Construction method and device of porous particle model and computer equipment

Abstract

The invention provides a porous particle model construction method, a porous particle model construction device and computer equipment. The method comprises the following steps: firstly, constructing a particle contour model based on real particles, and filling a plurality of sub-particles in the particle contour model by using a radius expansion method to obtain a first particle model; carrying out epitaxial operation on target sub-particles in contact with the boundary in the first particle model, then carrying out static balance, and carrying out equal-ratio reduction on the balanced sub-particles, so that the particle size distribution of a plurality of sub-particles in an obtained third particle model is the same as the pore size distribution of the porous particle model to be constructed; and finally, performing Boolean subtraction on the second solid model corresponding to the real particles and the first solid model obtained by combining the solid models of all the sub-particles to obtain a porous particle model. According to the method provided by the invention, the hole number and the hole diameter distribution of the holes of the porous particle model are accurately controlled based on the hole characteristics of the real particles, and the similarity between the constructed porous particle model and the real particles is extremely high.

Description

Technical Field

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

[0002] Various porous granular materials are commonly found in nature, such as calcareous sand, volcanic slag and reef limestone. Such materials are widely used in geotechnical foundation engineering such as roadbed and embankment fillings, railway ballast materials, and foundation fillings. Such materials have extremely developed pores, and under the action of external loads, stress concentration is most likely to occur at the tips and around these pores. Therefore, the destructive strength of porous particles is generally low. The destructive behavior of porous particles will change the macro- and micro-mechanical properties of porous granular materials, thereby affecting the performance of infrastructure.

[0003] The pore characteristics of porous particles, such as the number of pores and the pore size distribution, have a great impact on the crushing of porous particles. Finite element method and discrete element method are powerful tools for studying particle crushing. Usually, a numerical model with pore characteristics that meet the preset requirements can be constructed through modeling software, and then the discrete element or finite element software can be imported to perform single particle crushing numerical simulation to study the crushing behavior of porous particles, thereby obtaining the effect of porous particle crushing on the mechanical properties of its aggregate.

[0004] The way to simulate the influence of pore factors on particle crushing by discrete element method is as follows: first, through the bonded cell method, the particles are simulated as a collection of cells bonded together by bonding force, and the cells are generally spheres. Then, the spherical cells are randomly deleted to construct a porous particle model, and finally the discrete element simulation is performed. When using finite element or finite difference simulation, the solid particle model without holes is often meshed first, and then the mesh units are randomly deleted to realize the construction of the porous particle model, and finally the finite element or finite difference simulation of particle crushing 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 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 can accurately control 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 highly 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 holes 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 holes of the porous particle model;

[0009] Step S20, traversing all sub-particles in the first particle model, determining a plurality of target sub-particles in contact with the particle contour model, and subjecting the plurality of target sub-particles to an extension operation according to a preset rule, respectively, to obtain a second particle model;

[0010] Step S30, subjecting the second particle model to static equilibrium, 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 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;

[0011] Step S40: acquiring 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;

[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] The step S60 is to mesh the porous particle model 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, 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 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 plurality of sub-particles is the same as the number of pores, and the particle size distribution of the plurality of sub-particles in an initial state and the pore size distribution have a second corresponding relationship;

[0018] Step (2), taking 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;

[0019] Step (3), calculating the sum V1 of the volumes of all the sub-particles in the filling model, and calculating the ratio of V1 to the volume V2 of the particle contour 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), repeating steps (3) and (4) until the volume ratio corresponding to the updated filling model is greater than the target volume ratio, obtaining 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 a filled 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] Acquire the 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 implementation, the 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), based on the data information of each sub-particle, generating a sub-particle geometric model corresponding to each 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 entity model is obtained, wherein the first entity model is obtained by combining entity 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, the first acquisition module is used to construct a particle contour model based on the selected real particles, and fill 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 holes of the porous particle model to be constructed, and the particle size distribution of the plurality of sub-particles in a filling-completed state has a first corresponding relationship with the pore size distribution of the holes of the porous particle model;

[0032] A second acquisition module, the second acquisition module is used 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 extension operations on the plurality of target sub-particles respectively according to preset rules to acquire a second particle model;

[0033] a third acquisition module, the third acquisition module being used 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, the fourth acquisition module is used to acquire a first entity model based on the third particle model, 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 filling a plurality of sub-particles in the particle contour model using a radius expansion method to obtain a first particle model; performing an epitaxial operation on a target sub-particle in contact with a boundary in the first particle model, and then performing a static balance, and performing a geometric reduction operation on the sub-particles after the balance, 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. The entity model is subjected to Boolean subtraction 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 entity 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 purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail 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 in 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 diagram reconstructed in step S10;

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

[0046] Figure 5 is a relationship diagram of the particle contour model when the plurality of 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] Fig. 9 A porous particle model diagram constructed in step S50;

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

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

[0053] Fig.12 A module diagram of a device for constructing a porous particle model provided in 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, the method comprising the following steps:

[0056] Step S10, constructing a particle contour model based on the selected real particles, and using 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 holes 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 holes of the porous particle model.

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

[0058] Step (1), randomly selecting real particles, wherein the real particles are one of gravel, pebbles or rock 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 image method is specifically as follows: the particle contour is collected by using the particle close-up photography technology. First, the particles are photographed continuously, and the stone is rotated at a certain angle after each shooting, and the rotational shooting is performed around the horizontal axis from three pitch angles. The overlapping rate of two adjacent photos is ensured to be not less than 60% by controlling the rotation angle of each shooting. In this way, the photos can fully cover the block stone entity. Finally, all the photos of each particle are input into the computer for three-dimensional reconstruction to obtain the initial geometric model of the rock and soil particles.

[0063] The three-dimensional scanning method generates an initial geometric model composed of triangular meshes through relative position calibration, particle surface point cloud data acquisition, point cloud three-dimensional modeling, model automatic splicing, model sharpening and other operations. Specifically: a three-dimensional white light object scanner is used to obtain the particle outline. Before scanning, position calibration and the number of scans for a turntable rotation are required; then the particle is placed in the center of the turntable for a scan to obtain the particle surface point cloud data, and then the particle is turned to expose the unscanned part and scanned again. After each scan, the point cloud is automatically spliced, and the particle surface is judged to be completely scanned by observing whether there are large holes in the particle. If there are still large holes, continue to adjust the particle shape in the previous step until the particle outline composed of the point cloud can truly reflect the actual particle shape. The scanning is considered to be over. 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, 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 by spherical harmonic 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. The more meshes there are and the higher the order, the more accurate the reconstructed particle contour model is. However, the corresponding modeling time will be longer and the discrete element simulation load will be greater.

[0067] Existing studies have shown that when the number of meshes of the reconstructed particle model is greater than 1280 and the order is greater than 15, the overall shape of the irregular particles can be well reflected. 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 is reconstructed as follows: Figure 3 As shown, it is also saved as an "STL" format file. When the number of meshes in the particle contour model is 1536 and the spherical harmonic order is 15, the particle contour can be more comprehensively reflected, and extremely subtle concave and convex points in the contour are eliminated, which is conducive to rapid modeling and subsequent simulation. Of course, the number of meshes can also be reduced by other means, such as Rhino software, but the contour surface meshes obtained by these methods are highly random and have poor practicality.

[0069] In this step, the particle model can also be reconstructed through the four-sided reconstruction network operation of the Rhino3D modeling software. To ensure the calculation efficiency, the number of grids should not be too large, about 2000 is appropriate.

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

[0071] Step (1), based on the pore characteristics of the porous particle model to be constructed, determine the number of multiple sub-particles and the first particle size of the multiple sub-particles in an initial state, 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, which can be understood as follows: 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, which can be understood as 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 shown 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, which can be understood as: 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 the particle size range of 0.24 mm and 0.26 mm is 100*x%.

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

[0079] Step (2), taking 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 discrete element software PFC3D and converting it into a wall, and then generating a plurality of 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), calculating the sum V1 of the volumes of all the sub-particles in the filling model, and calculating the ratio of V1 to the volume V2 of the particle contour model to obtain a volume ratio value.

[0082] In the present invention, all sub-particles are 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, and 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), repeating 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, wherein the particle size distribution curve of the multiple sub-particles in the first particle model in a 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 state of filling is in a second corresponding relationship with the pore size distribution, which can be understood as the shape of the particle size distribution curve of the plurality of sub-particles in the state of filling 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 holes have a proportionally enlarged mapping relationship. If the proportional enlargement multiple is 3 times, when the number of holes between 1.2 mm and 1.3 mm is 100*x%, 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 is 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 a plurality of target sub-particles in contact with the particle contour model, and perform extension operations on the plurality of target sub-particles respectively according to preset rules to obtain a second particle model.

[0091] Preferably, the preset rule includes: taking 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 a target distance away from the center of the particle contour model, 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, an example is given. Assuming 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, the target sub-particle can be moved away from the center of the particle contour model by distances of 0 mm, 0.1 mm, 0.2 mm, 0.3 mm, 0.5 mm, 1.0 mm, etc.

[0093] Preferably, the moving distances of the multiple target sub-particles are evenly distributed in the range of [0, R], which can be understood as the same number of target sub-particles corresponding to each moving distance. That is, if the moving distances of the multiple target sub-particles are set to 5 values, when the total number of target sub-particles is 20, the number of target sub-particles corresponding to each moving distance is 4.

[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 center of the sphere.

[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 of 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 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 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, a plurality of target sub-particles in contact with the particle contour model move outward 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 plurality of sub-particles evenly distributed. Specifically, a static balance operation is performed on the second particle model through discrete element software PDC3D.

[0100] In this step, proportional reduction refers to proportionally reducing the particle sizes 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 hole 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 porous particle model to be constructed, that is, the particle size distribution curve of the sub-particles is the same as that of the pore size 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, and the only difference is that the pore size is changed to the particle size.

[0102] See also Figure 8 , FIG. 1 is a 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): based on the data information of each sub-particle, a sub-particle geometric model corresponding to each particle is generated to obtain 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 entity model is obtained, wherein the first entity model is obtained by combining entity models of all sub-particles.

[0108] In the present invention, the specific operation of this step is: first extract the center position coordinates and radius value of each sub-particle in the third particle model, and then write them into a text file according to the format of the sphere modeling instruction in the Rhino3D modeling software through the FISH function of the PFC software. Read the text file in the form of "Tools → Instruction Set → Read from File..." in the Rhino3D modeling software, so as to batch generate a sub-particle geometric model set consistent with PFC3D in the Rhino3D software, and save the model generated by the Rhino3D software in STEP format; import the saved STEP format sub-particle geometric model set into the ANSYS numerical software, and generate a first solid model obtained by combining the solid models of all sub-particles.

[0109] Step S50: acquiring 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 entity model is specifically as follows: importing the particle contour model in step format into ANSYS numerical software, and generating the second entity model through command operation. The second entity model is the entity model of the real particles.

[0111] In the present invention, the Boolean subtraction operation is performed using ANSYS numerical software. Specifically, the entity models of all sub-particles are independently grouped and named. Through the extraction instructions of the Boolean operation provided by the ANSYS software, the second entity model is selected as the first target geometry, the first entity model is selected as the second target geometry, and the part that overlaps with the entity model of the sub-particle is subtracted from the entity model of the real particle (the second entity model), and finally a porous particle model is constructed. When performing the Boolean operation in this step, the entity models of all sub-particles can be selected at one time in the form of grouping to prevent omission.

[0112] In the present invention, the number of holes in the porous particle model is the same as the number of sub-particles, and the pore size distribution of the holes 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 entity model can be precisely controlled, the hole characteristics of the porous particle model can be precisely controlled. That is, on the premise of knowing the hole characteristics of 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.

[0113] See also Fig. 9 , Fig. 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: meshing the porous particle model to construct a breakable porous particle model.

[0116] See also Fig.10 , Fig.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 transformed into an aggregate composed of numerous tetrahedrons.

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

[0120] ANSYS provides a variety of meshing standards, which can be used to mesh the model as needed, and achieve high-quality meshing while ensuring calculation accuracy and efficiency. After the meshing is completed, the model is saved in a text-readable format, and can be imported into other software through the interface program between the software and other software (such as discrete element software PFC or finite difference software FLAC3D, etc.) to simulate particle crushing and explore the influence of pore factors on porous particle crushing.

[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. Fig.11 .

[0123] It should be noted that Fig.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 constructing Fig.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, and R is 1 / 2 of the aperture.

[0125] See also Fig.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, the first acquisition module is used to construct a particle contour model based on the selected real particles, and fill 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 holes 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 holes of the porous particle model;

[0127] A second acquisition module 102, which is used 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 extension operations on the plurality of target sub-particles according to preset rules to obtain a second particle model;

[0128] A third acquisition module 103, which is used 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;

[0129] A fourth acquisition module 104, wherein the third acquisition module is used 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 acquire 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 comprises a second construction module 106, and the second construction module is used for performing mesh 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 constructing the device described above can refer to the contents in the aforementioned method embodiment and will not be repeated here.

[0133] The present invention also provides a computer device, Fig.12 The construction device shown can be arranged in the computer device. The computer device includes a memory, a processor, a communication interface and a bus. The memory, the processor and the communication interface are connected to each other through the bus. In addition, the computer device can include multiple processors so that the functions of the above-mentioned different modules can be realized by different processors.

[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 sequences. 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 embodiment 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, WINDOWSTM, 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. In the implementation process, some or all of the functions of the method for constructing the porous particle model of the present application can be completed by the hardware integrated logic circuit or software instructions in the processor. The above-mentioned processor can also be a general-purpose processor, a digital signal processor (digitalsignal processing, DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (field programmablegate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The 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, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a 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] The communication interface uses a transceiver module such as, but not limited to, a transceiver to realize communication between the computer device and other devices or a communication network. For example, the communication interface can be any one or any combination of the following devices: a network interface (such as an Ethernet interface), a wireless network card, and other devices with network access functions.

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

[0139] A communication path is established between each of the above-mentioned computer devices 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 an embodiment of the present application. Any computer device can be a computer device in a cloud data center (e.g., a server), or a computer device in an edge data center, etc.

[0140] The descriptions of the processes corresponding to the above-mentioned figures have different emphases. 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 by software, hardware, firmware, or any combination thereof. When implemented by 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, and 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 embodiment of the present application is implemented in whole or in part.

[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 wired (e.g., coaxial cable, optical fiber, digital subscriber line, or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium stores computer program instructions for providing data synchronization cloud services.

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

[0144] The 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] A person 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 instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0146] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope 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 holes 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 holes of the porous particle model; Step S20, traversing all sub-particles in the first particle model, determining a plurality of target sub-particles in contact with the particle contour model, and subjecting the plurality of target sub-particles to an extension operation according to a preset rule, respectively, to obtain a second particle model; Step S30, subjecting the second particle model to static equilibrium, 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 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; Step S40: acquiring 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; 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, characterized in that: The construction method further includes step S60 after step S50, wherein: The step S60 is to mesh the porous particle model 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: taking 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, 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.

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 by using the radius expansion method to obtain a first particle model comprises: 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 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 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 an initial state and the pore size distribution have a second corresponding relationship; Step (2), taking 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; Step (3), calculating the sum V1 of the volumes of all the sub-particles in the filling model, and calculating the ratio of V1 to the volume V2 of the particle contour 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), repeating steps (3) and (4) until the volume ratio corresponding to the updated filling model is greater than the target volume ratio, obtaining 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 a filled 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 comprises: Randomly selecting real particles, where the real particles are one of gravel, pebble and rock particles; Acquire the 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, characterized in that: The step S40 comprises: 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), based on the data information of each sub-particle, generating a sub-particle geometric model corresponding to each 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 entity model is obtained, wherein the first entity model is obtained by combining entity 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, the first acquisition module is used to construct a particle contour model based on the selected real particles, and fill 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 holes of the porous particle model to be constructed, and the particle size distribution of the plurality of sub-particles in a filling-completed state has a first corresponding relationship with the pore size distribution of the holes of the porous particle model; A second acquisition module, the second acquisition module is used 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 extension operations on the plurality of target sub-particles respectively according to preset rules to acquire a second particle model; a third acquisition module, the third acquisition module being used 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, the fourth acquisition module is used to acquire a first entity model based on the third particle model, 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 real 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 also 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 comprises: 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 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 executes the construction method according to any one of claims 1 to 6.

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