CN110069844B - Mesoscopic model generation method considering rock structure characteristics and mineral composition - Google Patents
Mesoscopic model generation method considering rock structure characteristics and mineral composition Download PDFInfo
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Abstract
The invention discloses a method for generating a mesoscopic numerical model by considering rock structure characteristics and mineral composition. The mesoscopic structural characteristics and the mineral composition of the rock can be considered, a mesoscopic numerical model reflecting the structural characteristics and the mineral composition of the rock is established, and rock mechanics numerical simulation can be carried out more truly. The invention comprises the following steps: performing rock structure characteristic analysis on a rock sample to obtain information such as size, form, mineral type and the like of mineral particles; simplifying the irregular mineral particles into a ball with reduced size, and rearranging the particles by adopting a particle size expansion method to obtain a more compact structure; then extracting information such as the spatial position and size of the particles, and carrying out space region division on the particle assembly based on a Voronoi diagram; and finally, carrying out finite element meshing on the obtained Voronoi polycrystalline structure, and inserting a non-thickness interface unit into the boundary of the mineral particles and the interior of the particles to generate a finite element numerical model capable of reflecting the rock structure and the lithofacies structure.
Description
Technical Field
The invention belongs to the field of rock mechanics research, and particularly relates to a mesoscopic numerical model generation method considering rock structure characteristics and mineral composition.
Background
Rock is the basic component of the crust, largely exposed on the earth surface, constituting the mountain canyon, which is the basic carrier and environment for ergonomic activities. Rock is a solid aggregate composed of one or more minerals with a stable shape, and the minerals have a certain chemical composition and a certain crystal structure. Rock structure refers to the characteristics exhibited by the degree of crystallinity of the materials making up the rock, the size of the mineral particles, the relative size of the mineral particles, the shape of the mineral particles, and the interrelationships between them. The structure can be divided into three categories of full-crystalline structure, semi-crystalline structure and vitreous structure according to the crystallization degree of the rock; the structure of grades such as coarse grains, medium grains, fine grains, micro grains and the like can be separated according to the absolute size of mineral particles in the rock; according to the relative size of mineral particles, the mineral particles can be divided into an equal-grain structure, an unequal-grain structure, a spot-shaped structure and a spot-like structure; according to the self-forming degree of mineral in rock, it can also be divided into self-forming structure, semi-self-forming structure and other form structure.
During the study of the mechanical properties of rock, the size, shape, degree of interlocking and type of contact of mineral particles have different effects. From the aspect of mineral particle size, in magma rock, metamorphic rock and sedimentary rock, the strength of an isograined structure is generally higher than that of a non-isograined structure; in an isograin structure, the fine grain structure is stronger than the coarse grain structure. In a spot-like structure, the fine-grained matrix is stronger than the glass matrix; the coarse grains have speckled crystals and the strength of the acidic deep diagenetic rock is lowest; the strength of the matrix extrusive rock is highest with fine crystallites and no glass. From the structural connection, most chemical rocks in magma rocks, metamorphic rocks and sedimentary rocks are tightly combined among crystal grains and have higher strength, but in the chemical rocks, the crystal grains are connected by soluble crystals, the strength is higher, but the water resistance is poor. A portion of the consolidated claystone is a recrystallized bond, which is much weaker than other hard rocks.
Many scholars at home and abroad carry out a great deal of research on the relationship between the macroscopic deformation and strength characteristics of the rock and the rock structure.
In 2013, Coggan et al studied the mechanical properties of granite in southwest of England, and thought that the increase of kaolin petrochemicals is the main reason for the significant decrease of the strength of granite in the area; and simultaneously Sousa et al also carry out comparative analysis on granite in different areas of the portuguese, and think that the texture characteristics of quartz in the rock have important influence on the mechanical behavior of the rock.
In 2014, Sajid and Arif et al investigated granite in northern pakistan and found that changes in the lithofacies inside the rock would result in a decrease in the strength of the fine grained granite and also in an increase in the water absorption of the rock.
In recent years, advanced observation technologies, such as acoustic emission, scanning electron microscope, X-ray tomography, digital image correlation, etc., are introduced into rock mechanics tests to study the physical processes of nucleation, expansion and confluence of microcracks of quasi-brittle materials such as rocks under the action of load. Because the mineral components and the crystal composition in the rock are very complex, the mechanical properties of the rock cannot be well revealed through indoor tests. In order to understand the influence of the rock microstructure on the mechanical properties thereof, researchers have begun to perform numerical simulation considering the rock microstructure and build a corresponding micro model to reflect the structural characteristics of the rock.
In 2014, Ghazvinian et al proposed a three-dimensional random Voronoi grain model for simulating crack propagation in brittle rocks, obtained by Voronoi tessellation, and simulated intergranular cracks with crystal boundaries.
In 2017, Peng et al proposed a grain model that simulates the micro-cracking behavior of Wuji-know granite, and simulated the microstructure of crystalline rock by constructing polygonal grains.
However, the above method does not completely reflect the rock microstructure, only mineral components and mineral contents of the rock are considered, and irregular shapes and arrangement structures of rock mineral particles are not considered, so that a microscopic numerical model considering rock structural characteristics and mineral composition needs to be established.
Disclosure of Invention
The present invention is designed to solve the above-mentioned disadvantages, and an object of the present invention is to provide a method for generating a mesoscopic numerical model that can reflect rock structural characteristics and mineral composition. Based on the analysis results of rock structural features, such as rock mineral particle content, mineral particle size distribution, grain size, particle shape and spatial position of grains, the influence of the rock microstructure on the macroscopic mechanical properties of the rock microstructure is reflected more truly.
In order to achieve the above object, the present invention adopts the following aspects.
A method of generating a mesoscopic numerical model taking into account structural features of rock and mineral composition, comprising the steps of:
step 1, obtaining mineral particle distribution and particle geometric information through rock structure characteristic analysis:
collecting and summarizing mineral particle distribution and particle geometric information obtained by analyzing rock structural features in the step 1, simplifying irregular-shaped mineral particles into round balls with reduced sizes, simulating the process of gradual expansion of particle sizes in a rock sample by adopting a discrete unit method, and stopping particle size expansion and discrete element numerical simulation until a particle system and a real rock sample have the same structural characteristics, namely whether the particle sizes reach the mineral particle size distribution or not, so as to complete rock sample modeling;
extracting spatial position and size information of particles in the rock sample model, and carrying out space region division on the particle aggregate based on a Voronoi diagram, wherein each Voronoi cell corresponds to an irregular-shaped mineral grain; assigning a mineral type to the mineral grain represented by each Voronoi cell according to the volume fraction of different mineral types in the rock;
and carrying out finite element mesh division on the obtained Voronoi polycrystalline structure, inserting a non-thickness interface unit into the boundary of the mineral particles and the interior of the particles, generating a finite element numerical model capable of reflecting the rock structure and the lithofacies structure, and finishing the generation of the mesoscopic numerical model.
Preferably, in step 1, the concrete method for analyzing the rock structure characteristics comprises the following steps:
firstly, slicing a rock, obtaining a rock sample slice image through microscopic observation, then carrying out digital image processing on the rock sample slice image, segmenting mineral particles from a binary image by adopting a watershed algorithm, and identifying the distribution of the mineral particles and the particle geometric information.
Preferably, in step 1, the mineral particle distribution information includes mineral types of the mineral particles, contents of different mineral types, particle sizes, particle size distributions, centroid positions, circularities, and areas of the particles, and the particle geometry information includes sizes, solidities, ratios of major and minor axes, and circularities of the mineral particles.
Preferably, the roundness is defined by the following formula:
in formula I, SgrainIs the area of mineral particles, P, obtained by structural feature analysis of the rockgrainIs the perimeter of the mineral particle and pi is the circumference ratio.
Preferably, in the step 2, in the process of simulating gradual expansion of the particle size of the particles in the rock sample by adopting a discrete unit method, the particles start to expand in volume at the same rate in a given boundary, in the expansion process, the particles are rearranged in the growth process and tend to be in a compact and isotropic structure under the constraint of the boundary of the sample and the condition that the particles are not mutually invaded, the structural characteristics of the particle aggregate of the rock sample simulated by adopting the discrete unit method can adopt a radial distribution function and key orientation sequence parameters, whether the particle size of the particles reaches the size distribution of the mineral particles is judged by the two parameters, and if the particle size does not meet the requirement, the particle size expansion is continued; discrete element numerical simulation is stopped when the requirements are met.
Preferably, the radial distribution function is:
in the second formula, dn (r) indicates the number of particles which are at a distance of r from the original point particles and can be found in a width dr range, N is the number of total mineral particles in the rock sample, V is the volume modeled by the rock sample, and the physical meaning of g (r) is the number density of particles in unit volume at a distance of r from the original point particles, so that the structural information of a particle system is obtained, and pi is a circumferential rate.
Preferably, the key orientation parameter function is:
in the formula III, the first step is carried out,
is a spherical harmonic function, wherein l represents a sequence parameter of symmetry, m is equal to or more than l and equal to or less than l,
i.e. the direction vector of the particle contact pair, and
and
polar and azimuthal angles of the vector of directions, pi being the circumferential ratio, QlIs a bond orientation sequence parameter value.
Preferably, the step 3 of performing Voronoi diagram-based spatial region division on the particle assembly refers to a technology of dividing the particle assembly in a three-dimensional space, and for D epsilon R3In the region (A), R means the length dimension, R3Indicating that there are a plurality of seed points in a given D in a three-dimensional space, the coordinates of the seed points in D being obtained by a particle size expansion method, and for the seed point i, { S } is satisfiedi(xi) Each seed point is assigned a Voronoi cell C ═ 1, …, N }iIn which C isiAnd the definition is shown as the formula IV:
in the fourth formula, d (P, S)j) Representing the euclidean distance.
Preferably, in step 4, inserting the interface unit without thickness means: after the rock sample is divided into the finite element grids, interface units adopting common node linkage are newly built among the entity units of the grids, the interface units have no thickness and do not influence the geometric characteristics of the rock sample, and when the stress state of the interface units meets the fracture criterion, the interface units fail and are deleted from the model, so that the microscopic fracture of the rock can be simulated.
The invention has the beneficial effects that:
(1) the invention provides a method for generating a microscopic numerical model by considering rock structural characteristics and mineral composition, which not only considers the mineral composition and anisotropy in the rock, but also considers the mineral particle content, mineral particle size distribution, grain size, particle shape and spatial position of grains in the rock, and can simulate the microstructure in the rock more accurately.
(2) The method provided by the invention can generate a numerical model of finite element calculation, and can adopt large commercial finite element calculation software to carry out simulation of mechanical behavior, such as uniaxial compression test, triaxial compression test, uniaxial tension test, three-point bending test and the like.
(3) The method provided by the invention has the advantages of higher calculation efficiency and good universality.
Drawings
FIG. 1 is a schematic diagram of a numerical model generation method involved in an embodiment of the present invention;
FIG. 2 is a partial schematic illustration of a rock structure feature analysis involved in an embodiment of the invention;
in the figure, 11 represents a rock sample slice, 12 represents slice microscopic observation, 13 represents mineral grain boundary identification, 14 represents grain geometric information extraction, and 15 represents grain geometric information calculation;
FIG. 3 is a schematic diagram of the distribution rule of mineral particle sizes obtained by rock structural feature analysis in the embodiment of the invention;
FIG. 4 is a schematic diagram of a process for simulating gradual expansion of particle sizes in a rock sample by using a discrete cell method according to an embodiment of the invention; wherein, 24 represents the schematic diagram of the initial stage of the ball throwing, 25 represents the schematic diagram of the middle stage of the ball expansion, and 26 represents the schematic diagram of the completion of the ball expansion and the achievement of the dense distribution;
FIG. 5 is a radial distribution function of a statistical spherical ball stacking system during expansion in an embodiment of the invention;
FIG. 6 is a schematic diagram of key orientation parameters for a statistical sphere stacking system during expansion in an embodiment of the invention;
fig. 7 is a schematic diagram of a space region division method of a Voronoi diagram involved in the embodiment of the present invention;
wherein 31 represents the obtained space position and size distribution schematic diagram of the sphere, and 32 represents the generation of a numerical model schematic diagram by carrying out space region division based on a Voronoi diagram;
FIG. 8 is a schematic diagram of a method of generating a finite element model according to an embodiment of the present invention;
wherein 32 represents a numerical model diagram generated in the embodiment of the invention, 41 represents a grid model diagram obtained by performing grid division on the numerical model, and 42 represents an interface unit diagram of inserting zero thickness into the boundary of the mineral particles;
FIG. 9 is a flow chart of an algorithm in an embodiment of the present invention.
Detailed Description
A detailed explanation of a method for generating a mesoscopic numerical model considering rock structural characteristics and mineral composition according to the present invention will be given below with reference to the accompanying drawings. The details which are not described in detail in the following examples are known in the art.
< example >
As shown in fig. 1, the method for generating a mesoscopic numerical model considering rock structural characteristics and mineral composition according to the present invention can be divided into 4 processes: rock structural feature analysis 1, particle size expansion 2, Voronoi diagram based spatial region partitioning 3 and generation of finite element models 4.
As shown in fig. 1 and 2, the rock structure feature analysis 1 may specifically include: rock sample slicing 11, slice microscopic observation 12, mineral grain boundary identification 13, grain geometry information extraction 14, and grain geometry information calculation 15. In this embodiment, the rock sample slice 11 is obtained by taking a granite slice with a diameter of 5cm × a height of 10cm, an enlarged picture of the slice is obtained under an optical microscope, an open source software ImageJ is used for boundary identification to obtain a boundary profile, and the calculation 15 of the geometric information of the particles includes: the size of the mineral particles (shown in formula five), the solidity of the mineral particles (shown in formula six), the ratio of the major and minor axes of the mineral particles (shown in formula seven), and the roundness of the mineral particles (shown in formula eight).
In the fifth formula, PgrainRepresenting the perimeter of the mineral particle, pi represents the circumference ratio, and R represents the size of the mineral particle.
Formula (II)Six, SgrainDenotes the area of the mineral particles, SconvexThe area of the convex polygon is shown when the mineral particle is surrounded by the convex polygon having the smallest area, and the Grain uniformity indicates the Solidity of the mineral particle.
In the formula VII, LMajorIndicating the major axis length, L, of the ellipse when fitting the boundary of the mineral grain with the ellipseMinorI.e. the minor axis length of the ellipse, Aspect ratio represents the ratio of the major and minor axes of the mineral particles.
In the formula VIII, SgrainDenotes the area of the mineral particles, LMajorThe ellipse is shown as long axis length, pi is circumference ratio, and gain round is the Roundness of the mineral Grain when the ellipse is fitted to the boundary of the mineral Grain.
As shown in fig. 1, 3, 4, 5 and 6, after a mineral particle size distribution rule 21 is obtained through rock structural feature analysis, open source software liggg ts is adopted to put round balls 23 in a cylindrical boundary 22 according to the size distribution rule, the round balls present a random distribution rule at the initial stage of putting 24, and then the round balls start to expand at the same rate, the distribution rule at the middle stage of expansion 25 shows that the stacking of the round balls gradually starts to be compact, and finally a relatively compact distribution 26 is achieved, and a radial distribution function 27 and key sequence parameters 28 of a round ball stacking system are counted in the expansion process, and the two parameters can measure whether the whole system meets the size distribution of mineral particles in a real rock sample. In this example, it is observed whether the peak of the radial distribution function 27 representing the curve of the radial distribution function after the particle size expansion (blue curve) coincides with the first peak of the curve of the radial distribution function representing the real rock pattern (black curve), the first peak in the radial distribution function representing the degree of order of the whole particle system, the coincidence of the first peaks indicates that the particle aggregates generated by the numerical method coincide with the mineral particle aggregates in the real rock in the degree of arrangement regularity; it was also observed whether the key orientation parameter (black dispersion point) of the key orientation parameters 28 after particle size expansion was close to the intersection of HCP (body centered cubic array structure) and FCC (face centered cubic array structure). When the above parameters are satisfied, the process of particle size expansion is completed.
As shown in fig. 1 and 7, the obtained spatial position and size distribution 31 of the spherical ball are extracted, and then a numerical model 32 is generated by performing spatial region division based on a Voronoi (thieson polygon) diagram.
As shown in fig. 1 and 8, in order to facilitate subsequent mechanical property simulation, the method divides the generated numerical model 32 into mesh models 41 for finite element software (such as ANSYS software) calculation, wherein each mineral particle 45 is discretized into about 100 tetrahedral units, and interface units 42 with zero thickness are inserted into the boundaries of the mineral particles in order to ensure the calculation accuracy, and interface units 44 are inserted into the mineral particles in order to simulate the cracking condition inside the mineral particles.
As shown in fig. 9, the method comprises the steps of firstly observing a rock sample slice through a microscope, carrying out digital image processing on the rock sample slice, segmenting mineral particles from a binarized image by using a watershed algorithm, and further obtaining information such as the size, the shape, the mineral type and the like of the mineral particles; the method is characterized in that mineral particles in irregular shapes are simplified into a ball with reduced size, a discrete unit method is adopted to simulate the process that the particle size of particles in a rock sample gradually expands, the particles are rearranged in the growth process under the constraint of sample boundary limitation and the condition that the particles are not invaded into each other, and the structure tends to be compact and isotropic; judging whether the particle size of the particles reaches the size distribution of mineral particles, and continuing to expand the particle size if the particle size does not meet the requirement; stopping discrete element numerical simulation when the requirements are met, extracting information such as spatial position and size of particles, and carrying out Voronoi diagram-based spatial region division on the particle assembly, wherein each Voronoi cell corresponds to an irregular-shaped mineral grain; assigning a mineral type to the mineral grain represented by each Voronoi cell according to the volume fraction of different mineral types in the rock; and carrying out finite element meshing on the obtained Voronoi polycrystalline structure, and inserting a non-thickness interface unit into the boundary of the mineral particles and the interior of the particles to generate a finite element numerical model capable of reflecting the rock structure and the lithofacies structure.
The above embodiments are merely illustrative of the technical solutions of the present invention. The method for generating the microscopic numerical model of the polycrystalline rock according to the present invention is not limited to the process described in the above embodiments, but is subject to the scope defined by the claims. Any modification, or addition, or equivalent replacement by a person skilled in the art on the basis of this embodiment is within the scope of the invention as claimed.
Claims (8)
1. A method of generating a mesoscopic numerical model taking into account structural features of rock and mineral composition, comprising the steps of:
step 1, obtaining mineral particle distribution and particle geometric information through rock structure characteristic analysis:
step 2, discrete element simulation;
collecting and summarizing mineral particle distribution and particle geometric information obtained by analyzing rock structural features in the step 1, simplifying irregular-shaped mineral particles into round balls with reduced sizes, simulating the process of gradual expansion of particle sizes in a rock sample by adopting a discrete unit method, and stopping particle size expansion and discrete element numerical simulation until a particle system and a real rock sample have the same structural characteristics, namely whether the particle sizes reach the mineral particle size distribution or not, so as to complete rock sample modeling;
step 3, generating a numerical simulation;
extracting spatial position and size information of particles in the rock sample model, and carrying out space region division on the particle aggregate based on a Voronoi diagram, wherein each Voronoi cell corresponds to an irregular-shaped mineral grain; assigning a mineral type to the mineral grain represented by each Voronoi cell according to the volume fraction of different mineral types in the rock;
step 4, generating a finite element model;
carrying out finite element mesh division on the obtained Voronoi polycrystalline structure, inserting a non-thickness interface unit into the boundary of mineral particles and the interior of the particles, generating a finite element numerical model capable of reflecting a rock structure and a lithofacies structure, and finishing the generation of a mesoscopic numerical model;
in the step 2, in the process of simulating gradual expansion of the particle size of particles in a rock sample by adopting a discrete unit method, the particles start to expand in volume at the same rate in a given boundary, in the expansion process, the particles are rearranged in the growth process and tend to be a compact and isotropic structure under the restriction of the boundary of the sample and the condition that the particles are not mutually invaded, the structural characteristics of the particle aggregate of the rock sample simulated by adopting the discrete unit method can adopt a radial distribution function and key orientation sequence parameters, whether the particle size of the particles reaches the size distribution of mineral particles is judged by the two parameters, and if the particle size does not meet the requirements, the particle size expansion is continued; discrete element numerical simulation is stopped when the requirements are met.
2. The mesoscopic numerical model generating method according to claim 1, characterized in that: in the step 1, the concrete method for analyzing the rock structure characteristics comprises the following steps:
firstly, slicing a rock, obtaining a rock sample slice image through microscopic observation, then carrying out digital image processing on the rock sample slice image, segmenting mineral particles from a binary image by adopting a watershed algorithm, and identifying the distribution of the mineral particles and the particle geometric information.
3. The mesoscopic numerical model generating method according to claim 2, characterized in that: in step 1, the mineral particle distribution information includes mineral types of mineral particles, contents of different mineral types, particle sizes, particle size distribution, centroid positions, roundness and areas of the particles, and the particle geometric information includes sizes, solidities, ratios of major and minor axes and roundness of the mineral particles.
4. The mesoscopic numerical model generating method according to claim 3, characterized in that: the roundness is defined by the following formula:
in formula I, SgrainIs the area of mineral particles, P, obtained by structural feature analysis of the rockgrainIs the perimeter of the mineral particle and pi is the circumference ratio.
5. The mesoscopic numerical model generating method according to claim 1, characterized in that: the radial distribution function is:
in the second formula, dn (r) indicates the number of particles which are at a distance of r from the original point particles and can be found in a width dr range, N is the number of total mineral particles in the rock sample, V is the volume modeled by the rock sample, and the physical meaning of g (r) is the number density of particles in unit volume at a distance of r from the original point particles, so that the structural information of a particle system is obtained, and pi is a circumferential rate.
6. The mesoscopic numerical model generating method according to claim 1, characterized in that: the key orientation sequence parameter function is:
in the formula III, the first step is carried out,
is a spherical harmonic function, wherein l represents a sequence parameter of symmetry, m is equal to or more than l and equal to or less than l,
i.e. the direction vector of the particle contact pair, and
and
polar and azimuthal angles of the vector of directions, pi being the circumferential ratio, QlIs a bond orientation sequence parameter value.
7. The mesoscopic numerical model generating method according to claim 1, characterized in that: in step 3, the Voronoi diagram-based space region division of the particle assembly refers to a technology of dividing in a three-dimensional space, and for D e R3In the region (A), R means the length dimension, R3Indicating that there are a plurality of seed points in a given D in a three-dimensional space, the coordinates of the seed points in D being obtained by a particle size expansion method, and for the seed point i, { S } is satisfiedi(xi) Each seed point is assigned a Voronoi cell C ═ 1, …, N }iIn which C isiThe definition is shown in formula four:
in the fourth formula, d (P, S)j) Representing the euclidean distance.
8. The mesoscopic numerical model generating method according to claim 1, characterized in that: in step 4, inserting interface units without thickness means: after the rock sample is divided into the finite element grids, interface units adopting common node linkage are newly built among the entity units of the grids, the interface units have no thickness and do not influence the geometric characteristics of the rock sample, and when the stress state of the interface units meets the fracture criterion, the interface units fail and are deleted from the model, so that the microscopic fracture of the rock can be simulated.
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Family Cites Families (5)
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| CN106294984B (en) * | 2016-08-08 | 2019-03-22 | 中国科学院武汉岩土力学研究所 | A method of Rock Damage threshold is determined based on micro-mechanical model growth rate |
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