Separation layer micro-crack analysis system based on material point method

Abstract

The invention relates to the technical field of separation layer micro-crack analysis, and provides a separation layer micro-crack analysis system based on a material point method.The method comprises the steps that firstly, a data acquisition module is used for acquiring data such as stress, strain, displacement and acoustic emission signals from a carbonaceous shale sample; the preprocessing module preprocesses the collected data to ensure the accuracy of subsequent analysis; the material point modeling module constructs a discrete material point model according to the processed data, and simulates the mechanical behavior of the carbonaceous shale sample; the micro-crack identification module is responsible for analyzing crack characteristics in the material point model and identifying geometric morphology and spatial distribution of micro-cracks; the crack evolution analysis module further perfects the crack network model by calculating the crack expansion and convergence process; the bed separation judgment module evaluates whether the sample has bed separation or not by combining the change of damage degree and complexity based on the evolution characteristics of the fracture network; and finally, the result output module summarizes and outputs various analyzed result data. Finally, the method is only the preferred embodiment of the invention and is not used for limiting the method, and any modification, equivalent replacement, improvement and the like made within the spirit and principle of the method should be included in the protection range of the method.

Description

Technical Field

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

[0002] Carbonaceous shale is a sedimentary rock rich in organic matter. Due to its high carbon content and rich reserves of shale gas and oil resources, it has received widespread attention in recent years. Carbonaceous shale has unique physical and mechanical properties. Especially during the mining process, external stress can cause microcracks inside it. As microcracks expand and converge, the integrity of the material may be seriously affected, and even delamination damage may occur. Therefore, in-depth research on delamination microcracks in carbonaceous shale is of great practical significance for the efficient mining of resources and the avoidance of engineering disasters. By accurately analyzing the evolution process of microcracks, the mechanical behavior of shale can be effectively predicted, production safety can be guaranteed, and resource recovery rate can be improved.

[0003] At present, the commonly used microcrack analysis methods are mainly based on numerical simulation techniques 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 deal with 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 problems of fiber reinforced composites based on XFEM. The study shows how to use XFEM to simulate the microcrack propagation and delamination phenomena in two-dimensional and three-dimensional composite materials, and proves the effectiveness of XFEM technology in predicting and analyzing microcracks in different materials. The research results show that XFEM can effectively capture the propagation path and delamination process of cracks, providing a powerful tool for the analysis of microcracks.

[0005] However, some existing studies still have some limitations in the detailed processing of microcrack identification and dynamic evolution. For example, in the paper "Characterization methods of delamination in a plain woven CFRPcomposite", Liu et al. proposed a method for analyzing the delamination behavior of plain carbon fiber reinforced polymer composites. Although this method uses a linear elastic fracture mechanics model to conduct a detailed analysis of the delamination phenomenon and compares the experimental results with the numerical simulation results under different crack modes, showing its potential in delamination assessment, this study is insufficient in the identification and evolution trend analysis of microcracks. Specifically, the method in the literature relies too much on the analysis of macroscopic crack extension, and the consideration of important parameters such as the initial formation mechanism of microcracks, crack convergence conditions, and the complexity of the crack network is relatively limited. In addition, this study lacks a comprehensive evaluation of the evolution of cracks under dynamic stress conditions and their impact on the overall performance of the material, especially in the extraction of key indicators for crack evolution to determine delamination, there is still much room for improvement.

[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; through the material point method combined with the microcrack evolution characteristics, the crack extension 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, comprising 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, and to determine the direction and length of crack expansion by calculating the stress intensity factor and geometric characteristics of the microcrack tip, and to analyze whether adjacent microcracks meet the convergence conditions;

[0012] The delamination determination module is used to determine whether delamination occurs in the carbonaceous shale sample based on the evolution characteristics of microcracks. The feature vector is constructed by extracting the number, average length, average width and average direction angle of microcracks, combining the material point damage degree change rate and the crack network complexity growth rate, and evaluating the influence of the geometric characteristics and evolution trend of microcracks on delamination. The delamination index evaluation value and the dynamic critical delamination index evaluation value are obtained by considering the change of mechanical properties of the carbonaceous shale sample during the damage accumulation process. The formula for obtaining the delamination index evaluation value is:

[0013]

[0014] Where DI is the delamination index assessment 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 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 desorption index evaluation value, DI c0 is the initial critical delamination index of carbonaceous shale sample, S r is the stiffness reduction ratio of 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 processing 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 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:

[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 units according to the geometric size of the carbonaceous shale sample and the preset grid resolution; generating a predetermined number of material points in each grid unit, and assigning initial coordinates to each material point;

[0021] Step A2, calculating the mass and volume of each material point according to the stress and strain data; calculating the initial velocity of each material point using the displacement data; and calculating the energy density of each material point based on the acoustic emission signal data;

[0022] Step A3, according to the carbonaceous shale type and experimental data, select the elastoplastic damage constitutive model, set the elastic parameters, strength parameters and damage parameters, define the damage evolution equation of anisotropic characteristics; establish the interaction relationship between material points, 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, constructing the mass matrix and stiffness matrix, performing time integration using the explicit central difference method, and setting 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 reaches the failure condition;

[0031] Step B3, connecting adjacent damaged material points in three-dimensional space to form an initial microcrack network, wherein the connection condition is set such that the spatial distance between two damaged material points is less than 1.5 mm; extracting geometric features of the formed initial microcrack network, and calculating the length, width and direction 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 a graph theory method, each microcrack is regarded as an edge in the graph, and the intersection of the microcracks is regarded as a node in the graph; the topological characteristics of the network are calculated, including connectivity, clustering coefficient, and average path length;

[0033] Step B5, analyzing the evolution trend of the microcrack network, calculating the dynamic characteristics of the network, including the growth rate of the number of microcracks, the average length change rate and the growth rate of network complexity, generating a microcrack distribution density map based on the network model and the evolution analysis results, and calculating the microcrack density distribution in three-dimensional space using a kernel density estimation method;

[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, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution and anisotropy index of microcracks;

[0036] Step B8, outputting the generated comprehensive microcrack characteristic 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 adopts the following steps to analyze the expansion, convergence and penetration process 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 the tip of each microcrack is calculated using the displacement extrapolation method;

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

[0041] Step C4, analyzing the spatial relationship and stress field interaction between adjacent microcracks; setting the critical conditions for crack convergence, and using 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; 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, the average crack length growth rate, the crack network complexity growth rate and the dominant change value of the crack direction to describe the evolution process of the crack network;

[0045] Step C8, generating a crack evolution analysis report, including crack extension path, convergence position, penetration status and evolution characteristic indicators; outputting 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 delamination 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 separation layer 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 realizes the simulation of the microcrack evolution process by discretizing the carbonaceous shale sample into a material point model, and can capture the detailed changes in the expansion, convergence and penetration process of microcracks, especially in the analysis of the geometric characteristics, evolution path and spatial distribution of microcracks, which makes up for the limitations of the traditional finite element method (FEM) and the 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 evaluation model is established. This can more comprehensively and dynamically evaluate the fracture expansion and delamination risk of carbonaceous shale during the mining process. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments. Obviously, the described embodiments 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 creative work 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 comprises 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; 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.

[0060] The data acquisition module in the embodiment of the present invention further includes a data acquisition card, which is used to convert 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 the 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 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:

[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 units according to the geometric size of the carbonaceous shale sample and the preset grid resolution; generating a predetermined number of material points in each grid unit, 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 point; Calculate the initial velocity of each material point using displacement data 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 criterion for subsequent microcrack identification;

[0066] Step A3, according to the carbonaceous shale type and experimental data, select the elastoplastic damage constitutive model, set the elastic parameters, strength parameters and damage parameters, and define the damage evolution equation with anisotropic characteristics; establish the interaction relationship between material points, including calculating the distance, interaction force and stiffness matrix between material points; among the elastic parameters, the elastic modulus is 25GPa and the Poisson's ratio is 0.23; among the strength parameters, the cohesion is 15MPa 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] In the formula, 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] In the formula, 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] In the formula, 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, constructing the mass matrix and stiffness matrix to prepare for the subsequent solution of the motion equation; performing time integration using the explicit central difference method and setting the time step; considering the anisotropy of carbonaceous shale, introducing the anisotropy coefficient when constructing the stiffness matrix to reflect the difference in mechanical response in different directions;

[0084] Step A6, solving the equation of motion for each time step, and updating the position, velocity, acceleration, stress and strain of the material point; in the solution process, the nonlinear mechanical behavior of carbonaceous shale is considered, and an iterative algorithm is used to deal with material nonlinearity and geometric nonlinearity;

[0085] Step A7, calculating the energy density change of the material point at each time step to provide dynamic energy information for microcrack identification; at the same time, according to the damage evolution equation of carbonaceous shale, updating the damage state of each material point, and adjusting 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, especially mark the material points where significant damage or energy density changes occur as potential microcrack starting positions.

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

[0088] Step B1, receiving the 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, calculating the stress state of each material point, using the Cauchy stress tensor to represent it; in the calculation process, considering the anisotropic characteristics of carbonaceous shale, applying the defined anisotropic damage evolution equation to ensure the accuracy of stress state calculation; this step lays a solid foundation for subsequent microcrack identification by comprehensively considering the mechanical properties of carbonaceous shale, and improves the accuracy and reliability of the analysis;

[0089] Step B2, 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, the cohesion is 15MPa, and the friction angle is 28°; through this step, the potential microcrack starting position is accurately identified; for the material point that is determined to have reached the failure condition, the maximum principal stress theory is applied to calculate its failure direction; in the calculation process, the three-dimensional characteristics of the stress state are fully considered to ensure the accuracy of the failure direction; this step can not only accurately locate the starting point of the microcrack, but also predict its development direction, providing important guidance for the subsequent construction of the microcrack network;

[0090] Step B3, connecting adjacent damaged material points in three-dimensional space to form an initial microcrack network, with the connection condition set to a spatial distance of less than 1.5 mm between two damaged material points; using a minimum spanning tree algorithm for connection to ensure that the generated microcrack network has a reasonable and efficient structure; extracting geometric features from the formed initial microcrack network to calculate the length, width and direction angle of each microcrack; using the least squares method 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] Step B4, based on the extracted geometric features, the microcracks are classified into three categories according to their lengths: microcracks, medium cracks and large cracks; the classification threshold is dynamically adjusted according to the actual size of the current carbonaceous shale sample to adapt to 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, each microcrack is regarded as an edge in the graph, and the intersection of microcracks is regarded as a node in the graph; the topological characteristics of the network are calculated, including connectivity, clustering coefficient and average path length; this step realizes a systematic description of the microcrack system through classification and network model construction, provides a structured data basis for subsequent analysis, and greatly enhances the analysis capability of the system;

[0092] Step B5, analyzing the evolution trend of the microcrack network, calculating the dynamic characteristics of the network, including the growth rate of the number of microcracks, the average length change rate and the growth rate of network complexity, these dynamic characteristics provide important basis for the subsequent crack evolution analysis; 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; the density map intuitively shows 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 microcrack network to reflect the distribution difference of microcracks in different directions; use the second-order structure tensor method to quantify the degree of anisotropy, and this index provides an important reference for evaluating the anisotropic damage of carbonaceous shale; through this step, the system can fully capture the anisotropic characteristics of carbonaceous shale and improve the prediction ability of 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, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution and anisotropy index of microcracks;

[0095] Step B8, outputting the generated comprehensive microcrack characteristic 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 process of microcracks. By calculating the stress intensity factor and geometric characteristics of the microcrack tip, 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 process of micro cracks:

[0098] Step C1, receiving the 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; standardizing the received data and unifying the data format into double-precision floating point type;

[0099] Step C2, based on the material point model data and the microcrack network model, the displacement extrapolation method is used to calculate the stress intensity factor at the tip of each microcrack; the anisotropic damage evolution equation is applied in the calculation process; this method takes into account the anisotropic characteristics of carbonaceous shale and improves the accuracy of the stress intensity factor calculation;

[0100] Step C3, applying the maximum circumferential stress criterion to predict the expansion direction of each microcrack, combining the set strength parameters, calculating the critical conditions for crack expansion, and determining the crack expansion length in each time step by the Paris formula. The material constants and exponents in the Paris formula are obtained through experimental calibration; this method achieves accurate quantification of the crack expansion process and provides a reliable basis for subsequent analysis;

[0101] Step C4, analyzing the spatial relationship and stress field interaction between adjacent microcracks, setting the critical condition for crack convergence as the crack spacing less than 0.5 mm and the stress superposition effect reaching 1.5 times the stress intensity factor of a single crack; using the constructed microcrack network model, evaluating whether each pair of adjacent cracks meets the convergence condition; this step achieves objective judgment of the crack convergence process through clear numerical standards;

[0102] 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; the converged cracks are merged into new crack entities; during the updating process, the consistency with the microcrack classification method defined in step B4 is maintained; the updating mechanism ensures that the crack network model reflects the latest crack status and improves the accuracy of subsequent analysis;

[0103] Step C6, analyze the updated crack network, identify the crack combination that has penetrated, set the penetration condition as the path formed by the crack connection penetrating 50% of the length of the sample; evaluate the impact of the through cracks on the overall mechanical properties of the sample, and calculate the reduction ratio of the sample stiffness; the penetration analysis provides a key basis for the risk assessment of delamination, and significantly improves the early warning capability of the system;

[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] Crack density change rate = (crack density at current time step - crack density at previous time step) / time step;

[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] The dominant change of crack direction = anisotropy index of the current time step - anisotropy index of the previous time step;

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

[0110] The delamination determination module is used to determine whether delamination occurs 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 influence of the geometric characteristics and evolution trend of microcracks on delamination is evaluated, and the changes in the mechanical properties of carbonaceous shale samples during the damage accumulation process are considered to obtain the delamination index evaluation value and the dynamic critical delamination index evaluation value.

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

[0112]

[0113] Where DI is the delamination index assessment 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 influence of the geometric characteristics and evolution trend of microcracks on 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 desorption index evaluation value, DI c0 is the initial critical delamination index of the carbonaceous shale sample, which is preset 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 occurrence 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 sample length, 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, it is determined that delamination has occurred in the sample; when the delamination probability is less than or equal to the preset threshold, it is determined that no delamination has occurred in the sample.

[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 and crack 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 occurs in the carbonaceous shale sample.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope 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 protection scope of the present invention.

Claims

1. A separation layer microcrack analysis system based on 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, and to determine the direction and length of crack expansion by calculating the stress intensity factor and geometric characteristics of the microcrack tip, and to analyze whether adjacent microcracks meet the convergence conditions; The delamination determination module is used to determine whether delamination occurs in the carbonaceous shale sample based on the evolution characteristics of microcracks. The feature vector is constructed by extracting the number, average length, average width and average direction angle of microcracks, combining the material point damage degree change rate and the crack network complexity growth rate, and evaluating the influence of the geometric characteristics and evolution trend of microcracks on delamination. The delamination index evaluation value and the dynamic critical delamination index evaluation value are obtained by considering the change of mechanical properties of the carbonaceous shale sample during the damage accumulation process. The formula for obtaining the delamination index evaluation value is: Where DI is the delamination index assessment 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 the material point, ΔC is the growth rate of the crack network complexity, and α and β are adjustable weight coefficients; The formula for calculating the dynamic critical index evaluation value is: DI c =DI c0 (1-γ S r ); Where, DI c is the dynamic critical desorption index evaluation value, DI c0 is the initial critical delamination index of carbonaceous shale sample, S r is the stiffness reduction ratio of carbonaceous shale sample, and γ is the influence coefficient.

2. A delamination microcrack analysis system based on 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 the microcracks inside the carbonaceous shale sample are generated and expanded; the data acquisition module also includes a data acquisition card, which is used to convert the analog signals collected by each sensor into digital signals; the preprocessing module is used to perform noise reduction processing, filtering processing and normalization processing 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 units according to the geometric size of the carbonaceous shale sample and the preset grid resolution; generating a predetermined number of material points in each grid unit, and assigning initial coordinates to each material point; Step A2, calculating the mass and volume of each material point according to the stress and strain data; calculating the initial velocity of each material point using the displacement data; and calculating the energy density of each material point based on the acoustic emission signal data; Step A3, according to the carbonaceous shale type and experimental data, select the elastoplastic damage constitutive model, set the elastic parameters, strength parameters and damage parameters, define the damage evolution equation of anisotropic characteristics; establish the interaction relationship between material points, 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, constructing the mass matrix and stiffness matrix, performing time integration using the explicit central difference method, and setting 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 a rock sample, 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 reaches the failure condition; Step B3, connecting adjacent damaged material points in three-dimensional space to form an initial microcrack network, wherein the connection condition is set such that the spatial distance between two damaged material points is less than 1.5 mm; extracting geometric features of the formed initial microcrack network, and calculating the length, width and direction 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 a graph theory method, each microcrack is regarded as an edge in the graph, and the intersection of the microcracks is regarded as a node in the graph; the topological characteristics of the network are calculated, including connectivity, clustering coefficient, and average path length; Step B5, analyzing the evolution trend of the microcrack network, calculating the dynamic characteristics of the network, including the growth rate of the number of microcracks, the average length change rate and the growth rate of network complexity, generating a microcrack distribution density map based on the network model and the evolution analysis results, and calculating the microcrack density distribution in three-dimensional space using a kernel density estimation method; 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, average length, average width, average orientation angle, network topology characteristics, dynamic evolution characteristics, density distribution and anisotropy index of microcracks; Step B8, outputting the generated comprehensive microcrack characteristic 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 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 the tip of each microcrack is calculated using the displacement extrapolation method; Step C3, applying the maximum circumferential stress criterion to predict the propagation direction of each microcrack; The extension length of the crack in each time step is determined by the Paris formula; Step C4, analyzing the spatial relationship and stress field interaction between adjacent microcracks; setting the critical conditions for crack convergence, and using 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; 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, the average crack length growth rate, the crack network complexity growth rate and the dominant change value of the crack direction to describe the evolution process of the crack network; Step C8, generating a crack evolution analysis report, including crack extension path, convergence position, penetration status and evolution characteristic indicators; outputting the report to the separation layer determination module.

6. 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 P is the probability of separation, DI is the evaluation value of separation index, and DI c is the dynamic critical delamination index evaluation value, G is the penetration degree, and δ is the penetration influence coefficient.

7. A delamination microcrack analysis system based on material point method according to claim 1 or 6, 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.

8. 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 situation 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 of whether delamination occurs in the carbonaceous shale sample.

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