Modeling method of real particle shape breakable particle model based on controllable grid division quantity and regular degree

Abstract

The application provides a modeling method of a real particle shape breakable particle model based on controllable grid division quantity and regularity degree, belongs to the technical field of the breakable particle model, and comprises the following steps: firstly, photographing, extracting and scanning a real particle shape to obtain particle contour information; then, reading the particle contour information through an editor, generating random points and circumscribed rectangles in the contour by using a control grid regularity degree method, exporting circumscribed rectangle information of the random points, importing random point coordinate information into neper software to perform Voronoi division on the circumscribed rectangles, exporting the divided unit set information, again importing the unit set information into a python editor to cut and retain the rectangles by using the particle contour, and finally assembling the processed Voronoi units into a breakable particle model. Based on real particle data obtained by photographing or scanning, the modeling method of the cohesive cell method is used to realize the modeling of the real particle shape, so that the model can more accurately simulate the shape, surface roughness, volume and other parameters of the real particle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of breakable particle models, and particularly relates to a modeling method of a real particle shape breakable particle model based on controllable grid division quantity and regularity degree. BACKGROUND

[0002] Particle materials are widely used in various civil engineering, such as water conservancy projects, mining, tunnel construction and building foundations, etc. Due to its excellent compression resistance and good water permeability, it has become an important part of engineering construction. However, during the construction and use process, the particle materials often encounter overloading or impact, leading to particle breakage. Particle breakage not only changes the macro mechanical properties of the material, but also causes changes in the internal mechanical properties of the material, thereby affecting the stability and safety of the engineering structure. Traditional physical tests are difficult to observe and accurately analyze the breakage process at the particle scale due to limitations in equipment precision and experimental conditions. Therefore, how to accurately simulate the mechanism of particle breakage at the microscale has become an important topic in the study of particle materials.

[0003] With the continuous development of numerical simulation technology, the discrete element method (DEM) has gradually become one of the main tools for studying particle breakage. The discrete element method regards particles as independent particles and simulates the interaction between particles, which can effectively reveal the mechanical behavior of the particle breakage process. In order to better simulate the particle breakage phenomenon and improve the reliability of the simulation results, it is urgent to develop a discrete element model based on real particle shape. This model can consider the shape, surface roughness of the particle and the micro-mechanical characteristics during the breakage process, thereby providing a more realistic and effective tool for numerical research on particle breakage. This not only has important significance for the performance analysis of particle materials, but also provides more scientific theoretical support for related engineering applications.

[0004] In the study of discrete element particle breakage, the model construction of particles is the primary task. There are two traditional modeling methods: one is the replacement particle method, which is to fill the inside of the real contour with many small volume spherical particles by sticking them together to construct a breakable particle model. The replacement particle method is convenient and simple, the particle model has small calculation load and fast running speed, but its breakage criteria and breakage mode are not clear, and the most critical defect of this model is that the volume enclosed by the contour and the total volume of the sub-particles are not equal, and the shape characteristics of the particles cannot be reflected after breakage.

[0005] Compared with the modeling method of particle replacement method, the polyhedral cell can meet the volume conservation in modeling, and can decompose the external load into tensile force and shear force acting on the polyhedron. The crushing path of tetrahedral cell is always smooth, while the polyhedral cell provides more crushing modes through micro-convex and rough failure path, which more accurately reflects the actual process of particle crushing from the mechanical point of view, and more truly reproduces the crushing path. Although existing open source software (such as Neper, Voro++) and commercial tools (such as PFC3D, 3DEC, Matlab) provide polyhedral partitioning functions, they can usually only handle simple convex geometry. The shape of the real particle is complex and the surface has the characteristics of concave and convex, and the existing tools either cannot directly segment it into a polyhedral cell suitable for discrete element analysis, or the segmented polyhedral cell cannot control the regularity of the grid.

[0006] The existing bonded cell method modeling method is to use the discrete element software PFC to import the outline of the particle model, convert it into a wall body with constraint effect, then generate a plurality of mutually non-contacting spherical small particle sub-particles in the wall body, and then gradually enlarge the radius of the spherical sub-particle by the radius expansion method to fill, after filling, taking the particle sub-particle center as the seed, the circumscribed cube of the particle outline is divided into a Voronoi polyhedron assembly, and then the PFC command is used to cut the cube through the particle outline. Another method is to import the outline of the particle into the finite element software, and generate the particle model through the Voronoi grid division of the finite element software. Although these methods can realize the modeling of real particles, they cannot control the regularity of the Voronoi grid division and the number of cells in the Voronoi grid division.

[0007] The prior art has the following technical defects:

[0008] (1) The model generated by the particle replacement method cannot simulate the shape, surface roughness, volume and other parameters of the real particle. (2) The discrete element software and the finite element software do not have a unified method to control the number of grids and the regularity of the grid division. (3) The existing modeling method cannot realize the unification of two-dimensional modeling and three-dimensional modeling. Therefore, in view of this problem, the present application provides a division method for controlling the regularity of the grid, which aims to accurately describe the shape of the real particle and provide effective support for the numerical simulation of the particle crushing process. SUMMARY

[0009] The purpose of the present application is to provide a modeling method of a real particle shape crushable particle model based on controllable grid division number and regularity, which solves the technical problems mentioned in the background art.

[0010] The method comprises the following steps: taking a photo to extract and scan the real particle shape to obtain particle contour information, reading the particle contour information through a python editor, generating random points and circumscribed rectangles (cubes in three dimensions) in the contour by using a control grid regularity method, exporting the circumscribed rectangle (cube in three dimensions) information of the random points, then importing the random point coordinate information into neper software to perform Voronoi partitioning on the circumscribed rectangle (cube in three dimensions), exporting the partitioned unit set information, and then importing the unit set information into the python editor again to cut and retain the rectangle (cube in three dimensions) by using the particle contour through the trimesh, numpy and shapely libraries, and finally assembling the processed Voronoi unit into a breakable particle model.

[0011] The modeling method of the bonding cell element is used to realize modeling based on the real particle shape, so that the model can more accurately simulate the shape, surface roughness, volume and other parameters of the real particle. A method for controlling the regularity of the grid is proposed to ensure the uniformity of the number and volume of the sub-particles during particle modeling. The modeling method is suitable for two-dimensional and three-dimensional modeling and realizes the unification of two-dimensional and three-dimensional modeling.

[0012] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0013] The modeling method of the real particle shape breakable particle model based on the controllable grid division number and regularity comprises the following steps:

[0014] Step 1: select a real particle, take a photo and obtain a two-dimensional real particle contour boundary through deep learning image recognition technology, and scan and obtain a three-dimensional real particle contour that needs to be modeled through 3D scanning software;

[0015] Step 2: read the two-dimensional and three-dimensional contour information of the particle as the model boundary, generate random points in the particle model by using the method for controlling the regularity of the grid, then generate contour information, and finally extract the random point coordinate information and the contour information;

[0016] Step 3: import the random point coordinate information and the contour information into software to generate a unit set, and extract all information of the unit set;

[0017] Step 4: Extract the vertex coordinates of each cell in the cell set, and number each cell. Read the information of several polyhedral cells and the particle contour information of one numbered cell. If the cell intersects with the contour, use the convex hull segmentation algorithm to complete the cutting of several polyhedrons by the data point replacement method. According to the particle contour, determine the information of the cutting part inside and outside the contour, and extract the vertex information inside the contour. If the cell is inside the contour, extract all the vertex information of the cell;

[0018] Step 5: Repeat step 4 to cut all the polygons or three-dimensional polyhedral cells according to the number, and save the vertex information in the same file in the same way;

[0019] Step 6: Import the file data information of the previous step into the discrete element software to generate all the polygons or three-dimensional polyhedral particle models that can be broken, and use the parallel bonding method to give the bonding force between the cells. Then the particle breaking can be simulated.

[0020] Further, the specific process of step 1 is: the real particles are derived from blasting gravel, crushed stone in actual engineering, or pebbles in riverbed. The two-dimensional real particle contour boundary is obtained by camera shooting and deep learning image recognition technology. The data file is saved as a JSON file. The three-dimensional contour information of the real particle is obtained by a scanner. The particle model consistent with the shape and volume of the real particle is generated by point cloud modeling and triangular mesh splicing, and is saved as an STL format file.

[0021] Further, in step 2, the information of the two-dimensional and three-dimensional contours of the particle is read by a python compiler. Random points are generated inside the particle model. After generating the random points, an enclosing rectangle or three-dimensional cube is generated. The coordinate information of the random points and the length, width and height information of the enclosing rectangle or three-dimensional cube are extracted.

[0022] Further, the specific process of the method for controlling the grid regularity in step 2 is:

[0023] a. First, calculate the area S or volume V of the particle contour;

[0024] a. Based on the Voronoi algorithm, set the number of random points or seed points Np;

[0025] c. Calculate the average equivalent radius r of the divided grid cells, , in three-dimensional case: ;

[0026] d. Define the grid regularity parameter a, where 0.0≤a≤0.8, to ensure that the minimum distance Lmin between random points or seed points is greater than a*2*r, and the random points are generated inside the contour.

[0027] e. Control the size of the parameter alpha to achieve the regularity of the control grid.

[0028] Further, in step 3, the random point information is imported into the neper software by the command of the neper software to divide the circumscribed rectangle or three-dimensional cube of the contour into a Voronoi grid or a three-dimensional polyhedral element set, extract the information of the element set, and the file is a tess file containing the information of the points, faces and edges of each element.

[0029] Further, in step 4, the tess file of the element set is read by using the Python compiler to extract the vertex coordinates of each element in the polygon or three-dimensional polyhedral element set, and each element is numbered, and the vertex coordinates of each element are saved under the number of the element. When it is a two-dimensional case, the information of the polygon element numbered 1 and the particle contour information are read, and if the element intersects with the contour, all the vertex information of the element is extracted, and the data point replacement method is directly used to replace the vertex coordinates outside the contour with the intersection points formed by the contour points and the contour point links inside the polygon, and the vertices inside the contour points are retained to complete the reconstruction of the polygon, and if the element is inside the contour, all the vertex information of the element is directly saved.

[0030] Further, in step 5, all the polygons or three-dimensional polyhedral elements are cycled according to the method of step 4, and all the information is extracted and converted into a command language format readable by the discrete element software and saved as a txt format.

[0031] Further, in step 6, the txt document of step 5 is imported into the discrete element software and run, and the bonding force is given between the polygons or three-dimensional polyhedrons to break the particles.

[0032] Further, in step 3, the number and size difference of the sub-particles in the particle breakage can be adjusted by controlling the Voronoi grid division, and the simulation error is reduced.

[0033] Further, in step 2, the control grid regularity method can ensure the uniformity of the number and size of the sub-particles in the particle modeling.

[0034] The present application has the following beneficial effects due to the adoption of the above technical scheme:

[0035] (1) The present application is based on the real particle data obtained by shooting or scanning, and uses the cohesive cell method modeling method to realize modeling based on the real particle shape, and ensures that the model can more accurately simulate the shape, surface roughness, volume and other parameters of the real particle.

[0036] (2) On the basis of the bonding cell method modeling method, a grid rule degree control method is proposed to ensure the uniformity of the number and volume size of the sub-particles in the particle modeling, which provides a new idea and method for the subsequent study of the influence of the differentiation degree of the number and volume of the sub-particles in the particle crushing on the particle crushing. Finally, the modeling method is not only suitable for two-dimensional but also for three-dimensional, and realizes the unification of two-dimensional and three-dimensional modeling, which provides an effective technical means for the discrete element research. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 It is a modeling method flow step schematic diagram of the real particle shape breakable particle model of the embodiment of the application which can control the grid division rule degree;

[0038] Figure 2 It is a real particle picture obtained by a camera of the embodiment of the application;

[0039] Figure 3 It is a two-dimensional contour image picture extracted by the deep learning technology of the embodiment of the application;

[0040] Figure 4 It is a contour data information picture calculated by binarizing the two-dimensional contour image introduced into Matlab of the embodiment of the application;

[0041] Figure 5 It is a random point picture generated by the embodiment of the application;

[0042] Figure 6 It is a Voronoi unit collection picture generated by introducing the random point and rectangular information into the neper software of the embodiment of the application;

[0043] Figure 7 It is a picture generated by introducing the polygon data of the Voronoi unit collection into the python editor of the embodiment of the application;

[0044] Figure 8 It is a particle contour cyclic cutting Voronoi unit collection picture of the embodiment of the application;

[0045] Figure 9 It is a discrete element real particle breakable model picture generated by introducing the cut polygon data into PFC2D of the embodiment of the application;

[0046] Figure 10 It is a random point comparison picture generated by the embodiment of the application;

[0047] Figure 11 It is a discrete element real particle breakable model comparison picture generated by the embodiment of the application;

[0048] Figure 12 is a three-dimensional contour map of a real particle scanned by a scanner of an embodiment of the present application;

[0049] Figure 13 is a random point map generated according to a grid rule parameter a = 0.0 after the introduction of particle contour information and the generation of an inscribed cube by an embodiment of the present application;

[0050] Figure 14 is a Voronoi cell aggregate map generated by the introduction of neper software by an embodiment of the present application;

[0051] Figure 15 is a discrete element real particle breakable model map generated by the introduction of cut polygon data into PFC3D by an embodiment of the present application;

[0052] Figure 16 is a random point comparison map generated according to a grid number Np = 100 and a grid rule parameter a = 0.0 and a = 0.8 by an embodiment of the present application;

[0053] Figure 17 is a discrete element real particle breakable model comparison map generated according to a grid number Np = 100 and a grid rule parameter a = 0.0 and a = 0.8 by an embodiment of the present application;

[0054] Figure 18 is a single particle breakage condition comparison map between a test and a simulation by an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application is further described in detail below with reference to the drawings and preferred embodiments. However, it should be noted that many details in the description are only provided to make the reader have a thorough understanding of one or more aspects of the present application, and the aspects of the present application can be realized even without these specific details.

[0056] The modeling method of the real particle shape breakable particle model based on controllable grid division number and rule degree, the method comprising the following steps:

[0057] Step 1: Select real particles, take pictures with a camera, and obtain two-dimensional real particle contour boundaries through deep learning image recognition technology. Scan with a scanner and obtain the real three-dimensional particle contour that needs to be modeled through 3D scanning software. Real particles come from blasting materials in actual engineering, gravel, or riverbed pebbles. Through camera photography, deep learning image recognition technology is used to obtain two-dimensional real particle contour boundaries, and data files are generally saved as "JSON" files. Through a scanner (such as RevoPoint POP2 scanner), three-dimensional contour information of real particles is obtained, and a particle model consistent with the shape and volume of the real particle is generated through point cloud modeling and triangular mesh splicing, usually saved as an "STL" format file. For example, Figure 2 is a real particle image obtained by camera photography, such as Figure 3 is a two-dimensional contour image extracted by deep learning technology, such as Figure 4 Import the two-dimensional contour image into Matlab for binary calculation of contour data information graph. For example, Figure 12 is a three-dimensional contour graph of real particles obtained by scanning.

[0058] Step 2: Read the two-dimensional and three-dimensional contour information of the particle as a model boundary through the python compiler, generate random points inside the particle model through the method proposed in the invention to control the regularity of the grid, then generate the circumscribed rectangle (three-dimensional is circumscribed cube) of the contour, and finally extract the random point coordinate information and the circumscribed rectangle information.

[0059] Read the two-dimensional and three-dimensional contour information of the particle through the python compiler, generate random points inside the particle model, and the method proposed in the invention to control the regularity of the grid is as follows, as shown in Figure 1 .

[0060] a. First, calculate the area S (volume V in three-dimensional case) enclosed by the particle contour.

[0061] b. Set the number of random points (seed points) Np based on the Voronoi algorithm.

[0062] c. Calculate the average equivalent radius r of the divided grid cells, , and in three-dimensional case: ;

[0063] d. Define the grid regularity parameter a (0.0≤a≤0.8) to ensure that the minimum distance Lmin between random points (seed points) is greater than a*2*r, and the random points are generated inside the contour.

[0064] e. Control the size of parameter a to achieve control of the regularity of the grid.

[0065] After generating the random point, continue to generate the circumscribed rectangle of the contour (cuboid in three dimensions). Extract the coordinate information of the random point and the length, width, and height information of the circumscribed rectangle (cuboid in three dimensions). As shown in Figure 13 Fig. 1 shows the import of particle contour information, generation of a circumscribed cuboid, and generation of a random point map according to the grid rule parameter a = 0.0. Figure 16 Fig. 2 shows a comparison of random point generation with grid number Np = 100, grid rule parameter a = 0.0, and a = 0.8.

[0066] Fig. 3 shows the import of particle contour information, generation of a circumscribed rectangle, input of grid number Np = 100, and generation of a random point map with grid rule parameter a = 0.0. As can be seen from the figure, according to the Voronoi algorithm, Np random points can be generated to generate Np grids, so that the number of grids can be controlled. Inputting the grid rule parameter a = 0.0 can control the degree of regularity of the grid. Figure 5

[0067] Step 3: Import the random point coordinate information and the circumscribed rectangle (cuboid in three dimensions) information into the neper software to generate a Voronoi polygonal mesh (polyhedral mesh in three dimensions) element collection, and extract all information such as vertices, faces, and edges of the element collection. Through the command of the neper software, import the random point information into the neper software to divide the circumscribed rectangle (cuboid in three dimensions) of the contour into a Voronoi polygonal mesh (polyhedral mesh in three dimensions) element collection, extract the information of the element collection, and the file is a tess file containing information such as points, faces, and edges of each element. As shown in Figure 6 Fig. 4 shows the Voronoi element collection generated by importing the random point and rectangle information into the neper software, which can quickly realize Voronoi division. As shown in Figure 14 Fig. 5 shows the Voronoi element collection generated by importing the random point and cuboid information into the neper software.

[0068] Step 4: Use the Python compiler to extract the vertex coordinates of each element in the polygonal mesh (polyhedral mesh in three dimensions) element collection, and number each element. Read the information of the polyhedral element numbered 1 and the particle contour information. If the element intersects with the contour: and use the convex hull segmentation algorithm to complete the cutting of the polyhedron by the data point replacement method. According to the particle contour, determine which part is inside the contour and which part is outside the contour after cutting; extract the vertex information of the polygonal mesh (polyhedral mesh in three dimensions) inside the contour. If the element is inside the contour: extract all vertex information of the element.

[0069] ​The tess file of the unit assembly is read by the Python compiler, the vertex coordinates of each unit in the polygon (polyhedron in three dimensions) unit set are extracted, each unit is numbered, and the vertex coordinates of each unit are saved under the number. In the two-dimensional case: read the polygon unit information and particle outline information with the number 1, if the unit intersects with the outline: extract all vertex information of the unit, directly use the data point replacement method, use trimesh, numpy, shapely and other libraries to replace the vertex coordinates outside the outline with the intersection points formed by the outline points and the links of the outline points inside the polygon, and retain the vertices inside the outline points to complete the reconstruction of the polygon. If the unit is inside the outline: extract all vertex information of the unit directly. Figure 7 The polygon data of the Voronoi unit assembly is imported into the python editor to generate a graph.

[0070] Step 5: Loop the last step, cut all polygons (polyhedron in three dimensions) units according to the number, and save the vertex information in the same way in the same file. According to the method of step 4, loop all polygons (polyhedron in three dimensions) units, and extract all information to convert it into a command format readable by discrete element software, and save it as a txt format. As shown in Figure 8 The figure shows the graph of the particle outline cutting the Voronoi unit assembly, which realizes the Voronoi division of the particle outline.

[0071] Step 6: Import the file data information of the last step into the discrete element software to generate all polygons (polyhedron in three dimensions) that can break the real particle model. Use parallel bonding method to give the bonding force between units, then you can simulate particle breakage. Import the txt document of step 5 into the discrete element software to run, and give the bonding force between polygons (polyhedron in three dimensions) to break the particles. As shown in Figure 9 The figure shows the discrete element real particle breakable model graph generated by importing the cut polygon data into PFC2D. As shown in Figure 10 The figure shows the comparison of the discrete element real particle breakable model generated by the grid number Np=100 and the grid regularity parameter α=0.0 and α=0.8, where the number of sub-particles in the two models is 100; the volume difference of the sub-particles in the model with α=0.0 is large; the volume difference of the sub-particles in the model with α=0.8 is small. From the comparison chart we can see that the grid number Np and the grid regularity parameter α have a specific effect on the discrete element modeling, which provides a new idea and method for the subsequent study of the influence of the number of sub-particles and the difference of the volume of sub-particles on particle breakage.

[0072] As shown in Figure 15 The figure shows the discrete element real particle breakable model graph generated by importing the cut polygon data into PFC3D.Figure 16 The figure shows the comparison of the discrete element real particle breakable model generated by the grid number Np=100, the grid regularity parameter alpha=0.0 and alpha=0.8. Figure 18 The figure is the comparison of the single particle breakage situation for the test and simulation, and the simulation effect is realized.

[0073] Based on the Voronoi algorithm, a method for controlling the number of Voronoi grid units by controlling the number of random points (seed points) is creatively proposed, the distribution of random points (seed points) is controlled, and the method for controlling the regularity of Voronoi grid is further controlled. By controlling the Voronoi grid division, the difference degree of the number and volume size of the sub-particles in the particle breakage can be adjusted, the simulation error can be reduced, and effective technical means for in-depth study of the influence of the shape and number of sub-particles on the particle breakage are provided. Step 4 is a key step for realizing step 2, computer two-dimensional and three-dimensional geometry knowledge is used, the use of particle contour cutting Voronoi unit collection is realized, the real particle Voronoi grid division model is built, and the real particle modeling in the particle breakage research is realized.

[0074] The remaining matters of the present application are known technologies.

[0075] The above only describes the preferred embodiments of the present application, and it should be pointed out that, for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A modeling method of a real particle shape breakable particle model based on controllable grid division quantity and regular degree, characterized in that: The method comprises the following steps: Step 1: selecting a real particle, taking a picture by a camera and obtaining a two-dimensional real particle contour boundary through a deep learning image recognition technology, scanning by a scanner and obtaining real three-dimensional particle contour information needing to be modeled through a 3D scanning software; Step 2: reading the two-dimensional and three-dimensional contour information of the particle as a model boundary, generating random points in the particle model by controlling the regularity of the grid, then generating contour information, and finally extracting random point coordinate information and contour information; Step 3: importing the random point coordinate information and the contour information into a software to generate a unit assembly, and extracting all information of the unit assembly; Step 4: extracting the vertex coordinates of each unit in the unit assembly, numbering each unit, reading the information of a plurality of polyhedral units and the particle contour information of a certain number, if the unit intersects with the contour, using a convex hull segmentation algorithm to complete the cutting of a plurality of polyhedrons through a data point replacement method, judging the information of the cutting part inside and outside the contour according to the particle contour, extracting the vertex information inside the contour, and if the unit is inside the contour, extracting all vertex information of the unit; Step 5: repeating step 4, cutting all polygons or three-dimensional polyhedral units according to the number, and saving the vertex information in the same file in the same way; Step 6: importing the file data information of the previous step into a discrete element software to generate all polygons or three-dimensional polyhedral units, i.e. a real particle model that can be broken, using parallel bonding method to give bonding force between units, and then simulating particle breakage; The specific process of the method for controlling the regularity of the grid in step 2 is as follows: a. First, calculate the area S or volume V of the particle contour; a. Set the number of random points or seed points Np based on the Voronoi algorithm; c. the average equivalent radius r of the divided grid cells is found, , in three dimensions: ; d. Define the grid regularity parameter α, where 0.0≤α≤0.8, ensure that the minimum distance Lmin between random points is greater than α*2*r, and the random points are generated inside the contour; e. Control the size of the parameter α to control the regularity of the grid.

2. The modeling method of the real particle shape breakable particle model based on the controllable number of mesh divisions and the degree of regularity according to claim 1, characterized in that: The specific process of step 1 is as follows: the real particle comes from blasting gravel, crushed stone in actual engineering, or pebbles in riverbed, the two-dimensional real particle contour boundary is obtained by camera shooting and deep learning image recognition technology, the data file is saved as a JSON file, the three-dimensional contour information of the real particle is obtained by a scanner, a particle model consistent with the shape and volume of the real particle is generated by point cloud modeling and triangular mesh splicing, and the model is saved as an STL format file.

3. The modeling method of the real particle shape breakable particle model based on the controllable number of mesh divisions and the degree of regularity according to claim 1, characterized in that: In step 2, the information of the two-dimensional and three-dimensional contours of the particle is read through a python compiler to generate random points inside the particle model. After generating the random points, continue to generate the circumscribed rectangle or three-dimensional cube of the contour, and extract the coordinate information of the random points and the length, width and height information of the circumscribed rectangle or three-dimensional cube.

4. The modeling method of the real particle shape breakable particle model based on the controllable number of mesh divisions and the degree of regularity according to claim 1, characterized in that: In step 3, the random point information is imported into the neper software by the command of the neper software to divide the circumscribed rectangle of the contour or the three-dimensional cube into a Voronoi grid of several edges or a three-dimensional polyhedral unit set, extract the information of the unit set, and the file is a tess file containing the point, surface and edge information of each unit.

5. The modeling method of controllable mesh partition number and rule degree based real particle shape breakable particle model according to claim 1, characterized in that: In step 4, the tess file of the unit set is read by using the Python compiler to extract the vertex coordinates of each unit in the several edge or three-dimensional polyhedral unit set, number each unit, save the vertex coordinates of each unit under the number of the unit, read the information of the several edge unit numbered 1 and the particle contour information in the case of two-dimensional, if the unit intersects with the contour, extract all the vertex information of the unit, directly use the data point replacement method, use the trimesh, numpy and shapely libraries to replace the vertex coordinates outside the contour with the intersection points formed by the contour points and the contour point links in the polygon inside, retain the vertices inside the contour points, complete the reconstruction of the polygon, if the unit is inside the contour, extract all the vertex information of the unit and save it directly.

6. The modeling method of controllable mesh partition number and rule degree based real particle shape breakable particle model according to claim 1, characterized in that: In step 5, all the several edge or three-dimensional polyhedral units are cycled according to the method of step 4, all the information is extracted and converted into a command language format readable by the discrete element software, and saved as a txt format.

7. The modeling method of controllable mesh partition number and rule degree based real particle shape breakable particle model according to claim 1, characterized in that: In step 6, the txt document of step 5 is imported into the discrete element software and run, the several edge or three-dimensional polyhedron is given a cohesive force to break the particles.

8. The modeling method of controllable mesh partition number and rule degree based real particle shape breakable particle model according to claim 1, characterized in that: In step 3, the number and size difference of the sub-particles in the particle breakage can be adjusted by controlling the Voronoi grid division, and the simulation error can be reduced.

9. The modeling method of controllable mesh partition number and rule degree based real particle shape breakable particle model according to claim 1, characterized in that: In step 2, the method of controlling the grid regularity can ensure the uniformity of the number and size of the sub-particles in the particle modeling.

Images