field strength value of the nano-composite material if the nano-composite material undergoes breakdown behavior, and use the current external electric field strength value as the target breakdown strength value.

[0016] Optionally, in one embodiment of the present application, the target breakdown probability of each grid is calculated as follows:

[0017]

[0018] Among them, E i The electric field strength of each grid, Q i The breakdown probability of each grid, U int is the molecular interaction energy at the interface, T is the temperature, v f is the volume fraction of the filler, k is the Boltzmann constant, a and b are adjustment coefficients, α Ei and β Ei are the Weibull distribution parameters of the material breakdown strength at the i-th grid, αQi and β Qi are the Weibull distribution parameters of the mesh breakdown probability of the material at the i-th mesh.

[0019] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a multi-scale simulation method for dielectric breakdown of composite materials as described in the above embodiment.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned multi-scale simulation method for dielectric breakdown of composite materials.

[0021] The fifth aspect of the present application provides a computer program product, including a computer program. When the computer program is executed, it is used to implement the above-mentioned multi-scale simulation method for dielectric breakdown of composite materials.

[0022] The embodiment of the present application can calculate the electric field strength of each grid in the nanocomposite material and the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material, respectively. Then, the target breakdown probability of each grid is calculated using the electric field strength and the molecular interaction energy, and the target breakdown strength value that meets certain conditions is further determined. The target breakdown strength value is subjected to Weibull distribution fitting processing to determine the simulation result of the breakdown strength of the nanocomposite material, effectively reducing the error of the breakdown simulation and improving the accuracy of the breakdown simulation. Thus, the problem that the breakdown simulation methods in the related art are mostly single-scale models and do not fully consider the interface effect, resulting in large errors in the quantitative prediction of the breakdown strength, reducing the accuracy of the breakdown simulation, and failing to better guide the design of high-performance nanocomposite dielectric materials is solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a multi-scale simulation method for dielectric breakdown of composite materials provided according to an embodiment of the present application;

[0026] Figure 2 This is a principle block diagram of a multi-scale breakdown simulation according to a specific embodiment of the present application;

[0027] Figure 3 A geometric structure randomly generated by Monte Carlo simulation in a specific embodiment of the present application;

[0028] Figure 4 This is a schematic diagram of the results of Weibull distribution fitting according to a specific embodiment of the present application;

[0029] Figure 5 This is a schematic diagram of the distribution of the internal electric field strength of a specific embodiment of the present application;

[0030] Figure 6 This is a schematic diagram of the grid breakdown probability distribution of a specific embodiment of the present application;

[0031] Figure 7 This is a breakdown path diagram of a specific embodiment of the present application;

[0032] Figure 8 This is a flow chart of a multi-scale simulation method for dielectric breakdown of composite materials according to a specific embodiment of the present application;

[0033] Figure 9 Schematic diagram of the structure of a multi-scale simulation device for dielectric breakdown of composite materials provided according to an embodiment of the present application;

[0034] Figure 10 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0036] The following describes a composite dielectric breakdown multi-scale simulation method and device according to an embodiment of the present application with reference to the accompanying drawings. In view of the fact that the breakdown simulation methods in the related art mentioned in the background technology center are mostly single-scale models, the consideration of interface effects is not sufficient, resulting in large errors in the quantitative prediction of breakdown strength, reducing the accuracy of breakdown simulation, and being unable to better guide the design of high-performance nanocomposite dielectric materials, the present application provides a composite dielectric breakdown multi-scale simulation method, in which the electric field strength of each grid in the nanocomposite and the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite can be calculated respectively, then, the target breakdown probability of each grid is calculated using the electric field strength and the molecular interaction energy, and the target breakdown strength value that meets certain conditions is further determined, and the target breakdown strength value is subjected to Weibull distribution fitting processing to determine the simulation result of the breakdown strength of the nanocomposite, effectively reducing the error of the breakdown simulation and improving the accuracy of the breakdown simulation. Thus, the problem that the breakdown simulation methods in the related art are mostly single-scale models, the consideration of interface effects is not sufficient, resulting in large errors in the quantitative prediction of breakdown strength and reducing the accuracy of the breakdown simulation is solved.

[0037] Specifically, Figure 1 A schematic flow chart of a multi-scale simulation method for dielectric breakdown of composite materials provided in an embodiment of the present application.

[0038] like Figure 1 As shown, the multi-scale simulation method for dielectric breakdown of composite materials includes the following steps:

[0039] In step S101 , the electric field intensity of each grid in the nanocomposite material is calculated based on a preset geometric structure of the nanocomposite material.

[0040] In the embodiments of this application, Figure 2 As shown, first set the MSBS (Multi-Scale Breakdown Simulation) model in the following steps. This model fully considers the influence of structural design on breakdown at the mesoscale and the influence of interface on breakdown at the microscale. The embodiment of the present application performs multi-scale simulation of dielectric breakdown based on this model.

[0041] It can be understood that the embodiments of the present application can be based on the pre-set geometric structure of the nanocomposite material. For example, the nanocomposite material can be first simulated at a mesoscopic scale, that is, the material geometry for finite element calculation is generated based on the structural parameters of the nanocomposite material. Then, the finite element method (FEM) is used to calculate the electric field strength of each grid in the nanocomposite material, which effectively improves the feasibility of multi-scale simulation of dielectric breakdown.

[0042] Optionally, in one embodiment of the present application, before calculating the electric field strength of each grid in the nanocomposite material, it also includes: setting the material parameters and structural parameters of the nanocomposite material based on a pre-constructed multi-scale breakdown simulation model, and setting the target simulation number of the multi-scale breakdown simulation model; judging whether the actual simulation number of the multi-scale breakdown simulation model is less than the target simulation number; if the actual simulation number is less than the target simulation number, determining the geometric structure of the nanocomposite material; if the actual simulation number is greater than or equal to the target simulation number, determining the corresponding number of breakdown strength values according to the actual simulation number; and performing Weibull distribution fitting processing on the corresponding number of breakdown strength values to generate a simulation result of the breakdown strength of the nanocomposite material.

[0043] In the embodiment of the present application, the actual number of simulations is the number of simulations performed; the target number of simulations is the total number of multi-scale breakdown simulations.

[0044] In the actual implementation process, Figure 2 As shown, first, the embodiment of the present application can construct a multi-scale breakdown simulation model, and then set the material parameters of the nanomaterial, including the type of matrix material and filler material, relative dielectric constant, Weibull distribution parameter α of the material breakdown strength E (where α E that is, the breakdown strength of the material) and β E , and the Weibull distribution parameter α of the grid breakdown probability Q and β Q ; Secondly, set the structural parameters of the nanomaterial, including the shape, size, and content of the filler; thirdly, set the total number N of multi-scale breakdown simulations, and initialize the number of simulations n = 1.

[0045] Furthermore, the embodiment of the present application can determine whether the multi-scale breakdown model has completed the simulation. For example, when the number of simulations n is less than N, it means that the simulation is not completed, and the nth simulation is started, that is, the structural parameters of the nanomaterial are generated, the material geometry for finite element calculation is generated, and the initial external electric field strength E0 is set, so that the electric field strength of each grid in the nanocomposite material can be accurately calculated.

[0046] In some embodiments, when the number of simulations of the multi-scale breakdown model n ≥ N, it indicates that the simulation is completed. For example, when the current number of simulations is n = N, the present application can obtain N breakdown strength values according to the number of simulations N; perform Weibull distribution fitting on the N breakdown strength values to obtain Weibull distribution parameters α and , where the breakdown strength E of the material is b =α.

[0047] In step S102 , the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material is calculated based on the material parameters and structural parameters of the nanocomposite material.

[0048] It is understandable that the embodiments of the present application can be based on the material parameters and structural parameters of the nanocomposite material set in the above steps, such as Figure 2 As shown, the molecular dynamics (MD) method is used to calculate the molecular interaction energy at the interface between the nanofiller and the matrix, that is, to perform microscale simulation of the nanomaterial, which effectively improves the feasibility of multi-scale simulation of dielectric breakdown.

[0049] In step S103, the target breakdown probability of each grid is calculated using the electric field intensity and the molecular interaction energy, and based on the target breakdown probability of each grid, the target state parameters of each grid are generated, and the target state parameters are used to determine the target breakdown strength value that meets the preset conditions, and the target breakdown strength value is subjected to Weibull distribution fitting processing to determine the simulation result of the breakdown strength of the nanocomposite material.

[0050] In the embodiment of the present application, the target breakdown probability includes the breakdown probability of the non-interface grid and the breakdown probability of the interface grid. Figure 2 As shown in the multi-scale breakdown simulation model constructed in the embodiment of the present application, the nanocomposite material is modeled as an ideal parallel plate capacitor with an applied electric field E0 in the z direction; the nanocomposite material is gridded, the grid located in the interface region is called the interface grid, and the other grids are called the non-interface grids; wherein the electric field intensity and breakdown probability of the i-th grid are respectively denoted as E i and Q i .

[0051] It is understandable that the embodiment of the present application can utilize the electric field strength E i and molecular interaction energy U int Calculate the breakdown probability Q of each grid i , for example, let α Ei , β Ei 、ɑ Qi , β Qi are the ɑ of the material at the i-th grid E , β E 、ɑ Q , β Q Parameters, k is the Boltzmann constant, T is the temperature, v f is the volume fraction of the filler, a and b are the adjustment coefficients, and the breakdown probability Q of the non-interface mesh and the interface mesh is calculated according to the following formula i ,Right now:

[0052]

[0053] Among them, E i The electric field strength of each grid, Q i The breakdown probability of each grid, U intis the molecular interaction energy at the interface, T is the temperature, v f is the volume fraction of the filler, k is the Boltzmann constant, a and b are adjustment coefficients, α Ei and β Ei are the Weibull distribution parameters of the material breakdown strength at the i-th grid, α Qi and β Qi are the Weibull distribution parameters of the mesh breakdown probability of the material at the i-th mesh.

[0054] Next, the embodiment of the present application can obtain a set of breakdown strength values based on the target breakdown probability of each grid through multiple Monte Carlo simulations, that is, when the number of simulations n is greater than or equal to the total number of multi-scale breakdown simulations N, N breakdown strength values are determined, and the N breakdown strength values are subjected to Weibull distribution fitting processing to determine the simulation results of the breakdown strength of the nanocomposite material, that is, to obtain Weibull distribution parameters α and β, wherein the breakdown strength E of the material is b =α, the breakdown path is also simulated at the same time, so that the embodiment of the present application can use the multi-scale breakdown simulation (MSBS) model to fully consider the influence of structural design on breakdown at the mesoscale and the influence of interface on breakdown at the microscale, and perform dielectric breakdown simulation of composite materials from multiple scales, effectively reducing the error of breakdown simulation, improving the accuracy of breakdown simulation, and better guiding the design of high-performance nanocomposite dielectric materials.

[0055] Optionally, in one embodiment of the present application, the target state parameter is used to determine a target breakdown strength value that meets preset conditions, including: determining a random number of at least one grid, and using the random number to determine the state parameter of at least one grid; judging whether the state parameter is in a target active state, wherein, if the state parameter is in the target active state, all adjacent grids in the target active state are connected to determine the active state grid; based on the active state grid, judging whether the nano-composite material undergoes breakdown behavior; if the nano-composite material undergoes breakdown behavior, obtaining the current external electric field strength value of the nano-composite material, and using the current external electric field strength value as the target breakdown strength value.

[0056] As a possible way to achieve this, Figure 2 As shown, the embodiment of the present application can perform multiple Monte Carlo simulations to obtain the grid state. For example, in the MSBS model, for each grid, there are two possible states, namely, the normal state and the active state. Let S i is the state parameter of the ith grid. When the ith grid is in normal state, S i =0; when the i-th grid is active, S i =1.

[0057] Among them, according to the target grid breakdown probability Q i, the state parameter S of the randomly generated grid i The Monte Carlo method is: For grid i, generate a random number ξ i ,ξ i Uniformly distributed in [0,1]; using ξ i The value of S i If the value of ξ i >Q i , then S i = 0, if ξ i ≤Q i , then S i =1.

[0058] Then, it will be in active state (S i =1) are connected to determine whether the nanomaterial has broken down. When the connected active state grids can form at least one path connected from top to bottom (through the entire material), it is determined that the material has broken down, and the connected path is the breakdown path. Then, the external electric field strength E0 at this time is recorded as the breakdown strength value of the nth simulation, and n=n+1 is set to determine whether the multi-scale breakdown simulation model has completed the simulation. For example, if the number of simulations n≥the total number of simulations N, it means that the simulation is completed. The present application can obtain N breakdown strength values according to the number of simulations N, and perform Weibull distribution fitting processing on the N breakdown strength values to obtain Weibull distribution parameters α and , wherein the breakdown strength E of the material is b =α, and the breakdown path is also simulated at the same time, which effectively reduces the error of the breakdown simulation and improves the accuracy of the breakdown simulation.

[0059] In addition, when the connected active state grids cannot form at least one path that is connected from top to bottom (through the entire material), it is determined that there is no breakdown, and the external electric field strength E0 is increased, and the mesoscale simulation and microscale simulation are performed again.

[0060] For example, the embodiment of the present application performs a breakdown simulation of a PVDF / BTO nanoparticle composite material. First, the material parameters are set. The matrix material type is PVDF, its relative dielectric constant is 10, and the Weibull distribution parameter ɑ of the material breakdown strength is E and β E The Weibull distribution parameter α of the grid breakdown probability is 370MV / m and 25 respectively. Q and β Q 0.2 and 52.3 respectively; the filler material is BaTiO3 (BTO), its relative dielectric constant is 1000, and the Weibull distribution parameter α of the material breakdown strength is E and β E The Weibull distribution parameter α of the grid breakdown probability is 50MV / m and 25 respectively. Q and β Q0.14 and 13 respectively.

[0061] Next, the structural parameters were set, and the shape of the filler was nanoparticles, the size thereof was a radius of 1.5 nm, and the content thereof was a volume fraction of 5 vol%.

[0062] Secondly, set the total number of simulations N = 30 and initialize the number of simulations n = 1. Then determine whether the simulation is completed. If n < N, it means that the simulation is not completed. Generate the geometric structure and generate the material geometry for finite element calculation based on the structural parameters. Figure 3 As shown in the figure, when n=1, Monte Carlo simulation randomly generates a geometric structure and starts the nth simulation; if n≥N, it means that the simulation is completed, and the N breakdown strength values obtained by the N simulations are fitted with Weibull distribution to obtain the Weibull distribution parameters α and β, as shown in the figure. Figure 4 As shown in the figure, the results of Weibull distribution fitting are obtained for 30 breakdown strength values obtained from 30 simulations, and the values of α and β are 325MV / m and 20 respectively. b =α. According to the fitting results, the breakdown strength E of the material is obtained b =325MV / m. This breakdown strength value is consistent with the experimental results, indicating that the breakdown simulation method proposed in this application has good quantitative prediction capability for breakdown strength.

[0063] Among them, such as Figure 3 As shown in Figure 1, a geometric structure randomly generated by Monte Carlo simulation when n=1 is used. When the nth simulation starts, the initial external electric field strength E0 is set to 195MV / m. Next, a mesoscopic simulation is performed, that is, the finite element method is used to calculate the internal electric field strength E of the material based on the relative dielectric constant and geometric structure of the material. i .like Figure 5 As shown in the figure, when n=1 and the external electric field strength is 330MV / m, the calculated internal electric field strength E i Distribution.

[0064] Next, a microscale simulation is performed, and the molecular interaction energy U at the interface between the filler and the matrix is calculated based on the material parameters and structural parameters using the molecular dynamics method. int =-38.7kcal / mol.

[0065] Furthermore, the breakdown probability of the grid is calculated, and α Ei , β Ei 、ɑ Qi , β Qi are the ɑ of the material at the i-th grid E , β E , α Q , β QParameters, k is the Boltzmann constant (value is 1.380649e-23J / K), T is the temperature (set to 298K), v f is the volume fraction of the filler (set to 0.05), a and b are adjustment coefficients (set to 0.005 and 20 respectively), and the breakdown probability Q can be calculated according to the formula for calculating the breakdown probability of the non-interface mesh and the interface mesh in the above steps. i .

[0066] like Figure 6 As shown, when n=1 and the applied electric field strength is 330MV / m, the calculated grid breakdown probability Q i Distribution.

[0067] Secondly, Monte Carlo simulation is performed to obtain the grid state. In the MSBS model, there are two possible states for each grid, namely normal state and active state. Let S i is the state parameter of the ith grid. When the ith grid is in normal state, S i = 0, when the i-th grid is active, S i = 1. According to the grid breakdown probability Q i , the state parameter S of the randomly generated grid i The Monte Carlo method is: For grid i, generate a random number ξ i ,ξ i Uniformly distributed in [0,1]; according to the use of ξ i The value of S i If the value of ξ i >Q i , then S i = 0, if ξ i ≤Q i , then S i = 1. Will be in active state (S i =1) are connected.

[0068] Next, determine whether it has broken down. That is, when the connected active state grids can form at least one path that is connected from top to bottom (through the entire material), it is determined that it has broken down. The connected path is the breakdown path, and the external electric field strength E0 at this time is recorded as the breakdown strength value of the nth simulation. For example, when n=1, after it is determined that it has broken down, 330MV / m should be recorded as the breakdown strength of this simulation. Figure 7 The figure shows the breakdown path when n = 1. When the applied electric field strength is 330 MV / m, the active state grid forms a breakdown path from top to bottom, and is judged to have broken down.

[0069] In addition, if the connected active state grids cannot form at least one path that is connected from top to bottom (through the entire material), it is determined that there is no breakdown, the external electric field strength E0 is increased, and the simulation is restarted.

[0070] When it is determined that the breakdown has occurred, the external electric field strength at this time is recorded as the breakdown strength value of the nth simulation. Let n = n + 1. If n ≥ N, it means that the simulation has been completed. Then, the N breakdown strength values obtained by the N simulations are fitted with a Weibull distribution to obtain the Weibull distribution parameters α and β, as shown in the following example: Figure 4 As shown in the figure, the results of Weibull distribution fitting are obtained for 30 breakdown strength values obtained from 30 simulations, and the values of α and β are 325MV / m and 20 respectively. b =α. According to the fitting results, the breakdown strength E of the material is obtained b =325MV / m. This breakdown strength value is consistent with the experimental results, indicating that the breakdown simulation method proposed in this application has good quantitative prediction capability for breakdown strength.

[0071] In the embodiments of the present application, multi-scale modeling is used to consider the influence of structural design on breakdown at the mesoscale and the influence of interface on breakdown at the microscale. The present application establishes a connection between the interface molecular interaction energy at the microscale and the interface breakdown probability. Compared with the existing breakdown simulation method, the present application more fully considers the differences in microscopic interface properties when different material selections (different matrix and filler materials) and different filler parameters (different filler shapes, sizes, etc.), making the breakdown simulation model more complete.

[0072] In addition, this application uses the statistical characteristics of the breakdown strength value obeying the Weber distribution to establish a mathematical relationship between the electric field strength and the breakdown probability of the grid at the mesoscopic scale, and combines the relationship between the interface molecular interaction energy and the interface grid breakdown probability to determine the breakdown probability of each material grid. On this basis, the breakdown strength is determined by Monte Carlo simulation, and the breakdown path is obtained at the same time. By repeating the Monte Carlo simulation multiple times, multiple breakdown strength values can be obtained. Referring to the processing method of experimental data, this application performs Weber distribution fitting on multiple breakdown strength values to obtain the final simulation result of the breakdown strength.

[0073] For example, Figure 8 As shown, the working principle of the embodiment of the present application is described in detail below with a specific embodiment.

[0074] Step S801: setting material parameters, that is, setting material parameters of nanomaterials.

[0075] Step S802: setting structural parameters, that is, setting structural parameters of the nanomaterial.

[0076] Step S803: setting the total number of simulations N, that is, setting the total number of simulations of the multi-scale breakdown simulation model.

[0077] Step S804: Determine whether the number of simulations n ≥ N. If n ≥ N, execute step S816; otherwise, execute step S805.

[0078] Step S805: Generate a geometric structure, that is, generate a material geometric structure for finite element calculation according to the structural parameters.

[0079] Step S806: setting the external electric field E0, that is, setting the initial external electric field strength E0.

[0080] Step S807: FEM calculation of internal electric field distribution E i , that is, using the finite element method to calculate the electric field strength inside the material.

[0081] Step S808: MD calculation of the interface molecular interaction energy U int , that is, the molecular interaction energy at the interface between the filler and the matrix is calculated using the molecular dynamics method.

[0082] Step S809: Calculate the grid breakdown probability Q i , that is, the grid breakdown probability is calculated using the internal electric field strength and molecular interaction energy.

[0083] Step S810: Monte Carlo simulation to obtain the grid state S i , that is, randomly generate the state parameters of the grid according to the grid breakdown probability.

[0084] Step S811: Connect the broken network and put it into active state (S i =1) are connected.

[0085] Step S812: Determine whether the battery has broken down. If the battery has broken down, execute step S814; otherwise, execute step S813.

[0086] Step S813: Increase the external electric field E0 and execute step S807.

[0087] Step S814: Record E0, that is, record the external electric field strength E0 at this time as the breakdown strength value of the nth simulation.

[0088] Step S815: n=n+1, that is, let n=n+1, and execute step S804.

[0089] Step S816: Perform Weibull distribution fitting on the N E0 values to obtain α and β, that is, perform Weibull distribution fitting on the N breakdown strength values obtained by N simulations to obtain Weibull distribution parameters α and β.

[0090] Step S817: Breakdown field strength E b =α.

[0091] Therefore, compared with the existing breakdown simulation method, this application more fully considers the influence of microscopic interfaces on the breakdown of nanocomposites, and the calculated breakdown strength value is more accurate, which can be used to study the dielectric breakdown mechanism of nanocomposites and better guide the design of high-performance nanocomposite dielectric materials.

[0092] According to a multi-scale simulation method for dielectric breakdown of composite materials proposed in an embodiment of the present application, the electric field strength of each grid in the nanocomposite material and the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material can be calculated respectively. Then, the target breakdown probability of each grid is calculated using the electric field strength and the molecular interaction energy, and the target breakdown strength value that meets certain conditions is further determined. The target breakdown strength value is subjected to Weibull distribution fitting processing to determine the simulation result of the breakdown strength of the nanocomposite material, effectively reducing the error of the breakdown simulation and improving the accuracy of the breakdown simulation. Thus, the problem that the breakdown simulation methods in the related art are mostly single-scale models and the consideration of the interface effect is not sufficient, resulting in large errors in the quantitative prediction of the breakdown strength and reducing the accuracy of the breakdown simulation, is solved.

[0093] Next, a composite material dielectric breakdown multi-scale simulation device proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0094] Figure 9 Schematic diagram of a multi-scale simulation device for dielectric breakdown of composite materials according to an embodiment of the present application.

[0095] like Figure 9 As shown, the composite material dielectric breakdown multi-scale simulation device 10 includes: a first calculation module 100, a second calculation module 200 and a simulation module 300.

[0096] Specifically, the first calculation module 100 is used to calculate the electric field intensity of each grid in the nano-composite material based on a preset geometric structure of the nano-composite material.

[0097] The second calculation module 200 is used to calculate the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material based on the material parameters and structural parameters of the nanocomposite material.

[0098] The simulation module 300 is used to calculate the target breakdown probability of each grid using the electric field strength and the molecular interaction energy, and generate the target state parameters of each grid based on the target breakdown probability of each grid, and use the target state parameters to determine the target breakdown strength value that meets the preset conditions, and perform Weibull distribution fitting processing on the target breakdown strength value to determine the simulation result of the breakdown strength of the nanocomposite material.

[0099] Optionally, in one embodiment of the present application, the device 10 of the embodiment of the present application further includes: a setting module, a judgment module, a first processing module, a second processing module and a determination module.

[0100] Among them, the setting module is used to set the material parameters and structural parameters of the nanocomposite material based on a pre-built multi-scale breakdown simulation model before calculating the electric field strength of each grid in the nanocomposite material, and set the target simulation times of the multi-scale breakdown simulation model.

[0101] The judgment module is used to judge whether the actual simulation number of the multi-scale breakdown simulation model is less than the target simulation number before calculating the electric field intensity of each grid in the nanocomposite material.

[0102] The first processing module is configured to determine the geometric structure of the nanocomposite material if the actual number of simulations is less than the target number of simulations before calculating the electric field intensity of each grid in the nanocomposite material.

[0103] The second processing module is used to determine a corresponding number of breakdown strength values according to the actual number of simulations if the actual number of simulations is greater than or equal to the target number of simulations before calculating the electric field strength of each grid in the nanocomposite material.

[0104] The determination module is used to perform Weibull distribution fitting processing on the corresponding number of breakdown strength values before calculating the electric field strength of each grid in the nanocomposite material to determine the simulation result of the breakdown strength of the nanocomposite material.

[0105] Optionally, in one embodiment of the present application, the simulation module 300 includes: a determination unit, a first judgment unit, a second judgment unit, and a processing unit.

[0106] The determining unit is used to determine a random number of at least one grid, and use the random number to determine a state parameter of at least one grid.

[0107] The first judgment unit is configured to judge whether the state parameter is in a target active state, wherein if the state parameter is in the target active state, all adjacent grids in the target active state are connected to determine an active state grid.

[0108] The second judgment unit is used to judge whether the nanocomposite material has undergone breakdown behavior based on the active state grid.

[0109] The processing unit is used to obtain the current external electric field strength value of the nano-composite material if the nano-composite material undergoes breakdown behavior, and use the current external electric field strength value as the target breakdown strength value.

[0110] Optionally, in one embodiment of the present application, the target breakdown probability of each grid is calculated as follows:

[0111]

[0112] Among them, E i The electric field strength of each grid, Q i The breakdown probability of each grid, U int is the molecular interaction energy at the interface, T is the temperature, v f is the volume fraction of the filler, k is the Boltzmann constant, a and b are adjustment coefficients, α Ei and β Ei are the Weibull distribution parameters of the material breakdown strength at the i-th grid, α Qi and β Qi are the Weibull distribution parameters of the mesh breakdown probability of the material at the i-th mesh.

[0113] It should be noted that the above explanation of the embodiment of a multi-scale simulation method for dielectric breakdown of a composite material is also applicable to the multi-scale simulation device for dielectric breakdown of a composite material in this embodiment, and will not be repeated here.

[0114] According to a multi-scale simulation device for dielectric breakdown of composite materials proposed in an embodiment of the present application, the electric field strength of each grid in the nanocomposite material and the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material can be calculated respectively. Then, the target breakdown probability of each grid is calculated using the electric field strength and the molecular interaction energy, and the target breakdown strength value that meets certain conditions is further determined. The target breakdown strength value is subjected to a Weibull distribution fitting process to determine the simulation result of the breakdown strength of the nanocomposite material, effectively reducing the error of the breakdown simulation and improving the accuracy of the breakdown simulation. Thus, the problem that the breakdown simulation methods in the related art are mostly single-scale models and the consideration of the interface effect is not sufficient, resulting in large errors in the quantitative prediction of the breakdown strength and reducing the accuracy of the breakdown simulation, is solved.

[0115] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0116] A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .

[0117] When the processor 1002 executes the program, the multi-scale simulation method for dielectric breakdown of composite materials provided in the above embodiment is implemented.

[0118] Furthermore, the electronic device further includes:

[0119] The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .

[0120] The memory 1001 is used to store computer programs that can be run on the processor 1002 .

[0121] The memory 1001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0122] If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0123] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.

[0124] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0125] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the multi-scale simulation method for dielectric breakdown of composite materials as described above is implemented.

[0126] This embodiment also provides a computer program product, including a computer program. When the computer program is executed, it is used to implement the above-mentioned multi-scale simulation method for dielectric breakdown of composite materials.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0131] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0134] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-scale simulation method for dielectric breakdown of composite materials, characterized in that: The following steps are involved: Calculating the electric field intensity of each grid in the nanocomposite material based on a preset geometric structure of the nanocomposite material; calculating the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material based on the material parameters and structural parameters of the nanocomposite material; The target breakdown probability of each grid is calculated using the electric field strength and the molecular interaction energy, and based on the target breakdown probability of each grid, a target state parameter of each grid is generated, and the target state parameter is used to determine a target breakdown strength value that meets preset conditions, and the target breakdown strength value is subjected to Weibull distribution fitting processing to determine a simulation result of the breakdown strength of the nanocomposite material.

2. The method according to claim 1, characterized in that Before calculating the electric field intensity of each grid in the nanocomposite material, it also includes: Based on a pre-built multi-scale breakdown simulation model, setting the material parameters and the structural parameters of the nanocomposite material, and setting a target number of simulations for the multi-scale breakdown simulation model; Determining whether an actual number of simulations of the multi-scale breakdown simulation model is less than a target number of simulations; wherein, if the actual number of simulations is less than the target number of simulations, determining the geometric structure of the nanocomposite material; If the actual number of simulations is greater than or equal to the target number of simulations, determining a corresponding number of breakdown strength values according to the actual number of simulations; The corresponding number of breakdown strength values are subjected to the Weibull distribution fitting process to determine a simulation result of the breakdown strength of the nanocomposite material.

3. The method according to claim 2, characterized in that The step of determining a target breakdown strength value that satisfies a preset condition by using the target state parameter includes: determining a random number of at least one grid, and determining a state parameter of at least one grid using the random number; Determining whether the state parameter is in a target active state, wherein if the state parameter is in the target active state, connecting all adjacent grids in the target active state to determine an active state grid; Based on the active state grid, determining whether the nanocomposite material undergoes breakdown behavior; If the nanocomposite material undergoes the breakdown behavior, the current externally applied electric field strength value of the nanocomposite material is obtained, and the current externally applied electric field strength value is used as the target breakdown strength value.

4. The method according to claim 1, wherein The calculation formula for the target breakdown probability of each grid is: Among them, E i The electric field strength of each grid, Q i The breakdown probability of each grid, U int is the molecular interaction energy at the interface, T is the temperature, v f is the volume fraction of the filler, k is the Boltzmann constant, a and b are adjustment coefficients, α Ei and β Ei are the Weibull distribution parameters of the material breakdown strength at the i-th grid, α Qi and β Qi are the Weibull distribution parameters of the mesh breakdown probability of the material at the i-th mesh.

5. A multi-scale simulation device for dielectric breakdown of composite materials, characterized in that: include: A first calculation module is used to calculate the electric field intensity of each grid in the nanocomposite material based on a preset geometric structure of the nanocomposite material; a second calculation module for calculating the molecular interaction energy at the interface between the nanofiller and the matrix in the nanocomposite material based on the material parameters and structural parameters of the nanocomposite material; A simulation module is used to calculate the target breakdown probability of each grid using the electric field strength and the molecular interaction energy, and generate a target state parameter for each grid based on the target breakdown probability of each grid, and use the target state parameter to determine a target breakdown strength value that meets preset conditions, and perform Weibull distribution fitting processing on the target breakdown strength value to determine a simulation result of the breakdown strength of the nanocomposite material.

6. The device according to claim 5, characterized in that Also includes: a setting module for setting the material parameters and the structural parameters of the nanocomposite material based on a pre-built multi-scale breakdown simulation model before calculating the electric field intensity of each grid in the nanocomposite material, and setting a target number of simulations of the multi-scale breakdown simulation model; a judgment module, configured to judge whether an actual number of simulations of the multi-scale breakdown simulation model is less than a target number of simulations before calculating the electric field strength of each grid in the nanocomposite material; a first processing module for determining a geometric structure of the nanocomposite material before calculating the electric field intensity of each grid in the nanocomposite material if the actual number of simulations is less than the target number of simulations; a second processing module, configured to determine a corresponding number of breakdown strength values according to the actual number of simulations if the actual number of simulations is greater than or equal to the target number of simulations before calculating the electric field strength of each grid in the nanocomposite material; The determination module is used to perform the Weibull distribution fitting process on the corresponding number of breakdown strength values before calculating the electric field strength of each grid in the nanocomposite material to determine a simulation result of the breakdown strength of the nanocomposite material.

7. The device according to claim 6, characterized in that The simulation module includes: a determining unit, configured to determine a random number of at least one grid, and determine a state parameter of at least one grid using the random number; a first judging unit, configured to judge whether the state parameter is in a target active state, wherein if the state parameter is in the target active state, connecting all adjacent grids in the target active state to determine an active state grid; a second judgment unit, configured to judge whether the nanocomposite material undergoes breakdown based on the active state grid; A processing unit is configured to obtain a current externally applied electric field strength value of the nano-composite material if the nano-composite material undergoes the breakdown behavior, and use the current externally applied electric field strength value as the target breakdown strength value.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a multi-scale simulation method for dielectric breakdown of a composite material according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a multi-scale simulation method for dielectric breakdown of composite materials as claimed in any one of claims 1 to 4.

10. A computer program product comprising a computer program, characterized in that The computer program is executed by a processor to implement a multi-scale simulation method for dielectric breakdown of composite materials as claimed in any one of claims 1 to 4.

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