Method for constructing porous particle model, device and computer equipment thereof

Abstract

The present invention provides a method for constructing a porous particle model, and a device and computer equipment thereof. The method first constructs a particle contour model based on real particles, and uses a radius expansion method to fill multiple sub-particles in the particle contour model to obtain a first particle model; then, the target sub-particles in contact with the boundary in the first particle model are subjected to an epitaxial operation, and then static equilibrium is performed, and the balanced sub-particles are proportionally reduced, so that the particle size distribution of the multiple sub-particles in the obtained third particle model is the same as the pore size distribution of the porous particle model to be constructed; finally, the second entity model corresponding to the real particle and the first entity model obtained by combining the entity models of all sub-particles are subjected to Boolean subtraction to obtain a porous particle model. The method provided by the present invention accurately controls the number of pores and the pore size distribution of the porous particle model based on the pore characteristics of the real particles, and the constructed porous particle model has a very high similarity to the real particles.

Description

Technical Field

[0001] The present invention belongs to the field of geotechnical engineering technology, and in particular relates to a method for constructing a porous particle model, a device thereof, and computer equipment. Background Art

[0002] Various porous granular materials are ubiquitous in nature, such as calcareous sand, volcanic scoria, and reef limestone. These materials are widely used in geotechnical engineering applications, including roadbed and embankment fillers, railway ballast, and foundation fillers. These materials are highly porous, and under external loads, stress concentration is most likely to occur at and around the tips of these pores. Therefore, porous particles generally have low failure strength. The failure behavior of porous particles can alter the macro- and micro-mechanical properties of the porous granular material, thereby impacting the performance of infrastructure.

[0003] The pore characteristics of porous particles, such as pore number and pore size distribution, significantly influence their breakage. Finite element and discrete element methods are powerful tools for studying particle breakage. Typically, numerical models with pore characteristics that meet pre-defined requirements are constructed using modeling software. These models are then imported into discrete element or finite element software for numerical simulation of single-particle breakage. This allows for the study of the breakage behavior of porous particles and the impact of their breakage on the mechanical properties of the aggregate.

[0004] The discrete element method (DEM) approach to simulating the effect of porosity on particle breakage is generally as follows: First, using the bonded cell method, the particles are modeled as a collection of cells held together by cohesive forces. Cells are typically spheres. Spherical cells are then randomly deleted to construct a porous particle model, and finally, a discrete element simulation is performed. Finite element or finite difference simulations often begin by meshing a solid particle model without holes. Mesh elements are then randomly deleted to construct a porous particle model, and finally, a finite element or finite difference simulation of particle breakage is performed.

[0005] In summary, when the existing methods are used to construct porous particle models, the deleted units or spheres are random, and the volume and number of the generated pores cannot be perfectly controlled. Therefore, it is impossible to construct a refined numerical model that can simultaneously meet the requirements of the number of pores and the pore size distribution. This restricts the research on the influence of pore factors on the crushing behavior of porous granular materials. Summary of the Invention

[0006] The purpose of the present invention is to provide a new method for constructing a porous particle model, which accurately controls the number of pores and the pore size distribution of the porous particle model based on the pore characteristics of real particles, so that the constructed porous particle model and the real particles are extremely similar, providing an effective technical means for further simulating the crushing behavior of porous particles.

[0007] To achieve the above object, the present invention provides a method for constructing a porous particle model, the method comprising the following steps:

[0008] Step S10: constructing a particle contour model based on the selected real particles, and filling a plurality of sub-particles in the particle contour model using a radius expansion method to obtain a first particle model, wherein the ratio of the sum of the volumes of the plurality of sub-particles in the first particle model to the volume of the particle contour model is greater than a target volume ratio, wherein the number of the plurality of sub-particles is the same as the number of pores of the porous particle model to be constructed, and the particle size distribution of the plurality of sub-particles in a filled state has a first corresponding relationship with the pore size distribution of the pores of the porous particle model;

[0009] Step S20: traverse all sub-particles in the first particle model, determine multiple target sub-particles that are in contact with the particle contour model, and perform extension operations on the multiple target sub-particles according to preset rules to obtain a second particle model;

[0010] Step S30: statically balancing the second particle model, and scaling down all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed;

[0011] Step S40: Based on the third particle model, a first entity model is obtained, where the first entity model is obtained by combining entity models of all sub-particles;

[0012] Step S50: acquiring a second entity model corresponding to the real particle based on the particle contour model, and performing a Boolean subtraction operation on the second entity model and the first entity model to construct the porous particle model.

[0013] In a specific embodiment, the construction method further includes step S60 after step S50, wherein:

[0014] In step S60, the porous particle model is meshed to construct a breakable porous particle model.

[0015] In a specific embodiment, the preset rule includes: using the line connecting the center of the particle contour model and the center of the current sub-target particle as an extension line, so that the current sub-target particle moves along the extension line away from the center of the particle contour model by a target distance, and the value of the target distance is between 0 and R, R is the radius of the current sub-target particle, and the current sub-target particle is any one of the multiple target sub-particles.

[0016] In a specific embodiment, the step of filling a plurality of sub-particles in the particle contour model using a radius expansion method to obtain a first particle model includes:

[0017] Step (1), determining the number of a plurality of sub-particles and a first particle size of the plurality of sub-particles in an initial state based on a pore characteristic of the porous particle model to be constructed, wherein the pore characteristic includes the number of pores and a pore size distribution, wherein the number of the plurality of sub-particles is the same as the number of pores, and the particle size distribution of the plurality of sub-particles in the initial state and the pore size distribution have a second corresponding relationship;

[0018] Step (2), using the particle outline model as a boundary, filling all of the plurality of sub-particles into the particle outline model to obtain a filled model;

[0019] Step (3), calculating the sum V1 of the volumes of all sub-particles in the filling model, and calculating the ratio of V1 to the volume V2 of the particle outline model to obtain a volume ratio;

[0020] Step (4), when the volume ratio is less than the target volume ratio, the radius of each sub-particle is enlarged by m times to obtain an updated filling model, wherein 1.02≤m≤1.08;

[0021] Step (5): Repeat steps (3) and (4) until the volume ratio corresponding to the updated filling model is greater than the target volume ratio, and obtain the first particle model, wherein all sub-particles of the first particle model do not contact each other, wherein the particle size distribution of the multiple sub-particles in the first particle model in the filling completion state and the pore size distribution have a first corresponding relationship.

[0022] In a specific embodiment, the step of constructing a particle contour model based on the shape of the selected real particles includes:

[0023] Randomly selecting real particles, where the real particles are one of gravel, pebble and rock particles;

[0024] Obtaining an initial geometric model of the real particles by an imaging method or a three-dimensional scanning method;

[0025] The initial geometric model is reconstructed by a spherical harmonic function analysis method or a four-sided surface reconstruction network method to construct the particle contour model.

[0026] In a specific embodiment, step S40 includes:

[0027] Step (a), obtaining data information of each sub-particle in the third particle model, wherein the data information includes the center position coordinates and radius information of the sub-particle;

[0028] Step (b), generating a sub-particle geometric model corresponding to each particle based on the data information of each sub-particle, and obtaining a geometric model set, wherein the geometric model set includes the sub-particle geometric models corresponding to all sub-particles;

[0029] Step (c): Based on the geometric model set, a first solid model is obtained, where the first solid model is obtained by combining the solid models of all sub-particles.

[0030] The present invention provides a device for constructing a porous particle model, the device comprising:

[0031] a first acquisition module, configured to construct a particle contour model based on selected real particles, and fill a plurality of sub-particles within the particle contour model using a radius expansion method to obtain a first particle model, wherein a ratio of a sum of volumes of the plurality of sub-particles in the first particle model to a volume of the particle contour model is greater than a target volume ratio, wherein the number of the plurality of sub-particles is the same as the number of pores of the porous particle model to be constructed, and a particle size distribution of the plurality of sub-particles in a filled state has a first corresponding relationship with a pore size distribution of the pores of the porous particle model;

[0032] a second acquisition module, configured to traverse all sub-particles in the first particle model, determine a plurality of target sub-particles in contact with the particle contour model, and perform an extension operation on the plurality of target sub-particles according to a preset rule to acquire a second particle model;

[0033] a third acquisition module, configured to statically balance the second particle model and proportionally reduce all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the plurality of sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed;

[0034] a fourth acquisition module, configured to acquire a first entity model based on the third particle model, wherein the first entity model is obtained by combining entity models of all sub-particles;

[0035] A first construction module is used to obtain a second entity model corresponding to the real particle based on the particle contour model, and perform a Boolean subtraction operation on the second entity model and the first real model to construct the porous particle model.

[0036] In a specific embodiment, the construction device further includes a second construction module, and the second construction module is used to perform grid division on the porous particle model to construct a breakable porous particle model.

[0037] The present invention also provides a computer device, comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the processor executes the computer program, the computer device implements the construction method described above.

[0038] The present invention also provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor, the processor executes the construction method described above.

[0039] The beneficial effects of the present invention include at least:

[0040] The present invention provides a method for constructing a porous particle model, the method comprising constructing a particle contour model based on real particles, and using a radius expansion method to fill a plurality of sub-particles in the particle contour model to obtain a first particle model; performing an epitaxial operation on the target sub-particles in contact with the boundary in the first particle model, and then performing a static balance, and performing a geometric reduction operation on the balanced sub-particles, so that the particle size distribution of the plurality of sub-particles in the obtained third particle model is the same as the pore size distribution of the porous particle model to be constructed; and then combining a second entity model corresponding to the real particle and a first entity model obtained by combining the entity models of all the sub-particles. Boolean subtraction is performed on the solid model to obtain the porous particle model; in this way, the number of holes in the porous particle model is the same as the number of sub-particles, the pore size distribution of the holes is the same as the particle size distribution of the sub-particles, and the positions of the holes correspond to the positions of the sub-particles. Since the number of sub-particles and the particle size distribution of the sub-particles in the first solid model can be precisely controlled, the hole characteristics of the porous particle model can be precisely controlled. That is, under the premise of knowing the hole characteristics of the real particles, a porous particle model with the same hole characteristics as the real particles can be constructed, which provides an effective technical means for further simulating the crushing behavior of porous particles.

[0041] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of the steps of a method for constructing a porous particle model provided by one embodiment of the present invention;

[0043] Figure 2 The initial geometric model diagram of the real particle obtained in step S10;

[0044] Figure 3 The particle contour model image reconstructed in step S10;

[0045] Figure 4A pore size distribution curve diagram of a hole provided in one embodiment of the present invention;

[0046] Figure 5 is a relationship diagram of the particle contour model when the multiple particles are in the initial state in step S10;

[0047] Figure 6 The first particle model image obtained in step S10;

[0048] Figure 7 The second particle model image obtained in step S20;

[0049] Figure 8 The third particle model image obtained in step S30;

[0050] Figure 9 The porous particle model diagram constructed in step S50;

[0051] Figure 10 A model diagram of the breakable porous particles constructed in step S60;

[0052] Figure 11 Three porous particle models constructed under different pore numbers;

[0053] Figure 12 A module diagram of a device for constructing a porous particle model provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0054] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] See also Figure 1 The present invention provides a method for constructing a porous particle model, which comprises the following steps:

[0056] Step S10: construct a particle contour model based on the selected real particles, and use the radius expansion method to fill multiple sub-particles in the particle contour model to obtain a first particle model, wherein the ratio of the sum of the volumes of the multiple sub-particles in the first particle model to the volume of the particle contour model is greater than the target volume ratio, wherein the number of the multiple sub-particles is the same as the number of pores of the porous particle model to be constructed, and the particle size distribution of the multiple sub-particles in the filled state has a first corresponding relationship with the pore size distribution of the pores of the porous particle model.

[0057] The step of constructing a particle contour model based on the shape of the selected real particles includes:

[0058] Step (1): randomly selecting real particles, wherein the real particles are one of gravel, pebbles or rock and soil particles.

[0059] In the present invention, the real particles selected are 20 mm limestone crushed stone particles.

[0060] Step (2): obtaining the initial geometric model of the real particles by an imaging method or a three-dimensional scanning method.

[0061] In the present invention, the initial geometric model is a three-dimensional model.

[0062] The imaging method involves capturing particle outlines through close-up photography. First, the particles are photographed continuously. After each shot, the rock is rotated a certain angle and photographed around the horizontal axis from three different elevation angles. The rotation angle is controlled to ensure that the overlap between two adjacent photos is at least 60%. This ensures that the photos fully cover the rock mass. Finally, all photos of each particle are input into a computer for 3D reconstruction, resulting in an initial geometric model of the rock particles.

[0063] The three-dimensional scanning method generates an initial geometric model composed of triangular meshes through relative position calibration, acquisition of particle surface point cloud data, three-dimensional point cloud modeling, automatic model splicing, and model sharpening. Specifically: a three-dimensional white light object scanner is used to obtain the particle outline. Before scanning, position calibration and the number of scans per turntable rotation are required; the particle is then placed in the center of the turntable and scanned for a circle to obtain the particle surface point cloud data. The particle is then turned so that the unscanned part is exposed and scanned a second time. After each scan, the point cloud is automatically spliced. By observing whether there are large holes in the particle, it is determined whether the particle surface has been completely scanned. If there are still large holes, continue the particle morphology adjustment in the previous step until the particle outline formed by the point cloud can truly reflect the actual particle shape. The scan is considered to be complete. Point cloud modeling is performed through sharpening processing to generate an initial geometric model composed of triangular meshes.

[0064] In the present invention, a Wiiboox Reeyee 3D white light object scanner is used to scan real particles to obtain particle contour information, and an initial geometric model consistent with the real shape is generated through point cloud modeling technology and splicing technology. The initial geometric model obtained by the final scan is as follows: Figure 2 As shown in the figure, the model surface is composed of a large number of triangular meshes through topological relationships and is saved as a simple and widely used "STL" format file.

[0065] Step (3): reconstructing the initial geometric model by spherical harmonic function analysis method or four-sided surface reconstruction network method to construct the particle contour model.

[0066] In the present invention, particles are reconstructed using spherical harmonics analysis (spherical harmonic function analysis). The similarity between the reconstructed particle contour model and the initial geometric model is determined by the number of triangular meshes and the order of spherical harmonics. A greater number of meshes and a higher order result in a more accurate reconstructed particle contour model. However, this increases the modeling time and the discrete element simulation load.

[0067] Previous studies have shown that the overall shape of irregular particles can be well reflected when the number of meshes in the reconstructed particle model is greater than 1280 and the order is greater than 15. In the present invention, preferably, the number of triangular meshes is 1280 to 2000, and the spherical harmonic order is 15.

[0068] In this embodiment, the number of triangular meshes is 1536, the order is 15, and the particle contour model obtained by reconstruction is as follows: Figure 3 As shown, the model is also saved as an "STL" format file. The particle contour model has a grid cell count of 1536 and a spherical harmonic order of 15. This allows for a more comprehensive reflection of the particle contour and eliminates extremely subtle bumps and depressions in the contour, facilitating rapid modeling and subsequent simulation. Of course, other methods can be used to reduce the number of grid cells, such as Rhino software, but these methods produce contour surface meshes with high randomness and poor practicality.

[0069] In this step, the particle model can also be reconstructed through the quadrilateral reconstruction network operation provided by the Rhino3D modeling software. To ensure computational efficiency, the number of grids should not be too large, approximately 2,000 is appropriate.

[0070] The step of filling a plurality of sub-particles in the particle contour model using the radius expansion method to obtain a first particle model includes:

[0071] Step (1): determining the number of multiple sub-particles and the first particle size of the multiple sub-particles in an initial state based on the pore characteristics of the porous particle model to be constructed, wherein the pore characteristics include the number of pores and the pore size distribution, wherein the number of the multiple sub-particles is the same as the number of pores, and the particle size distribution of the multiple sub-particles in the initial state and the pore size distribution have a second corresponding relationship.

[0072] In the present invention, the sub-particles are all spherical particles.

[0073] The number of the multiple sub-particles is the same as the number of holes. It can be understood that if the number of holes in the porous particle model to be constructed is 100, the number of sub-particles is 100; if the number of holes in the porous particle model to be constructed is 200, the number of sub-particles is 200.

[0074] In the present invention, the particle size distribution of the multiple sub-particles in the initial state and the pore size distribution have a second corresponding relationship. It can be understood that the shape of the particle size distribution curve of the multiple sub-particles in the initial state is the same as the shape of the pore size distribution curve, and the first particle size of the multiple sub-particles and the pore size of the multiple holes have a proportionally reduced mapping relationship.

[0075] For ease of understanding, Figure 4 An example of pore size distribution curve is given below. Figure 4 The horizontal axis is the aperture (aperture range is 1.0-2.0 mm, and the unit length is 0.05 mm), and the vertical axis is the cumulative probability distribution (0-100%). Figure 4 The probability distribution and number of holes in each aperture range can be determined. For example, the probability distribution value of holes 1.2mm to 1.3mm is the difference (x%) between the value of the ordinate corresponding to the aperture 1.3mm and the value of the ordinate corresponding to the aperture 1.2mm. If the total number of holes is 100, the number of holes between 1.2mm and 1.3mm is 100*x%.

[0076] The shape of the particle size distribution curve of the multiple sub-particles is the same as the shape of the pore size distribution curve, and the first particle size of the multiple sub-particles has a proportionally reduced mapping relationship with the pore size of the multiple holes. It can be understood that: the abscissa of the particle size distribution curve of the multiple sub-particles is the particle size (equivalent to the pore size), and the ordinate is the cumulative probability distribution, and the ordinate of the particle size distribution curve of the multiple sub-particles is the same as the ordinate of the pore size distribution curve, and the particle size of the abscissa is proportionally reduced compared with the pore size, for example, if it is proportionally reduced by 5 times, the particle size range is 0.2-0.4 mm, and the unit length is 0.01 mm (the total length is divided into 20 equal parts), or if it is proportionally reduced by 4 times, the particle size range is 0.25-0.5 mm, and the unit length is 0.0125 mm (the total length is divided into 20 equal parts), or if it is proportionally reduced by 2 times, the particle size range is 0.5-1.0 mm, and the unit length is 0.025 mm (the total length is divided into 20 equal parts).

[0077] If the proportional reduction factor is 5 times, when the number of holes between 1.2 mm and 1.3 mm is 100*x%, the number of sub-particles between 0.24 mm and 0.26 mm in size is 100*x%.

[0078] See also Figure 5 , Figure 5 is a relationship diagram between the sub-particle arrangement and the particle contour model in this step, that is, a relationship diagram between the multiple sub-particle arrangements and the particle contour model in the initial state, wherein, Figure 5 The 1 refers to the sub-particle, and the 2 refers to the outline of the particle outline model.

[0079] Step (2): using the particle contour model as a boundary, filling all of the plurality of sub-particles into the particle contour model to obtain a filled model.

[0080] In the present invention, this step is specifically as follows: importing the particle contour model into the discrete element software PFC3D and converting it into a wall, and then generating multiple sub-particles inside the wall based on the number of sub-particles and the first particle size of the sub-particles determined in step (1).

[0081] Step (3): Calculate the sum V1 of the volumes of all sub-particles in the filling model, and calculate the ratio of V1 to the volume V2 of the particle outline model to obtain a volume ratio.

[0082] In the present invention, the sub-particles are all spherical particles, and the particle size of the sub-particles is a known value. According to the volume calculation formula of a sphere, the volume of each sub-particle can be calculated, and then the volumes of all sub-particles are added together to obtain the sum of the volumes of all sub-particles V1. The sum of the volumes of the particle contour model V2 is a known value, so the volume ratio = V1 / V2.

[0083] Step (4): When the volume ratio is less than the target volume ratio, the radius of each sub-particle is enlarged by m times to obtain an updated filling model, wherein 1.02≤m≤1.08.

[0084] Preferably, m is 1.05, that is, when the volume ratio is less than the target volume ratio, the radius of each sub-particle is enlarged by 1.05 times to obtain an updated filling model.

[0085] Step (5): Repeat steps (3) and (4) until the volume ratio corresponding to the updated filling model is greater than the target volume ratio, thereby obtaining the first particle model, wherein all sub-particles of the first particle model do not contact each other, and wherein the particle size distribution curve of the multiple sub-particles in the first particle model in the filling completion state and the pore size distribution curve have a first corresponding relationship.

[0086] In the present invention, the target volume ratio is 0.6 to 0.8, and specifically can be 0.6, 0.62, 0.65, 0.7, 0.75, 0.8 and other values, which are not enumerated here.

[0087] Preferably, the target volume ratio is 0.7.

[0088] In the present invention, the particle size distribution of the plurality of sub-particles in the fully filled state and the pore size distribution have a second corresponding relationship. This can be understood as follows: the shape of the particle size distribution curve of the plurality of sub-particles in the fully filled state is the same as the shape of the pore size distribution curve, and the second particle size of the plurality of sub-particles and the pore size of the plurality of pores have a proportionally enlarged mapping relationship. If the proportional enlargement factor is 3, when the number of pores between 1.2 mm and 1.3 mm is 100*x%, then the number of sub-particles in the particle size range of 3.6 mm to 3.9 mm is 100*x%.

[0089] See also Figure 6 , Figure 6 The first particle model image obtained in step S10, wherein: Figure 6 The 1 refers to the sub-particle, and the 2 refers to the outline of the first particle model.

[0090] Step S20: traverse all sub-particles in the first particle model, determine multiple target sub-particles that are in contact with the particle contour model, and perform extension operations on the multiple target sub-particles according to preset rules to obtain a second particle model.

[0091] Preferably, the preset rule includes: using the line connecting the center of the particle contour model and the center of the current sub-target particle as an extension line, so that the current sub-target particle moves a target distance away from the center of the particle contour model along the extension line, and the value of the target distance is between 0 and R, R is the radius of the current sub-target particle, and the current sub-target particle is any one of the multiple target sub-particles.

[0092] For ease of understanding, let's take an example. Assume that the number of sub-particles in contact with the particle contour model is 20, then the number of target sub-particles is 20. This step requires moving these 20 target sub-particles along the extension line. If the radius of the target sub-particle is 1 mm, then the target sub-particle can be moved away from the center of the particle contour model by distances such as 0 mm, 0.1 mm, 0.2 mm, 0.3 mm, 0.5 mm, and 1.0 mm.

[0093] Preferably, the movement distances of the multiple target sub-particles are evenly distributed within the range [0, R]. This means that each movement distance corresponds to the same number of target sub-particles. For example, if the movement distances of the multiple target sub-particles are set to 5 values, and the total number of target sub-particles is 20, then each movement distance corresponds to 4 target sub-particles.

[0094] In the present invention, the moving distance of all target sub-particles is R.

[0095] In the present invention, the sub-particle is a sphere, and the center of the sub-particle is the sphere center.

[0096] In the present invention, the center of the particle outline model is determined based on the vertex coordinates of the particle outline model. Specifically, when the number of vertices in the particle outline model is 10, the arithmetic mean of the coordinate values ​​of the 10 vertices on the X-axis is the coordinate value on the X-axis corresponding to the center of the particle outline model, the arithmetic mean of the coordinate values ​​of the 10 vertices on the Y-axis is the coordinate value on the Y-axis corresponding to the center of the particle outline model, and the arithmetic mean of the coordinate values ​​of the 10 vertices on the Z-axis is the coordinate value on the Z-axis corresponding to the center of the particle outline model.

[0097] See also Figure 7 , Figure 7 This is the second particle model image obtained in step S20.

[0098] Step S30: statically balance the second particle model, and proportionally reduce all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the multiple sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed.

[0099] In the present invention, in step S20, the multiple target sub-particles in contact with the particle contour model move outward by a certain distance, which makes the distances between the sub-particles uneven. In this step, a static balance operation is performed to make the distances between the multiple sub-particles uniformly distributed. Specifically, a static balance operation is performed on the second particle model using discrete element software PDC3D.

[0100] In this step, proportional reduction refers to proportionally reducing the particle size of the sub-particles.

[0101] For ease of understanding, based on the above example, if the second particle size of multiple sub-particles in the first particle model is proportionally enlarged by 3 times relative to the pore size of the pore to be constructed, in this step, the second particle size of all sub-particles is reduced by 3 times. In this way, the particle size distribution of multiple sub-particles in the third particle model is exactly the same as the pore size distribution of the pores of the porous particle model to be constructed, that is, the particle size distribution curve of the sub-particles is the same as that of the pores of the porous particle model to be constructed. Figure 4 Basically the same, the same includes that the horizontal axis and the vertical axis are exactly the same, the only difference is that the pore size is changed to the particle size.

[0102] See also Figure 8 , the figure is the third particle model diagram obtained in step S30.

[0103] Step S40: Based on the third particle model, a first entity model is obtained, where the first entity model is obtained by combining entity models of all sub-particles.

[0104] This step includes:

[0105] Step (a): obtaining data information of each sub-particle in the third particle model, wherein the data information includes the center position coordinates and radius information of the sub-particle.

[0106] Step (b): generating a sub-particle geometric model corresponding to each particle based on the data information of each sub-particle, and obtaining a geometric model set, wherein the geometric model set includes the sub-particle geometric models corresponding to all sub-particles.

[0107] Step (c): Based on the geometric model set, a first solid model is obtained, where the first solid model is obtained by combining the solid models of all sub-particles.

[0108] In the present invention, the specific operation of this step is as follows: first, the center position coordinates and radius values ​​of each sub-particle in the third particle model are extracted, and then, using the FISH function provided by the PFC software, these are written to a text file in the format of sphere modeling instructions in the Rhino3D modeling software. This text file is read in the Rhino3D modeling software through "Tools → Instruction Set → Read from File...", thereby batch-generating a set of sub-particle geometric models consistent with those in PFC3D in the Rhino3D software, and the models generated by the Rhino3D software are saved in STEP format; the saved set of sub-particle geometric models in STEP format is imported into the ANSYS numerical software to generate a first solid model composed of the solid models of all sub-particles.

[0109] Step S50: obtaining a second entity model corresponding to the real particle based on the particle contour model, performing a Boolean subtraction operation on the second entity model and the first entity model to construct the porous particle model.

[0110] The method for obtaining the second solid model is specifically as follows: importing the particle contour model in step format into ANSYS numerical software, and generating the second solid model through command operation. The second solid model is the solid model of the real particles.

[0111] In the present invention, the Boolean subtraction operation is performed using ANSYS numerical software. Specifically, the solid models of all sub-particles are independently grouped and named. Using the Boolean operation extraction instructions provided by ANSYS software, the second solid model is selected as the first target geometry, and the first solid model is selected as the second target geometry. The portion of the real particle solid model (the second solid model) that overlaps with the solid model of the sub-particle is subtracted to ultimately construct the porous particle model. During this step, when performing the Boolean operation, the solid models of all sub-particles can be selected at once in a grouped manner to prevent omissions.

[0112] In the present invention, the number of pores in the porous particle model is the same as the number of sub-particles, and the pore size distribution of the pores is the same as the particle size distribution of the sub-particles. Since the number of sub-particles and the particle size distribution of the sub-particles in the first solid model can be precisely controlled, the pore characteristics of the porous particle model can be precisely controlled. That is, under the premise of knowing the pore characteristics of real particles, a porous particle model with the same pore characteristics as the real particles can be constructed, which provides an effective technical means for further simulating the crushing behavior of porous particles.

[0113] See also Figure 9 , Figure 9 This is the porous particle model diagram constructed in step S50.

[0114] Preferably, the method further comprises step S60, wherein:

[0115] The step S60 is: performing mesh division on the porous particle model to construct a breakable porous particle model.

[0116] See also Figure 10 , Figure 10 The breakable porous particle model obtained by constructing step S60.

[0117] In the present invention, the breakable porous particle model can be converted into a discrete element or finite element numerical model through an interface program, so as to facilitate the simulation of real particle breakage.

[0118] In the present invention, the porous particle geometric model is converted into an aggregate composed of numerous tetrahedrons.

[0119] In the present invention, ANSYS software is used for mesh generation.

[0120] ANSYS offers a variety of meshing standards, allowing you to customize the model as needed, achieving high-quality meshing while ensuring computational accuracy and efficiency. Once meshed, the model is saved in a text-readable format. Through the software's interface with other software (such as discrete element software PFC or finite difference software FLAC3D), you can import it into other software to simulate particle breakage and explore the impact of porosity on the breakage of porous particles.

[0121] Example

[0122] The present invention uses the method described above to construct three particle models with different profiles. Based on the particle model of each profile, multiple porous particle models are constructed under the conditions of 0, 100, 200, 400, and 800 holes, respectively. Figure 11 .

[0123] It should be noted that Figure 11Each row in corresponds to a contour, and each column corresponds to the same number of holes. The first column corresponds to 0 holes, the second column corresponds to 100 holes, the third column corresponds to 200 holes, the fourth column corresponds to 400 holes, and the fifth column corresponds to 800 holes.

[0124] It should be noted that when building Figure 11 When corresponding to multiple porous particle models, the aperture of each hole is the same, which is 1.7 mm. In step S20, the moving distance of all target sub-particles is R, where R is 1 / 2 of the aperture.

[0125] See also Figure 12 The present invention also provides a device for constructing a porous particle model, wherein the device 100 comprises:

[0126] A first acquisition module 101 is configured to construct a particle contour model based on selected real particles, and fill the particle contour model with a plurality of sub-particles using a radius expansion method to obtain a first particle model, wherein the ratio of the sum of the volumes of the plurality of sub-particles in the first particle model to the volume of the particle contour model is greater than a target volume ratio, wherein the number of the plurality of sub-particles is the same as the number of pores of the porous particle model to be constructed, and the particle size distribution of the plurality of sub-particles in a filled state has a first corresponding relationship with the pore size distribution of the pores of the porous particle model;

[0127] A second acquisition module 102 is configured to traverse all sub-particles in the first particle model, determine a plurality of target sub-particles in contact with the particle contour model, and perform an extension operation on each of the plurality of target sub-particles according to a preset rule to obtain a second particle model;

[0128] a third acquisition module 103, configured to statically balance the second particle model and geometrically reduce all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed;

[0129] A fourth acquisition module 104, wherein the third acquisition module is configured to acquire a first entity model based on the third particle model, wherein the first entity model is obtained by combining entity models of all sub-particles;

[0130] The first construction module 105, the fourth acquisition module is used to obtain a second entity model corresponding to the real particle based on the particle contour model, and perform a Boolean subtraction operation on the second entity model and the first entity model to construct the porous particle model.

[0131] Preferably, the construction device 100 further includes a second construction module 106, which is used to perform grid division on the porous particle model to construct a breakable porous particle model.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the working process of the construction device described above can refer to the contents of the aforementioned method embodiment and will not be repeated here.

[0133] The present invention also provides a computer device, Figure 12 The construction device shown can be deployed in the computer device. The computer device includes a memory, a processor, a communication interface, and a bus. The memory, processor, and communication interface are interconnected via the bus. Furthermore, the computer device may include multiple processors, so that different processors can implement the functions of the different modules described above.

[0134] The memory may be a read-only memory, a static storage device, a dynamic storage device, or a random access memory. The memory may store executable code. When the executable code stored in the memory is executed by the processor, the processor and the communication interface are used to execute the method for constructing a porous particle model provided in the embodiments of the present application. The memory may also include software modules and data required for other running processes, such as an operating system. The operating system may be LINUX, UNIX, WINDOWS™, etc.

[0135] The processor may be a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), a graphics processing unit (GPU), or one or more integrated circuits.

[0136] The processor can also be an integrated circuit chip with signal processing capabilities. During implementation, some or all of the functions of the method for constructing the porous particle model of the present application can be completed by hardware integrated logic circuits or software instructions in the processor. The above-mentioned processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the method for constructing the porous particle model of the embodiment of the present application in combination with its hardware.

[0137] A communication interface uses a transceiver module, such as, but not limited to, a transceiver, to enable communication between a computer device and other devices or a communication network. For example, a communication interface can be any one or a combination of the following devices: a network interface (such as an Ethernet interface), a wireless network card, or other network access device.

[0138] A bus may include a pathway that transfers information between various components of a computer device (eg, memory, processor, communication interface).

[0139] Each of the aforementioned computer devices establishes a communication path through a communication network. Each computer device is used to implement part of the functions of the method for constructing a porous particle model provided in the embodiments of the present application. Any computer device can be a computer device (e.g., a server) in a cloud data center, or a computer device in an edge data center, etc.

[0140] The descriptions of the processes corresponding to the above figures have different focuses. For parts that are not described in detail in a certain process, please refer to the relevant descriptions of other processes.

[0141] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product providing the data synchronization cloud service includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer device, the process or function of the method for constructing a porous particle model provided in the embodiments of the present application is fully or partially implemented.

[0142] The computer device may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium stores computer program instructions for providing data synchronization cloud services.

[0143] An embodiment of the present application further provides a storage medium, which is a non-volatile computer-readable storage medium. When the instructions in the storage medium are executed by a processor, the method for constructing a porous particle model provided in the embodiment of the present application is implemented.

[0144] An embodiment of the present application also provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer executes the method for constructing a porous particle model provided in the embodiment of the present application.

[0145] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0146] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for constructing a porous particle model, characterized in that: The construction method comprises the following steps: Step S10: constructing a particle contour model based on the selected real particles, and filling a plurality of sub-particles in the particle contour model using a radius expansion method to obtain a first particle model, wherein the ratio of the sum of the volumes of the plurality of sub-particles in the first particle model to the volume of the particle contour model is greater than a target volume ratio, wherein the number of the plurality of sub-particles is the same as the number of pores of the porous particle model to be constructed, and the particle size distribution of the plurality of sub-particles in a filled state has a first corresponding relationship with the pore size distribution of the pores of the porous particle model; Step S20: traverse all sub-particles in the first particle model, determine multiple target sub-particles that are in contact with the particle contour model, and perform extension operations on the multiple target sub-particles according to preset rules to obtain a second particle model; Step S30: statically balancing the second particle model, and scaling down all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed; Step S40: Based on the third particle model, a first entity model is obtained, where the first entity model is obtained by combining entity models of all sub-particles; Step S50: acquiring a second entity model corresponding to the real particle based on the particle contour model, and performing a Boolean subtraction operation on the second entity model and the first entity model to construct the porous particle model.

2. The method for constructing a porous particle model according to claim 1, wherein: The construction method further includes step S60 after step S50, wherein: In step S60, the porous particle model is meshed to construct a breakable porous particle model.

3. The method for constructing a porous particle model according to claim 1 or 2, characterized in that: The preset rule includes: using a line connecting the center of the particle contour model and the center of the current sub-target particle as an extension line, and moving the current sub-target particle along the extension line away from the center of the particle contour model by a target distance, wherein the value of the target distance is between 0 and R, where R is the radius of the current sub-target particle, and the current sub-target particle is any one of the multiple target sub-particles.

4. The method for constructing a porous particle model according to claim 1 or 2, characterized in that: The step of filling a plurality of sub-particles in the particle contour model using the radius expansion method to obtain a first particle model includes: Step (1), determining the number of a plurality of sub-particles and a first particle size of the plurality of sub-particles in an initial state based on a pore characteristic of the porous particle model to be constructed, wherein the pore characteristic includes the number of pores and a pore size distribution of the pores, wherein the number of the plurality of sub-particles is the same as the number of pores, and the particle size distribution of the plurality of sub-particles in the initial state and the pore size distribution have a second corresponding relationship; Step (2), using the particle outline model as a boundary, filling all of the plurality of sub-particles into the particle outline model to obtain a filled model; Step (3), calculating the sum V1 of the volumes of all sub-particles in the filling model, and calculating the ratio of V1 to the volume V2 of the particle outline model to obtain a volume ratio; Step (4), when the volume ratio is less than the target volume ratio, the radius of each sub-particle is enlarged by m times to obtain an updated filling model, wherein 1.02≤m≤1.08; Step (5): Repeat steps (3) and (4) until the volume ratio corresponding to the updated filling model is greater than the target volume ratio, and obtain the first particle model, wherein all sub-particles of the first particle model do not contact each other, wherein the particle size distribution of the multiple sub-particles in the first particle model in the filling completion state and the pore size distribution have a first corresponding relationship.

5. The method for constructing a porous particle model according to claim 4, characterized in that: The step of constructing a particle contour model based on the selected real particles includes: Randomly selecting real particles, where the real particles are one of gravel, pebble and rock particles; Obtaining an initial geometric model of the real particles by an imaging method or a three-dimensional scanning method; The initial geometric model is reconstructed by a spherical harmonic function analysis method or a four-sided surface reconstruction network method to construct the particle contour model.

6. The method for constructing a porous particle model according to claim 1, wherein: The step S40 includes: Step (a), obtaining data information of each sub-particle in the third particle model, wherein the data information includes the center position coordinates and radius information of the sub-particle; Step (b), generating a sub-particle geometric model corresponding to each particle based on the data information of each sub-particle, and obtaining a geometric model set, wherein the geometric model set includes the sub-particle geometric models corresponding to all sub-particles; Step (c): Based on the geometric model set, a first solid model is obtained, where the first solid model is obtained by combining the solid models of all sub-particles.

7. A device for constructing a porous particle model, characterized in that: The construction device comprises: a first acquisition module, configured to construct a particle contour model based on selected real particles, and fill a plurality of sub-particles within the particle contour model using a radius expansion method to obtain a first particle model, wherein a ratio of a sum of volumes of the plurality of sub-particles in the first particle model to a volume of the particle contour model is greater than a target volume ratio, wherein the number of the plurality of sub-particles is the same as the number of pores of the porous particle model to be constructed, and a particle size distribution of the plurality of sub-particles in a filled state has a first corresponding relationship with a pore size distribution of the pores of the porous particle model; a second acquisition module, configured to traverse all sub-particles in the first particle model, determine a plurality of target sub-particles in contact with the particle contour model, and perform an extension operation on the plurality of target sub-particles according to a preset rule to acquire a second particle model; a third acquisition module, configured to statically balance the second particle model and proportionally reduce all sub-particles in the balanced second particle model to obtain a third particle model, wherein the particle size distribution of the plurality of sub-particles in the third particle model is the same as the pore size distribution of the pores of the porous particle model to be constructed; a fourth acquisition module, configured to acquire a first entity model based on the third particle model, wherein the first entity model is obtained by combining entity models of all sub-particles; A first construction module is used to obtain a second entity model corresponding to the real particle based on the particle contour model, and perform a Boolean subtraction operation on the second entity model and the first entity model to construct the porous particle model.

8. The device for constructing a porous particle model according to claim 7, characterized in that: The construction device further includes a second construction module, which is used to perform grid division on the porous particle model to construct a breakable porous particle model.

9. A computer device, characterized in that: The computer device includes: a processor and a memory, wherein a computer program is stored in the memory. When the processor executes the computer program, the computer device implements the construction method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that When the instructions in the computer-readable storage medium are executed by a processor, the processor performs the construction method according to any one of claims 1 to 6.

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