A delamination microcrack analysis system based on material point method

Abstract

The present invention relates to the technical field of delamination microcrack analysis and provides a delamination microcrack analysis system based on a material point method. First, a data acquisition module is used to obtain stress, strain, displacement, and acoustic emission signal data from a carbonaceous shale sample; a preprocessing module preprocesses the collected data to ensure the accuracy of subsequent analysis; a material point modeling module constructs a discrete material point model based on the processed data to simulate the mechanical behavior of the carbonaceous shale sample; a microcrack identification module is responsible for analyzing the crack characteristics in the material point model and identifying the geometric morphology and spatial distribution of microcracks; a crack evolution analysis module further improves the crack network model by calculating the crack expansion and convergence process; a delamination determination module evaluates whether the sample has delamination based on the evolution characteristics of the crack network and the changes in damage degree and complexity; finally, a result output module summarizes and outputs the various analysis result data. Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Description

Technical Field

[0001] The present invention relates to the technical field of separation layer microcrack analysis, and more particularly to a separation layer microcrack analysis system based on a material point method. Background Art

[0002] Carbonaceous shale, a sedimentary rock rich in organic matter, has garnered widespread attention in recent years due to its high carbon content and abundant reserves of shale gas and oil. Carbonaceous shale exhibits unique physical and mechanical properties. During the mining process, external stresses can induce microcracks within the rock. As these microcracks expand and converge, the integrity of the material can be severely compromised, even leading to delamination failure. Therefore, in-depth research on delamination microcracks in carbonaceous shale is of great practical significance for efficient resource extraction and the prevention of engineering disasters. Accurately analyzing the evolution of these microcracks can effectively predict the mechanical behavior of shale, ensuring production safety and improving resource recovery.

[0003] At present, the commonly used microcrack analysis methods are mainly based on numerical simulation technologies such as the finite element method (FEM) or the extended finite element method (XFEM). These methods help researchers predict the crack propagation path and the final failure mode of the material by modeling and analyzing the stress and strain of the material. In particular, the XFEM technology is widely used in the fracture mechanics research of various composite materials and rock structures because it can handle crack propagation problems without relying on mesh reconstruction. However, the traditional finite element method and XFEM have certain limitations in dealing with the evolution analysis of complex microcrack networks, and it is difficult to fully capture the geometric characteristics of microcrack propagation and its impact on the overall performance of the material.

[0004] For example, the paper "Extended finite element method (XFEM) analysis of fiber-reinforced composites for prediction of micro-crack propagation and delaminations in progressive damage" systematically studies the microcrack propagation and delamination of fiber-reinforced composites based on XFEM. This study demonstrates how to use XFEM to simulate microcrack propagation and delamination in two-dimensional and three-dimensional composites, demonstrating the effectiveness of XFEM technology in predicting and analyzing microcracks in diverse materials. The results demonstrate that XFEM can effectively capture the crack propagation path and delamination process, providing a powerful tool for microcrack analysis.

[0005] However, some existing studies still have limitations in the detailed treatment of microcrack identification and dynamic evolution. For example, in the paper "Characterization methods of delamination in a plain woven CFRP composite," Liu et al. proposed a method for analyzing the delamination behavior of plain carbon fiber reinforced polymer composites. While this method meticulously analyzes the delamination phenomenon using a linear elastic fracture mechanics model and compares experimental results with numerical simulations under different crack modes, demonstrating its potential for delamination assessment, the study lacks the ability to identify and analyze microcrack evolution trends. Specifically, the method in the paper relies too heavily on the analysis of macroscopic crack propagation, while considering only limited key parameters such as the initial formation mechanism of microcracks, crack convergence conditions, and the complexity of the crack network. Furthermore, the study lacks a comprehensive assessment of crack evolution under dynamic stress conditions and its impact on the overall material properties. In particular, there is still considerable room for improvement in the extraction of key indicators for crack evolution in determining delamination.

[0006] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a delamination microcrack analysis system based on the material point method, including data acquisition, preprocessing, material point modeling, microcrack identification, crack evolution analysis, delamination judgment and result output modules; by combining the material point method with the microcrack evolution characteristics, the crack expansion and delamination behavior of the carbonaceous shale sample are accurately simulated, a fracture network model is constructed, and delamination judgment is performed through characteristic vectors and delamination index evaluation values, which solves the problem of insufficient microcrack identification and dynamic evolution analysis in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A delamination microcrack analysis system based on a material point method includes a data acquisition module, a preprocessing module, a material point modeling module, a microcrack identification module, a crack evolution analysis module, a delamination determination module, and a result output module, wherein the data acquisition module is connected to the preprocessing module, the preprocessing module is connected to the material point modeling module, the material point modeling module is connected to the microcrack identification module and the crack evolution analysis module, the microcrack identification module is bidirectionally connected to the crack evolution analysis module, the crack evolution analysis module is connected to the delamination determination module, and the delamination determination module is connected to the result output module;

[0010] The material point modeling module is used to convert the preprocessed data into a discrete material point model, combine the stress, strain, displacement and acoustic emission signal data, calculate the energy density of the material point through the acoustic emission signal, and establish the interaction relationship between the material points to form a complete material point model;

[0011] The crack evolution analysis module is used to analyze the expansion, convergence and penetration process of microcracks. By calculating the stress intensity factor and geometric characteristics of the microcrack tips, the direction and length of crack expansion are determined, and whether adjacent microcracks meet the convergence conditions is analyzed.

[0012] The delamination determination module is used to determine whether delamination has occurred in the carbonaceous shale sample based on the evolution characteristics of microcracks. By extracting the number, average length, average width, and average orientation angle of microcracks, and combining the material point damage change rate and the crack network complexity growth rate, a characteristic vector is constructed. The geometric characteristics and evolution trend of microcracks are evaluated for the impact on delamination. The change in the mechanical properties of the carbonaceous shale sample during the damage accumulation process is considered to obtain the delamination index evaluation value and the dynamic critical delamination index evaluation value. The formula for obtaining the delamination index evaluation value is:

[0013]

[0014] Where DI is the delamination index evaluation value, N is the number of microcracks, L is the average length, W is the average width, θ is the average direction angle, ΔD is the average damage change rate of the material point, ΔC is the growth rate of the crack network complexity, and α and β are adjustable weight coefficients;

[0015] The formula for calculating the dynamic critical index evaluation value is:

[0016] DI c =DI c0 ·(1-γ·S r );

[0017] Where, DI c is the dynamic critical delamination index evaluation value, DI c0 is the initial critical delamination index of carbonaceous shale sample, S r is the stiffness reduction ratio of the carbonaceous shale sample, and γ is the influence coefficient.

[0018] As a further solution of the present invention, the data acquisition module includes a stress sensor, a strain sensor, a displacement sensor and an acoustic emission sensor; wherein the stress sensor is used to measure the axial stress and lateral stress of the carbonaceous shale sample, the strain sensor is used to measure the axial strain and lateral strain of the carbonaceous shale sample, the displacement sensor is used to measure the axial displacement and radial displacement of the carbonaceous shale sample, and the acoustic emission sensor is used to detect the elastic waves released when microcracks inside the carbonaceous shale sample are generated and expanded; the data acquisition module also includes a data acquisition card for converting the analog signals collected by each sensor into digital signals; the preprocessing module is used to perform noise reduction, filtering and normalization on the digital signals collected by the data acquisition module.

[0019] As a further solution of the present invention, the material point modeling module discretizes the continuum into discrete material points using a material point method and simulates the mechanical behavior of the carbonaceous shale sample by establishing an interaction relationship between the material points, including the following steps:

[0020] Step A1: receiving stress, strain, displacement, and acoustic emission signal data transmitted by the preprocessing module; dividing the carbonaceous shale sample into uniformly distributed hexahedral grid cells according to the geometric dimensions of the carbonaceous shale sample and a preset grid resolution; generating a predetermined number of material points in each grid cell, and assigning initial coordinates to each material point;

[0021] Step A2: Calculate the mass and volume of each material point based on the stress and strain data; calculate the initial velocity of each material point using the displacement data; and calculate the energy density of each material point based on the acoustic emission signal data.

[0022] Step A3: Based on the carbonaceous shale type and experimental data, an elastoplastic damage constitutive model is selected, and elastic parameters, strength parameters, and damage parameters are set to define an anisotropic damage evolution equation; the interaction relationship between material points is established, including calculating the distance between material points, interaction force, and stiffness matrix;

[0023] Step A4, setting boundary conditions, including displacement boundary conditions and force boundary conditions, and setting initial conditions, including initial stress field, initial displacement field, and initial velocity field;

[0024] Step A5: construct the mass matrix and stiffness matrix, perform time integration using the explicit central difference method, and set the time step;

[0025] Step A6, solving the equation of motion for each time step and updating the position, velocity, acceleration, stress, and strain of the material point;

[0026] Step A7, calculating the energy density change of the material point at each time step;

[0027] Step A8: Output the material point position, velocity, acceleration, stress, strain, energy density and damage state information at each time step.

[0028] As a further solution of the present invention, the microcrack identification module is used to identify microcracks in a rock sample, comprising the following steps:

[0029] Step B1: receiving material point model data transmitted by the material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; and calculating the stress state of each material point based on the received data;

[0030] Step B2, applying the Mohr-Coulomb strength theory to each material point to determine whether it meets the failure condition;

[0031] Step B3: Connect adjacent failure material points in three-dimensional space to form an initial microcrack network, where the connection condition is set to be that the spatial distance between two failure material points is less than 1.5 mm; perform geometric feature extraction on the formed initial microcrack network to calculate the length, width, and orientation angle of each microcrack;

[0032] Step B4: Based on the extracted geometric features, the microcracks are classified and a complete microcrack network model is constructed to describe the spatial relationship and connectivity between the microcracks. The network model is constructed using graph theory, with each microcrack considered as an edge in the graph and the intersections of microcracks as nodes in the graph. The topological characteristics of the network are calculated, including connectivity, clustering coefficient, and average path length.

[0033] Step B5: Analyze the evolution trend of the microcrack network and calculate the dynamic characteristics of the network, including the growth rate of the number of microcracks, the average length change rate, and the network complexity growth rate. Based on the network model and the evolution analysis results, generate a microcrack distribution density map and use the kernel density estimation method to calculate the microcrack density distribution in three-dimensional space.

[0034] Step B6, calculating the anisotropy index of the microcrack network and quantifying the degree of anisotropy using a second-order structure tensor method;

[0035] Step B7, integrating the analysis results of the previous steps to generate a comprehensive microcrack feature report, including the number of microcracks, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution, and anisotropy index;

[0036] Step B8: Output the generated comprehensive microcrack feature report, microcrack network model and dynamic evolution data to the crack evolution analysis module and the separation layer determination module.

[0037] As a further solution of the present invention, the crack evolution analysis module uses the following steps to analyze the expansion, convergence and penetration of microcracks:

[0038] Step C1, receiving material point model data provided by a material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; and performing standardization processing on the received data;

[0039] Step C2, based on the material point model data and the microcrack network model, the stress intensity factor at each microcrack tip is calculated using the displacement extrapolation method;

[0040] Step C3, applying the maximum circumferential stress criterion to predict the propagation direction of each microcrack; determining the propagation length of the crack in each time step by using the Paris formula;

[0041] Step C4: Analyze the spatial relationship and stress field interaction between adjacent microcracks; set the critical conditions for crack convergence, and use the constructed microcrack network model to evaluate whether each pair of adjacent cracks meets the convergence conditions;

[0042] Step C5: Based on the analysis results of steps C3 and C4, the microcrack network model is updated, and the geometric parameters of the extended cracks are adjusted; and the converged cracks are merged into new crack entities;

[0043] Step C6, analyzing the updated crack network, identifying the crack combination that has penetrated, and calculating the reduction ratio of the specimen stiffness;

[0044] Step C7, calculating the crack density change rate, average crack length growth rate, crack network complexity growth rate, and dominant change value of crack direction to describe the evolution process of the crack network;

[0045] Step C8: Generate a crack evolution analysis report, including the crack propagation path, convergence position, penetration status, and evolution characteristic indicators; and output the report to the separation layer determination module.

[0046] As a further solution of the present invention, the separation determination module calculates the probability of separation occurrence based on the separation index evaluation value and the dynamic critical separation index, and the formula is:

[0047]

[0048] Where P is the probability of separation, DI is the evaluation value of separation index, and DI c is the dynamic critical separation index evaluation value, G is the penetration degree, and δ is the penetration influence coefficient.

[0049] As a further solution of the present invention, the delamination determination module determines whether delamination occurs in the carbonaceous shale sample by comparing the calculated delamination probability with a preset threshold.

[0050] As a further solution of the present invention, the result output module is used to collect and organize the material point position, velocity, stress, strain and damage state provided by the material point modeling module; the number of microcracks, average length, average width and average direction angle provided by the microcrack identification module; the crack extension length, convergence position, penetration condition and crack network complexity growth rate provided by the crack evolution analysis module; and the delamination index evaluation value, dynamic critical delamination index evaluation value and final delamination probability provided by the delamination determination module; and output the conclusion on whether delamination occurs in the carbonaceous shale sample.

[0051] Compared with the prior art, the beneficial effects of the delamination microcrack analysis system based on the material point method of the present invention are:

[0052] 1. The delamination microcrack analysis system based on the material point method of the present invention simulates the microcrack evolution process by discretizing the carbonaceous shale sample into a material point model. It can capture the detailed changes in the expansion, convergence and penetration of microcracks, especially in the analysis of the geometric characteristics, evolution path and spatial distribution of microcracks. It makes up for the limitations of the traditional finite element method (FEM) and extended finite element method (XFEM) in dealing with complex microcrack networks, thereby improving the prediction accuracy of shale mechanical behavior.

[0053] 2. The present invention adopts a method that combines the characteristics of fracture evolution with delamination determination. By extracting multiple indicators such as the number, length, width, and direction of fractures, and combining the degree of material point damage and the rate of change of fracture network complexity, a delamination index assessment model is established. This can more comprehensively and dynamically assess the fracture expansion and delamination risk of carbonaceous shale during the mining process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a structural schematic diagram of a delamination microcrack analysis system based on the material point method of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example 1

[0057] A separation layer microcrack analysis system based on a material point method includes a data acquisition module, a preprocessing module, a material point modeling module, a microcrack identification module, a crack evolution analysis module, a separation layer determination module and a result output module.

[0058] The data acquisition module in the embodiment of the present invention is connected to the preprocessing module, the preprocessing module is connected to the material point modeling module, the material point modeling module is connected to the microcrack identification module and the crack evolution analysis module, the microcrack identification module is bidirectionally connected to the crack evolution analysis module, the crack evolution analysis module is connected to the delamination determination module, and the delamination determination module is connected to the result output module.

[0059] The data acquisition module in the embodiment of the present invention includes a stress sensor, a strain sensor, a displacement sensor and an acoustic emission sensor; among them, the stress sensor is used to measure the axial stress and lateral stress of the carbonaceous shale sample, the strain sensor is used to measure the axial strain and lateral strain of the carbonaceous shale sample, the displacement sensor is used to measure the axial displacement and radial displacement of the carbonaceous shale sample, and the acoustic emission sensor is used to detect the elastic waves released when microcracks inside the carbonaceous shale sample are generated and expanded.

[0060] The data acquisition module in the embodiment of the present invention further includes a data acquisition card for converting the analog signals collected by each sensor into digital signals.

[0061] The preprocessing module in the embodiment of the present invention is used to perform noise reduction processing, filtering processing and normalization processing on the digital signal collected by the data collection module to improve data quality and comparability.

[0062] The material point modeling module is used to convert the preprocessed data into a discrete material point model, combine the stress, strain, displacement and acoustic emission signal data, calculate the energy density of the material point through the acoustic emission signal, and establish the interaction relationship between the material points to form a complete material point model.

[0063] The material point modeling module in the embodiment of the present invention uses the material point method to discretize the continuum into discrete material points, and simulates the mechanical behavior of the carbonaceous shale sample by establishing the interaction relationship between the material points, including the following steps:

[0064] Step A1: receiving stress, strain, displacement, and acoustic emission signal data transmitted by the preprocessing module; dividing the carbonaceous shale sample into uniformly distributed hexahedral grid cells according to the geometric dimensions of the carbonaceous shale sample and a preset grid resolution; generating a predetermined number of material points in each grid cell, and assigning initial coordinates to each material point;

[0065] Step A2: Calculate the mass and volume of each material point based on stress and strain data to achieve the conversion from continuous medium to discrete material points; use displacement data to calculate the initial velocity of each material point to ensure that the motion state of the material point is consistent with the actual carbonaceous shale sample; calculate the energy density of each material point based on acoustic emission signal data to provide energy criteria for subsequent microcrack identification;

[0066] In step A3, based on the carbonaceous shale type and experimental data, an elastoplastic damage constitutive model is selected, and elastic parameters, strength parameters, and damage parameters are set to define an anisotropic damage evolution equation. The interaction relationship between material points is established, including calculating the distance, interaction force, and stiffness matrix between material points. Among them, among the elastic parameters, the elastic modulus is 25 GPa and the Poisson's ratio is 0.23; among the strength parameters, the cohesion is 15 MPa and the friction angle is 28°; among the damage parameters, the damage initiation strain is 0.0008 and the critical damage strain is 0.008. The expression of the anisotropic damage evolution equation is:

[0067]

[0068] Where D1 represents the damage variable in the first principal direction, β1 is the material constant in this direction, ε1 is the strain in the first principal direction, and ε d0 is the damage initiation strain, ε f is the critical damage strain.

[0069]

[0070] Where D2 represents the damage variable in the second principal direction, β2 is the material constant in this direction, ε2 is the strain in the second principal direction, and ε d0 is the damage initiation strain, ε f is the critical damage strain.

[0071]

[0072] Where D3 represents the damage variable in the third principal direction, β3 is the material constant in this direction, ε3 is the strain in the third principal direction, and ε d0 is the damage initiation strain, ε f is the critical damage strain.

[0073] The formula for calculating the distance vector between material points is:

[0074] r ij =x j -x i

[0075] Where r ij is the distance vector between material points, x j and xi are the position vectors of material points j and i, respectively;

[0076] The formula for calculating the interaction force between material points is:

[0077]

[0078] Where, F ij is the interaction force between material points, r ij is the distance vector between material points, W is the strain energy density function, which is determined by the elastic-plastic damage constitutive model;

[0079] The formula for calculating the local stiffness matrix between material points is:

[0080]

[0081] Where K ij is the local stiffness matrix between material points, r ij is the distance vector between material points, W′ is the strain energy density function, which is determined by the elastic-plastic damage constitutive model;

[0082] Step A4: setting boundary conditions, including displacement boundary conditions and force boundary conditions, to ensure that the model is consistent with the actual test conditions; setting initial conditions, including initial stress field, initial displacement field, and initial velocity field;

[0083] Step A5: Construct the mass matrix and stiffness matrix to prepare for the subsequent solution of the motion equations. The explicit central difference method is used for time integration, and the time step is set. Considering the anisotropy of carbonaceous shale, an anisotropy coefficient is introduced when constructing the stiffness matrix to reflect the differences in mechanical response in different directions.

[0084] Step A6: Solve the equation of motion for each time step and update the position, velocity, acceleration, stress, and strain of the material point. During the solution process, the nonlinear mechanical behavior of carbonaceous shale is considered and an iterative algorithm is used to handle material nonlinearity and geometric nonlinearity.

[0085] Step A7: Calculate the energy density change of the material point at each time step to provide dynamic energy information for microcrack identification; at the same time, update the damage state of each material point based on the damage evolution equation of carbonaceous shale, and adjust the mechanical parameters of the material point according to the damage degree;

[0086] Step A8: Output the material point position, velocity, acceleration, stress, strain, energy density, and damage state information at each time step. In particular, mark the material points where significant damage or energy density changes occur as potential microcrack initiation locations.

[0087] The microcrack identification module is used to identify microcracks in rock samples, including the following steps:

[0088] Step B1: Receive material point model data transmitted by the material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; based on the received data, calculate the stress state of each material point, using the Cauchy stress tensor as the representation; during the calculation process, consider the anisotropic properties of carbonaceous shale and apply the defined anisotropic damage evolution equation to ensure the accuracy of the stress state calculation; this step lays a solid foundation for subsequent microcrack identification by comprehensively considering the mechanical properties of carbonaceous shale, thereby improving the accuracy and reliability of the analysis;

[0089] In step B2, the Mohr-Coulomb strength theory is applied to each material point to determine whether it has reached the failure condition. The failure criterion parameters use the set strength parameters, with a cohesion of 15 MPa and a friction angle of 28°. This step accurately identifies the potential microcrack initiation locations. For material points determined to have reached the failure condition, the maximum principal stress theory is applied to calculate their failure direction. During the calculation process, the three-dimensional characteristics of the stress state are fully considered to ensure the accuracy of the failure direction. This step not only accurately locates the starting point of microcracks, but also predicts their development direction, providing important guidance for the subsequent construction of microcrack networks.

[0090] Step B3: Connect adjacent failure material points in three-dimensional space to form an initial microcrack network. The connection condition is set to be that the spatial distance between two failure material points is less than 1.5 mm. The minimum spanning tree algorithm is used for connection to ensure that the generated microcrack network structure is reasonable and efficient. The geometric features of the formed initial microcrack network are extracted to calculate the length, width, and orientation angle of each microcrack. The least squares method is used to fit the geometric shape of the microcracks to improve the accuracy of feature extraction. This step provides key parameters for subsequent crack classification and delamination determination by constructing a microcrack network and extracting its geometric features, significantly improving the comprehensiveness and accuracy of microcrack characterization.

[0091] In step B4, microcracks are classified based on the extracted geometric features, and are divided into three categories according to their length: microcracks, medium cracks, and large cracks. The classification threshold is dynamically adjusted based on the actual size of the current carbonaceous shale sample to accommodate samples of different sizes. A complete microcrack network model is constructed to describe the spatial relationship and connectivity between microcracks. The network model is constructed using graph theory, treating each microcrack as an edge in the graph and the intersections of microcracks as nodes in the graph. The topological characteristics of the network are calculated, including connectivity, clustering coefficient, and average path length. This step, through classification and network model construction, achieves a systematic description of the microcrack system, providing a structured data foundation for subsequent analysis and greatly enhancing the system's analytical capabilities.

[0092] Step B5: Analyze the evolution trend of the microcrack network and calculate the network's dynamic characteristics, including the microcrack number growth rate, average length change rate, and network complexity growth rate. These dynamic characteristics provide an important basis for subsequent crack evolution analysis. Based on the network model and evolution analysis results, a microcrack distribution density map is generated. The kernel density estimation method is used to calculate the microcrack density distribution in three-dimensional space. This density map intuitively displays the concentrated areas of microcracks and potential high-risk areas, providing important visual and quantitative basis for delamination risk assessment.

[0093] Step B6: Calculate the anisotropy index of the microfracture network, reflecting the differences in the distribution of microfractures in different directions. The degree of anisotropy is quantified using a second-order structural tensor method. This index provides an important reference for assessing anisotropic damage in carbonaceous shale. Through this step, the system can fully capture the anisotropic characteristics of carbonaceous shale, improving the ability to predict delamination risk under complex geological conditions.

[0094] Step B7, integrating the analysis results of the previous steps to generate a comprehensive microcrack feature report, including the number of microcracks, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution, and anisotropy index;

[0095] Step B8: Output the generated comprehensive microcrack feature report, microcrack network model and dynamic evolution data to the crack evolution analysis module and the separation layer determination module.

[0096] The crack evolution analysis module is used to analyze the expansion, convergence, and penetration of microcracks. By calculating the stress intensity factor and geometric characteristics of the microcrack tips, the direction and length of crack expansion are determined, and whether adjacent microcracks meet the convergence conditions is analyzed.

[0097] The crack evolution analysis module in the embodiment of the present invention uses the following steps to analyze the expansion, convergence and penetration of microcracks:

[0098] Step C1: receiving material point model data provided by the material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; integrating the comprehensive microcrack feature report, microcrack network model, and dynamic evolution data generated by the microcrack identification module; and standardizing the received data to a unified data format of double-precision floating point.

[0099] In step C2, the stress intensity factor (SIF) at each microcrack tip is calculated using the displacement extrapolation method based on the material point model data and the microcrack network model. The calculation process applies the anisotropic damage evolution equation. This method takes into account the anisotropic properties of carbonaceous shale and improves the accuracy of the SIF calculation.

[0100] In step C3, the maximum circumferential stress criterion is applied to predict the propagation direction of each microcrack. Combined with the set strength parameters, the critical conditions for crack propagation are calculated. The crack propagation length at each time step is determined using the Paris formula. The material constants and exponents in the Paris formula are obtained through experimental calibration. This method achieves precise quantification of the crack propagation process, providing a reliable foundation for subsequent analysis.

[0101] Step C4 analyzes the spatial relationship and stress field interaction between adjacent microcracks, setting the critical conditions for crack convergence to be a crack spacing less than 0.5 mm and a stress superposition effect of 1.5 times the stress intensity factor of a single crack. Using the constructed microcrack network model, each pair of adjacent cracks is evaluated to determine whether they meet the convergence conditions. This step, through clear numerical criteria, enables objective judgment of the crack convergence process.

[0102] In step C5, based on the analysis results of steps C3 and C4, the microcrack network model is updated. The geometric parameters of the extended cracks are adjusted, and the converging cracks are merged into new crack entities. During the update process, consistency with the microcrack classification method defined in step B4 is maintained. The update mechanism ensures that the crack network model reflects the latest crack status, improving the accuracy of subsequent analysis.

[0103] Step C6: Analyze the updated crack network and identify crack combinations that have penetrated. The penetration condition is set as the path formed by the crack connection passing through 50% of the length of the specimen. Evaluate the impact of the through-cracks on the overall mechanical properties of the specimen and calculate the reduction in specimen stiffness. The through-crack analysis provides a key basis for delamination risk assessment and significantly improves the system's early warning capabilities.

[0104] Step C7, calculate the crack density change rate, average crack length growth rate, crack network complexity growth rate, and dominant change value of crack direction to describe the evolution process of the crack network:

[0105] Fracture density change rate = (fracture density at the current time step - fracture density at the previous time step) / time step length;

[0106] Average crack length growth rate = (average crack length of current time step - average crack length of previous time step) / time step;

[0107] Crack network complexity growth rate = (number of network nodes in the current time step - number of network nodes in the previous time step) / time step;

[0108] Dominant change in crack direction = anisotropy index of the current time step - anisotropy index of the previous time step;

[0109] Step C8: Generate a crack evolution analysis report, including the crack propagation path, convergence position, penetration status, and evolution characteristic indicators; and output the report to the separation layer determination module.

[0110] The delamination determination module is used to determine whether delamination has occurred in carbonaceous shale samples based on the evolution characteristics of microcracks. By extracting the number, average length, average width, and average orientation angle of microcracks, and combining the material point damage change rate and the fracture network complexity growth rate, a characteristic vector is constructed. The impact of the geometric characteristics and evolution trend of microcracks on delamination is evaluated. Taking into account the changes in the mechanical properties of carbonaceous shale samples during the damage accumulation process, the delamination index evaluation value and the dynamic critical delamination index evaluation value are obtained.

[0111] The formula for obtaining the separation index evaluation value in the embodiment of the present invention is:

[0112]

[0113] Where DI is the delamination index evaluation value, N is the number of microcracks, L is the average length, W is the average width, θ is the average direction angle, ΔD is the average damage degree change rate of material points, ΔC is the growth rate of crack network complexity, and α and β are adjustable weight coefficients.

[0114] The delamination index evaluation value in the embodiment of the present invention is used to comprehensively evaluate the impact of the geometric characteristics and evolution trend of microcracks on the delamination.

[0115] The formula for calculating the dynamic critical index evaluation value in the embodiment of the present invention is:

[0116] DI c =DI c0 ·(1-γ·S r );

[0117] Where, DI c is the dynamic critical delamination index evaluation value, DI c0 is the initial critical delamination index of the carbonaceous shale sample, which is pre-set according to the mechanical properties of this type of shale; S r is the stiffness reduction ratio of the carbonaceous shale sample, and γ is the influence coefficient, which is used to adjust the influence of stiffness reduction on the critical delamination index.

[0118] The separation determination module in the embodiment of the present invention calculates the probability of separation based on the separation index evaluation value and the dynamic critical separation index, and the formula is:

[0119]

[0120] Where P is the probability of separation, DI is the evaluation value of separation index, and DI cis the dynamic critical delamination index evaluation value, G is the penetration degree, which is defined as the ratio of the longest penetration path to the specimen length and is obtained from the crack evolution analysis module; δ is the penetration influence coefficient.

[0121] The delamination determination module in the embodiment of the present invention determines whether delamination has occurred in the carbonaceous shale sample by comparing the calculated delamination probability with a preset threshold. When the delamination probability is greater than the preset threshold, the sample is determined to have delamination; when the delamination probability is less than or equal to the preset threshold, the sample is determined not to have delamination.

[0122] The result output module is used to collect and organize the material point position, velocity, stress, strain and damage state provided by the material point modeling module; the number of microcracks, average length, average width and average direction angle provided by the microcrack identification module; the crack extension length, convergence position, penetration status and fracture network complexity growth rate provided by the crack evolution analysis module; and the delamination index evaluation value, dynamic critical delamination index and final delamination probability provided by the delamination determination module; and output the conclusion on whether delamination has occurred in the carbonaceous shale sample.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A separation layer microcrack analysis system based on the material point method, comprising a data acquisition module, a preprocessing module, a material point modeling module, a microcrack identification module, a crack evolution analysis module, a separation layer determination module and a result output module, characterized in that: The data acquisition module is connected to the preprocessing module, the preprocessing module is connected to the material point modeling module, the material point modeling module is connected to the microcrack identification module and the crack evolution analysis module, the microcrack identification module is bidirectionally connected to the crack evolution analysis module, the crack evolution analysis module is connected to the separation layer determination module, and the separation layer determination module is connected to the result output module; The material point modeling module is used to convert the preprocessed data into a discrete material point model, combine the stress, strain, displacement and acoustic emission signal data, calculate the energy density of the material point through the acoustic emission signal, and establish the interaction relationship between the material points to form a complete material point model; The crack evolution analysis module is used to analyze the expansion, convergence and penetration process of microcracks. By calculating the stress intensity factor and geometric characteristics of the microcrack tips, the direction and length of crack expansion are determined, and whether adjacent microcracks meet the convergence conditions is analyzed. The delamination determination module is used to determine whether delamination has occurred in the carbonaceous shale sample based on the evolution characteristics of microcracks. By extracting the number, average length, average width, and average orientation angle of microcracks, and combining the material point damage change rate and the crack network complexity growth rate, a characteristic vector is constructed. The geometric characteristics and evolution trend of microcracks are evaluated for the impact on delamination. The change in the mechanical properties of the carbonaceous shale sample during the damage accumulation process is considered to obtain the delamination index evaluation value and the dynamic critical delamination index evaluation value. The formula for obtaining the delamination index evaluation value is: ； Where, is the evaluation value of the separation index, is the number of microcracks, is the average length, is the average width, is the average direction angle, is the average damage degree change rate of the material point, is the growth rate of crack network complexity, and is an adjustable weight coefficient; The formula for calculating the dynamic critical index evaluation value is: ； Where, is the dynamic critical separation index evaluation value, is the initial critical delamination index of carbonaceous shale sample, is the stiffness reduction ratio of the carbonaceous shale sample, is the influence coefficient; The crack evolution analysis module uses the following steps to analyze the expansion, convergence and penetration of microcracks: Step C1, receiving material point model data provided by a material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; and performing standardization processing on the received data; Step C2, based on the material point model data and the microcrack network model, the stress intensity factor at each microcrack tip is calculated using the displacement extrapolation method; Step C3, applying the maximum circumferential stress criterion to predict the propagation direction of each microcrack; determining the propagation length of the crack in each time step by using the Paris formula; Step C4: Analyze the spatial relationship and stress field interaction between adjacent microcracks; set the critical conditions for crack convergence, and use the constructed microcrack network model to evaluate whether each pair of adjacent cracks meets the convergence conditions; Step C5: Based on the analysis results of steps C3 and C4, the microcrack network model is updated, and the geometric parameters of the extended cracks are adjusted; and the converged cracks are merged into new crack entities; Step C6, analyzing the updated crack network, identifying the crack combination that has penetrated, and calculating the reduction ratio of the specimen stiffness; Step C7, calculating the crack density change rate, average crack length growth rate, crack network complexity growth rate, and dominant change value of crack direction to describe the evolution process of the crack network; Step C8: Generate a crack evolution analysis report, including the crack propagation path, convergence position, penetration status, and evolution characteristic indicators; and output the report to the separation layer determination module.

2. The delamination microcrack analysis system based on the material point method according to claim 1, characterized in that: The data acquisition module includes a stress sensor, a strain sensor, a displacement sensor and an acoustic emission sensor; wherein the stress sensor is used to measure the axial stress and lateral stress of the carbonaceous shale sample, the strain sensor is used to measure the axial strain and lateral strain of the carbonaceous shale sample, the displacement sensor is used to measure the axial displacement and radial displacement of the carbonaceous shale sample, and the acoustic emission sensor is used to detect the elastic waves released when microcracks inside the carbonaceous shale sample are generated and expanded; the data acquisition module also includes a data acquisition card for converting the analog signals collected by each sensor into digital signals; the preprocessing module is used to perform noise reduction, filtering and normalization on the digital signals collected by the data acquisition module.

3. The delamination microcrack analysis system based on the material point method according to claim 1, characterized in that: The material point modeling module discretizes the continuum into discrete material points using the material point method and simulates the mechanical behavior of the carbonaceous shale sample by establishing the interaction relationship between the material points, including the following steps: Step A1: receiving stress, strain, displacement, and acoustic emission signal data transmitted by the preprocessing module; dividing the carbonaceous shale sample into uniformly distributed hexahedral grid cells according to the geometric dimensions of the carbonaceous shale sample and a preset grid resolution; generating a predetermined number of material points in each grid cell, and assigning initial coordinates to each material point; Step A2: Calculate the mass and volume of each material point based on the stress and strain data; calculate the initial velocity of each material point using the displacement data; and calculate the energy density of each material point based on the acoustic emission signal data. Step A3: Based on the carbonaceous shale type and experimental data, an elastoplastic damage constitutive model is selected, and elastic parameters, strength parameters, and damage parameters are set to define an anisotropic damage evolution equation; the interaction relationship between material points is established, including calculating the distance between material points, interaction force, and stiffness matrix; Step A4, setting boundary conditions, including displacement boundary conditions and force boundary conditions, and setting initial conditions, including initial stress field, initial displacement field, and initial velocity field; Step A5: construct the mass matrix and stiffness matrix, perform time integration using the explicit central difference method, and set the time step; Step A6, solving the equation of motion for each time step and updating the position, velocity, acceleration, stress, and strain of the material point; Step A7, calculating the energy density change of the material point at each time step; Step A8: Output the material point position, velocity, acceleration, stress, strain, energy density and damage state information at each time step.

4. The delamination microcrack analysis system based on the material point method according to claim 1, characterized in that: The microcrack identification module is used to identify microcracks in rock samples, and includes the following steps: Step B1: receiving material point model data transmitted by the material point modeling module, including the position, velocity, acceleration, stress, strain, energy density, and damage state information of each material point; and calculating the stress state of each material point based on the received data; Step B2, applying the Mohr-Coulomb strength theory to each material point to determine whether it meets the failure condition; Step B3: Connect adjacent failure material points in three-dimensional space to form an initial microcrack network, where the connection condition is set to be that the spatial distance between two failure material points is less than 1.5 mm; perform geometric feature extraction on the formed initial microcrack network to calculate the length, width, and orientation angle of each microcrack; Step B4: Based on the extracted geometric features, the microcracks are classified and a complete microcrack network model is constructed to describe the spatial relationship and connectivity between the microcracks. The network model is constructed using graph theory, with each microcrack considered as an edge in the graph and the intersections of microcracks as nodes in the graph. The topological characteristics of the network are calculated, including connectivity, clustering coefficient, and average path length. Step B5: Analyze the evolution trend of the microcrack network and calculate the dynamic characteristics of the network, including the growth rate of the number of microcracks, the average length change rate, and the network complexity growth rate. Based on the network model and the evolution analysis results, generate a microcrack distribution density map and use the kernel density estimation method to calculate the microcrack density distribution in three-dimensional space. Step B6, calculating the anisotropy index of the microcrack network and quantifying the degree of anisotropy using a second-order structure tensor method; Step B7, integrating the analysis results of the previous steps to generate a comprehensive microcrack feature report, including the number of microcracks, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution, and anisotropy index; Step B8: Output the generated comprehensive microcrack feature report, microcrack network model and dynamic evolution data to the crack evolution analysis module and the separation layer determination module.

5. The delamination microcrack analysis system based on the material point method according to claim 1, characterized in that: The separation determination module calculates the probability of separation based on the separation index evaluation value and the dynamic critical separation index, and the formula is: ； Where, is the probability of separation, is the evaluation value of the separation index, is the dynamic critical separation index evaluation value, For the degree of penetration, is the penetration influence coefficient.

6. A delamination microcrack analysis system based on material point method according to claim 1 or 5, characterized in that: The delamination determination module determines whether delamination occurs in the carbonaceous shale sample by comparing the calculated delamination probability with the preset threshold.

7. The delamination microcrack analysis system based on the material point method according to claim 1, characterized in that: The result output module is used to collect and organize the material point position, velocity, stress, strain and damage state provided by the material point modeling module; the number of microcracks, average length, average width and average direction angle provided by the microcrack identification module; the crack extension length, convergence position, penetration status and crack network complexity growth rate provided by the crack evolution analysis module; and the delamination index evaluation value, dynamic critical delamination index evaluation value and final delamination probability provided by the delamination determination module; and output a conclusion on whether delamination has occurred in the carbonaceous shale sample.

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