A method for generating a random microstructure model of a soil-rock mixture

Abstract

The application provides a generation method of a random mesoscopic structure model of a soil-rock mixture, and belongs to the field of geotechnical material mesoscopic numerical simulation. Firstly, a three-dimensional mapping grid model is generated according to specific research requirements, and node information and element information of the three-dimensional mapping grid are stored; secondly, the particle size distribution of block stones is determined, and the spatial orientation of the block stones is determined according to a block stone grading curve obtained through a soil-rock mixture screening test, so as to generate a random block stone model; finally, the mesoscopic components of the soil-rock mixture are identified, the generated random block stone model is mapped into an initial homogenization finite element grid model, the mesoscopic components are distinguished, the block stones and the soil matrix are collected, and the properties of the components are determined. The method effectively solves the problems of non-convergence caused by irregular grids and low efficiency of mesoscopic mechanics numerical simulation, and the generated random mesoscopic structure model of the soil-rock mixture can be directly used for mesoscopic numerical simulation in the field of geotechnical engineering.

Description

Technical Field

[0001] This invention belongs to the field of microscopic numerical simulation of soil and rock materials, and specifically relates to a method for generating a stochastic microstructural model of soil-rock mixtures. Background Technology

[0002] Because of the complex structural characteristics of soil-rock mixtures, which are highly heterogeneous, discontinuous, spatially variable and environmentally dependent, their macroscopic and microscopic mechanical properties vary greatly due to differences in the content of internal components, particle shape, spatial distribution and arrangement.

[0003] Due to limitations in indoor testing instruments and conditions, revealing the damage evolution process of internal microstructures using indoor testing methods presents significant challenges. Furthermore, traditional macroscopic analysis methods cannot consider the characteristics of internal microstructures, making it difficult to describe the micromechanics between different components and the influence of microstructure on macroscopic mechanical properties. The development and maturation of microstructure modeling technology has made it possible to study the microstructural mechanical characteristics, macroscopic mechanical behavior, and deformation and failure mechanisms of materials using numerical simulation methods. The rationality and effectiveness of the model are fundamental prerequisites for ensuring the reliability of quantitative analysis results in geotechnical engineering. Existing microstructure models for soil-rock mixtures suffer from convergence issues due to grid irregularities and low efficiency in micromechanical numerical simulation. Summary of the Invention

[0004] To overcome the shortcomings of the existing technology, the present invention provides a method for generating a random microstructure model of soil-rock mixture.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for generating a stochastic microstructure model of a soil-rock mixture includes the following steps:

[0007] The area where the soil-rock mixture is placed is divided into subdivisions, and a grid model is established.

[0008] Obtain the particle size distribution curve of the boulders, and use the particle size distribution curve to calculate the number of boulders of each particle size in the soil-rock mixture with a certain stone content.

[0009] Based on the number of stones of each size, simulated stone particles of multiple gradation segments are generated. All simulated stone particles are combined according to their positions to obtain a stone distribution model.

[0010] The distance between the centroid of each grid cell in the grid model and the sphere of each simulated rock particle in the rock distribution model is calculated, and each simulated rock particle is mapped into the grid model to obtain a stochastic microstructure model of soil-rock mixture.

[0011] Preferably, the particle size distribution curve of the boulders is obtained based on sieve analysis.

[0012] Preferably, the step of generating simulated stone particles with multiple gradations corresponding to the number of stones of each size includes:

[0013] Determine the representative particle size of the stones in each gradation section;

[0014] The proportion of the volume of each gradation segment to the total volume of the total gradation segment is determined based on the gradation curve of the rubble.

[0015] Calculate the total volume of the blocks in the corresponding gradation section based on the proportion of the volume of the blocks in each gradation section to the total volume of the blocks.

[0016] The quantity of blocks in each gradation section is determined based on the total volume of the blocks in each gradation section.

[0017] Simulated stone particles for each gradation segment are randomly generated based on the representative particle size, total volume, and quantity of the stones in each gradation segment.

[0018] Preferably, the graded segment [d] s ,d s+1 The proportion of the volume of the block stone to the total volume of the blocks stone is P[d]. s ,d s+1 ]:

[0019]

[0020] Where, d min For the gradation segment [d s ,d s+1 The minimum diameter of the stones in the block.

[0021] Preferably, the step of randomly generating simulated stone particles for each gradation segment based on the representative particle size, total volume, and quantity of the stones in each gradation segment includes:

[0022] Generate a set of uniformly distributed random variables in the interval [0,1], and map the set of random variables onto the projection area in the form of spatial coordinates;

[0023] Simulated stone particles for each gradation segment are generated based on the order of random variables in a set of random variables and their positions within the distribution area, taking into account the representative particle size, total volume, and quantity of the stones in each gradation segment.

[0024] Preferably, the step of generating simulated stone particles based on the order and spatial coordinates of random variables in a set of random variables, using the representative particle size, total volume, and quantity of each graded stone segment, includes:

[0025] When one of the random variables in a set of random variables overlaps with the coordinates of the already generated simulated stone particles in the spatial coordinates, a new set of uniformly distributed random variables is generated in the interval [0,1]. Based on the order of the random variables in the newly generated set of random variables and their positions in the spatial coordinates, the remaining simulated stone particles for each grade segment are generated according to the representative particle size, total volume, and quantity of the stone particles in each grade segment.

[0026] Preferably, Matlab is used to generate simulated stone particles of multiple gradation segments according to the number of stones of each size. All simulated stone particles are combined according to their positions to obtain a stone distribution model.

[0027] Preferably, the step of obtaining the stochastic microstructure model of the soil-rock mixture includes:

[0028] Assign coordinates to each node of each grid cell in the mesh model; obtain the centroid of each grid cell based on the coordinates of each node of each grid cell;

[0029] The simulated stone particles in each gradation section are assigned values ​​according to their particle size;

[0030] Calculate the distance between the centroid of each grid cell and the center of each simulated stone particle;

[0031] Based on the distance between the centroid of each grid cell and the center of each simulated rock particle, and the composition of each grid cell is determined according to the particle size of the simulated rock particles in each gradation section, a stochastic microstructure model of the soil-rock mixture is obtained.

[0032] Preferably, the step of determining the components of each grid cell includes:

[0033] The simulated stone particles in the gradation section are assigned values ​​num1, num2, ..., num according to their particle size. i ;

[0034] The minimum distance between the center point of the simulated stone particle and the centroid of all grid cells is denoted as dis_min;

[0035] The position of a simulated stone particle corresponding to the minimum distance in a gradation segment is denoted as loc_min;

[0036] If dis_min < 0 and num1 + num2 + ... + num i-1 +1 <loc_min<num1+num2+…+num i-1If +1, the number of the network unit corresponding to the minimum distance is assigned to a graded block stone, and the component in the network unit corresponding to the minimum distance is defined as block stone; if dis_min<0, the component in the network unit corresponding to the minimum distance is defined as soil matrix.

[0037] The method for generating a stochastic microstructure model of soil-rock mixtures provided by this invention has the following beneficial effects:

[0038] 1. This method effectively solves the problems of non-convergence caused by irregular meshes and low efficiency of micromechanical numerical simulation, and can generate a stochastic microstructural model of soil-rock mixtures. 2. This invention uses a mapping method to generate a stochastic microstructural model of soil-rock mixtures, resulting in a regular finite element mesh. 3. This invention obtains the gradation curve of the boulders based on sieve analysis tests, and generates a stochastic model of the boulders based on the gradation curve. The generated model has geometric similarity to the undisturbed soil-rock mixture. 4. This invention generates a stochastic microstructural model of soil-rock mixtures using the Monte Carlo method, satisfying the structural characteristics of soil-rock mixtures and the characteristics of random spatial distribution of each component. Attached Figure Description

[0039] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the method for generating a random microstructure model of a soil-rock mixture according to Embodiment 1 of the present invention;

[0041] Figure 2 This is a schematic diagram of a three-dimensional soil-rock mixture random block model with 50% stone content according to Embodiment 1 of the present invention;

[0042] Figure 3 This is a schematic diagram of the particle size distribution curve of a fault-filled soil-rock mixture “block stone” in a three-dimensional soil-rock mixture random block stone model with 50% stone content in Embodiment 1 of the present invention.

[0043] Figure 4 This is a schematic diagram of the structural composition of a random microstructure model of a soil-rock mixture with a 50% stone content according to Embodiment 1 of the present invention. Figure 4 (a) is a mesh model of a soil-rock mixture with 50% stone content according to Example 1 of the present invention. Figure 4 (b) is the set of soil units for a soil-rock mixture with a 50% stone content according to Example 1 of the present invention. Figure 4 (c) is a collection of block stone units of a soil-rock mixture with a 50% stone content according to Example 1 of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0045] Example 1

[0046] See Figure 1 This invention provides a method for generating a stochastic microstructure model of a soil-rock mixture, specifically including the following steps: dividing the area where the soil-rock mixture is placed into a grid model; obtaining the particle size distribution curve of the boulders through sieving tests, and calculating the number of boulders of each particle size in the soil-rock mixture under a certain stone content using the particle size distribution curve; generating simulated boulder particles of multiple gradation segments according to the number of boulders of each particle size using Matlab, and obtaining a boulder distribution model by combining all the simulated boulder particles according to their positions; calculating and mapping each simulated boulder particle to the grid model according to the distance between the centroid of each grid cell in the grid model and the sphere center of each simulated boulder particle in the boulder distribution model to obtain a stochastic microstructure model of the soil-rock mixture.

[0047] The steps for generating simulated stone particles for multiple gradation segments based on the number of stones of each size include: determining the representative particle size of the stones in each gradation segment; determining the proportion of the stone volume in each gradation segment to the total stone volume based on the stone gradation curve; calculating the total volume of the stones in the corresponding gradation segment based on the proportion of the stone volume in each gradation segment to the total stone volume; determining the number of stones in the corresponding gradation segment based on the total stone volume; and randomly generating simulated stone particles for each gradation segment based on the representative particle size, total volume, and number of stones in each gradation segment.

[0048] Specifically, the gradation segment [d] s ,d s+1 The proportion of the volume of the block stone to the total volume of the blocks stone is P[d]. s ,d s+1 ]:

[0049]

[0050] Where, d min For the gradation segment [d s ,d s+1 The minimum diameter of the stones in the block.

[0051] In this embodiment, the step of randomly generating simulated stone particles for each gradation segment based on the representative particle size, total volume, and quantity of the stones in each gradation segment includes: generating a set of uniformly distributed random variables in the interval [0,1], mapping the set of random variables onto the placement area in the form of spatial coordinates; and generating simulated stone particles for each gradation segment based on the order of the random variables in the set of random variables and their positions in the placement area, using the representative particle size, total volume, and quantity of the stones in each gradation segment.

[0052] The step of generating simulated stone particles based on the order and position of random variables in a set of random variables in spatial coordinates, with respect to the representative particle size, total volume, and quantity of each graded stone segment, includes: when one of the random variables in the set of random variables overlaps with the coordinates of the already generated simulated stone particles in spatial coordinates, a new set of uniformly distributed random variables is generated in the interval [0,1], and the remaining simulated stone particles for each graded segment are generated based on the order and position of the random variables in the newly generated set of random variables in spatial coordinates, with respect to the representative particle size, total volume, and quantity of each graded stone segment.

[0053] In this embodiment, the steps for obtaining the random microstructure model of the soil-rock mixture include: assigning coordinates to each node of each grid cell in the grid model; obtaining the centroid of each grid cell based on the coordinates of each node; assigning values ​​to the simulated rock particles of each gradation segment according to their particle size; calculating the distance between the centroid of each grid cell and the center of each simulated rock particle; and determining the composition of each grid cell based on the distance between the centroid of each grid cell and the center of each simulated rock particle, and the simulated rock particles of each gradation segment according to their particle size, thus obtaining the random microstructure model of the soil-rock mixture.

[0054] Specifically, the steps for determining the components of each grid cell include: assigning the simulated stone particles in the gradation section to values ​​num1, num2, ..., num according to their particle size. i The minimum distance between the center point of the simulated stone particle and the centroid of all grid elements is denoted as dis_min; the position of the simulated stone particle corresponding to the minimum distance in a gradation segment is denoted as loc_min; if dis_min<0 and num1+num2+…+num i-1 +1 <loc_min<num1+num2+…+num i-1 If +1, the number of the network unit corresponding to the minimum distance is assigned to a graded block stone, and the component in the network unit corresponding to the minimum distance is defined as block stone; if dis_min<0, the component in the network unit corresponding to the minimum distance is defined as soil matrix.

[0055] Figure 2A stochastic microstructural model of a soil-rock mixture with a 50% rock content in a fault is presented. The following details the implementation of this invention through the generation process of this stochastic microstructural model. The steps for generating the stochastic microstructural model of the soil-rock mixture with a 50% rock content include:

[0056] First, according to the requirements, the area for placing the stone samples was set as a cylinder with a diameter of 100mm and a height of 200mm. The mesh element size of the mesh model was set to 2mm. The mesh model was then established and meshed according to its specific specifications, resulting in... Figure 4 (a) shows a mesh model of a soil-rock mixture with 50% rock content, storing the node and cell information of the mesh model.

[0057] Secondly, the particle size distribution curve of the boulders was obtained through sieve analysis, such as... Figure 3 As shown, in this embodiment, the particle size is divided into four gradation segments. The particle size of the boulders can be represented by a representative value for each gradation segment. The representative particle sizes for the four gradation segments selected here are 8mm, 15mm, 30mm, and 40mm, respectively. Grading segment [d] s ,d s+1 The proportion of the volume of the block stone to the total volume of the blocks stone is P[d]. s ,d s+1 ]:

[0058]

[0059] Where, d min For the gradation segment [d s ,d s+1 The minimum diameter of the stones in the block.

[0060] via P[d] s ,d s+1 The calculation formula yielded that the proportions of the volume of the four graded stone sections to the total volume of the stone were 0.132, 0.329, 0.327 and 0.212, respectively. Based on this, the number of stones in each graded stone section according to the representative particle size was 2, 11, 94 and 405, respectively.

[0061] Using Matlab, simulated stone particles in four gradation segments were generated based on the number of stones of each size. The stone distribution model was obtained by combining all the simulated stone particles according to their positions. Figure 3 As shown.

[0062] Finally, the microstructure of the soil-rock mixture is identified, and the properties of each component material are determined by the set of boulders and soil matrix to obtain a stochastic microstructure model of the soil-rock mixture. The specific steps include: (1) assigning coordinate values ​​to each node of each grid cell in the grid model; obtaining the centroid of each grid cell based on the coordinate values ​​of each node of each grid cell; (2) assigning values ​​to the simulated boulders in each gradation segment according to their particle size from largest to smallest, as num1, num2, ..., num i , that is, 2, 11, 94 and 405; (3) Calculate the distance between the centroid of each grid unit and the center of each simulated stone particle, and denote the minimum distance between the center of the simulated stone particle and the centroid of all grid units as dis_min, and denote the position of a simulated stone particle corresponding to the minimum distance in a gradation segment as loc_min; (4) Determine the composition of each grid unit according to the distance between the centroid of each grid unit and the center of each simulated stone particle, and the simulated stone particles in each gradation segment according to their particle size. If dis_min<0 and num1+num2+…+numi -1 +1 <loc_min<num1+num2+…+num i-1 If dis_min is increased by 1, the network cell corresponding to the minimum distance is assigned a graded block stone, and the component in the network cell corresponding to the minimum distance is defined as block stone; if dis_min < 0, the component in the network cell corresponding to the minimum distance is defined as soil matrix, thus obtaining the stochastic microstructure model of the soil-rock mixture. See also... Figure 4 (b) shows the set of soil elements for a soil-rock mixture with 50% stone content. See [reference needed] Figure 4 (c) shows the set of block stones in a soil-rock mixture with a 50% stone content.

[0063] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited thereto. Any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the scope of the technology disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for generating a random mesostructure model of a soil-rock mixture, characterized by, The method comprises the following steps: The soil-rock mixture is divided into a grid model; A particle size distribution curve of the rock is obtained, and the number of rocks of each particle size in the soil-rock mixture under a certain rock content is calculated by using the particle size distribution curve; A plurality of simulation rock particles of each grading section are generated according to the number of rocks of each particle size, and a rock distribution model is obtained by combining all the simulation rock particles according to positions; Each simulation rock particle is mapped into the grid model according to the distance between the centroid of each grid unit in the grid model and the spherical center of each simulation rock particle in the rock distribution model to obtain a random microstructure model of the soil-rock mixture, which comprises the following steps: assigning coordinates to each node of each grid unit of the grid model; obtaining the centroid of each grid unit according to the coordinates of each node of each grid unit; The simulation rock particles of each grading section are assigned values according to particle sizes; the distance between the centroid of each grid unit and the spherical center of each simulation rock particle is calculated; the composition of each grid unit is determined according to the distance between the centroid of each grid unit and the spherical center of each simulation rock particle and the simulation rock particles of each grading section according to particle sizes, so as to obtain the random microstructure model of the soil-rock mixture; The step of determining the component of each grid unit comprises: assigning the simulated block stone particles in the grading section to the particle size ; recording the minimum distance between the ball center point of the simulated block stone particle and the center point of all grid units as ; recording the position of the simulated block stone particle corresponding to the minimum distance in the grading section as ; if or and If the minimum distance corresponds to the network unit with the number of 1, then the number of the network unit corresponding to the minimum distance is assigned to a graded section block stone, and the component in the network unit corresponding to the minimum distance is defined as the block stone; if the minimum distance corresponds to the network unit with the number of 2, If the minimum distance corresponds to the network unit with the number of 1, then the number of the network unit corresponding to the minimum distance is assigned to a graded section block stone, and the component in the network unit corresponding to the minimum distance is defined as the block stone; if the minimum distance corresponds to the network unit with the number 2. The method of claim 1, wherein, The particle size distribution curve of the rock is obtained according to a screening test.

3. The method of claim 1, wherein the method further comprises: The step of generating a plurality of simulation rock particles of each grading section according to the number of rocks of each particle size comprises the following steps: determining the representative particle size of the rock of each grading section; determining the proportion of the volume of the rock of each grading section in the total volume of the rock according to the rock size distribution curve; calculating the total volume of the rock of each grading section according to the proportion of the volume of the rock of each grading section in the total volume of the rock; determining the number of the rock of each grading section according to the total volume of the rock of each grading section; randomly generating the simulation rock particles of each grading section according to the representative particle size, the total volume and the number of the rock of each grading section.

4. The method of claim 3, wherein the method further comprises: grading section The ratio of the volume of the block stone of the grading section to the total volume of the block stone is : wherein is the minimum value of the stone diameter in the block stone of the grading section is the minimum value of the stone diameter in the block stone of the grading section 5. The method of claim 3, wherein the method further comprises: The step of randomly generating the simulation rock particles of each grading section according to the representative particle size, the total volume and the number of the rock of each grading section comprises the following steps: generating a group of uniformly distributed random variables on the interval [0, 1], and mapping the group of random variables to the placement area in the form of spatial coordinates; generating the simulation rock particles of each grading section according to the representative particle size, the total volume and the number of the rock of each grading section according to the order of the random variables in the group of random variables and the positions of the random variables in the placement area.

6. The method of claim 5, wherein the method further comprises: The step of generating the simulation rock particles according to the representative particle size, the total volume and the number of the rock of each grading section according to the order of the random variables in the group of random variables and the positions of the random variables on the spatial coordinates comprises the following steps: when a random variable in the group of random variables overlaps with the coordinates of the generated simulation rock particles on the spatial coordinates, a group of uniformly distributed random variables is generated again on the interval [0, 1], and the simulation rock particles of the remaining grading sections are generated according to the representative particle size, the total volume and the number of the rock of each grading section according to the order of the random variables in the group of randomly generated random variables and the positions of the random variables on the spatial coordinates.

7. The method of claim 1, wherein the method further comprises: The simulation rock particles of each grading section are generated according to the number of rocks of each particle size by using Matlab, and the rock distribution model is obtained by combining all the simulation rock particles according to positions.

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