Simulation method of micro-pore structure evolution in breakage process of porous friable particles

Abstract

This invention belongs to the field of particulate materials technology and relates to a simulation method for the evolution of microscopic pore structure during the crushing process of porous and fragile particles. The method includes the following steps: sample preparation, high-resolution X-CT scanning, image processing to obtain the analysis volume, three-dimensional reconstruction to generate a three-dimensional rendering model, multi-scale simulation to establish the model, pore extraction, porosity analysis, pore connectivity analysis, connected porosity analysis, and pore structure parameter analysis. Based on X-CT technology and AVIZO software, using high-resolution tomographic data and combining the denoising, segmentation, and modeling functions of AVIZO software, the pore structure changes during particle crushing are accurately extracted. Through three-dimensional reconstruction and pore structure analysis of single particles at the microscale, and overall simulation of the crushing behavior of particle groups at the microscale, the evolution law of porosity and its key parameters is dynamically quantified, and a quantitative correlation model between the degree of crushing and porosity changes is established.

Description

Technical Field

[0001] This invention belongs to the field of particulate materials technology, specifically relating to a simulation method for the evolution of microscopic pore structure during the crushing process of porous and fragile particles. It is applicable to analyzing porosity changes and pore structure evolution caused by particle crushing. Background Technology

[0002] Porous and fragile granular soil and rock materials, such as carbonate granules, volcanic rock granules, glacial till granules, and expansive soil granules, are widely found in nature and engineering. For example, coarse-grained carbonate granules, represented by coral, are common in tropical and subtropical coastal areas and terrestrial sedimentary strata. They possess a unique biological framework structure, resulting in abundant internal pores and a fragile skeleton. These materials, due to their high porosity and low compressive strength, are easily broken down by external forces such as earthquakes, collapses, and floods in natural environments, leading to significant evolution of the pore structure and ultimately weakening of mechanical properties and changes in permeability. In engineering, using materials appropriate to local conditions can minimize economic costs and reduce environmental pollution. Porous and fragile granular soil and rock materials existing in different environments are often important components of local strata, and in reality, these materials are widely used in projects such as dams, roadbeds, and embankments. However, they are prone to particle breakage under engineering loads, and the changes in pore structure during the breakage process often become a key factor affecting the stability and safety of the project. Therefore, studying the evolution of pore structure in such materials is of great significance for understanding their performance evolution and optimizing their engineering applications.

[0003] Currently, the main techniques for studying the evolution of pore structure in porous and fragile materials include scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), and nuclear magnetic resonance imaging (NMR). SEM offers high resolution and is suitable for characterizing local geometric details of pores, allowing for detailed observation of pore morphology and microscopic features on particle surfaces. However, its field of view is relatively small, only allowing observation of local areas of the sample and failing to reflect the overall pore structure characteristics. Furthermore, it is only suitable for static monitoring. MIP, by measuring the infiltration characteristics of mercury under different pressures, can quantify total porosity and pore size distribution, making it particularly suitable for describing the macroscopic pore characteristics of materials. However, its testing process is destructive, causing irreversible damage to the sample's pore structure, and it cannot capture the dynamic changes in pore structure during particle breakage. NMR utilizes magnetic resonance signals for non-destructive detection of pore distribution within the sample, offering advantages of being non-destructive and efficient. However, its resolution is relatively low, making it difficult to accurately characterize complex pore networks and multi-scale pore structures. Overall, these traditional techniques are mainly limited to static structural analysis, lack the ability to continuously monitor the dynamic evolution of porosity during particle crushing, and cannot reveal the correlation mechanism between pore structure changes and material mechanical properties and permeation behavior, thus limiting their application potential in dynamic process research.

[0004] In recent years, X-ray computed tomography (X-CT) technology has demonstrated significant advantages in the study of porous materials, enabling high-resolution three-dimensional non-destructive reconstruction. However, existing research on the evolution of pore structure during the crushing process of porous and fragile soil granular materials remains primarily static, lacking a systematic framework for multi-scale dynamic monitoring and correlation modeling. This makes accurately characterizing the dynamic changes of pore structure parameters such as porosity, equivalent pore diameter, and pore connectivity during particle crushing, and further revealing their impact on mechanical behavior and permeability, a crucial problem urgently needing to be solved in current research. Summary of the Invention

[0005] Based on the shortcomings of existing technologies in the study of porosity evolution during the crushing process of porous particles, this invention provides a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles. The purpose is to solve the problem that it is difficult to achieve continuous dynamic monitoring and quantitative analysis of porosity during particle crushing in existing technologies.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] A simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles includes the following steps:

[0008] 1. Sample preparation: Dry the sample in an oven at 110℃. When the moisture content is 0%, take out the dried sample, pass it through a sieve with a 2mm aperture, select particles with a diameter d>2mm, and use a fine brush to remove dust and impurities from the sample surface to obtain the sample to be tested.

[0009] 2. X-CT Scan: The sample is attached to the test stage of the high-resolution X-CT scanner with double-sided tape, so that the long axis of the sample is perpendicular to the test stage. The sample is scanned using the X-CT scanner with a scanning voltage of 120kV, a current of 150μA, and a resolution of 20μm to obtain a particle tomographic image data file in TIFF format. The resolution of the TIFF format file must exceed 1000 pixels.

[0010] 3. Image processing: Import the TIFF format file into AVIZO (2022 version) software, use the "Non-LocalMeans" algorithm to remove noise generated during the scanning process, use the "Volume Edit" function to cut the image area, remove the extra space voxels outside the solid interface, and obtain the analysis volume;

[0011] 4. 3D Reconstruction: Using the "Thresholding" tool on the analysis volume obtained in step 3, a threshold is set to separate the pores from the main particle body; the "Watershed" algorithm is used to further refine the boundary of the pore region; and the "VolumeRendering" function is used to generate a 3D rendering model of the main particle body based on the processed image sequence.

[0012] 5. Multi-scale simulation: To simulate the reduction in particle volume caused by particle breakage, the initial 3D model in step 4 is divided into multiple main bodies of different scales using the "Extract Subvolume" function, and multiple 3D rendering models of different scales are established using the "VolumeRendering" function.

[0013] 6. Pore Extraction: For the multiple 3D rendering models of the main body at different scales obtained in step 5, the "Thresholding" tool is used to perform pore threshold segmentation, and the "Volume Rendering" function is used to obtain the 3D pore rendering model.

[0014] 7. Porosity Analysis: Using the "Volume Fraction" module, calculate the total porosity of the model and the porosity of the two-dimensional slice. The calculation method is: Total Porosity (Φ v ) is the voxel volume (V) occupied by each pore model in step 6. pore ), and the voxel volume (V) occupied by each main model in step 5. model The percentages between ) are obtained as follows: Φ v The closer a value is to 1, the greater the porosity of the model; the closer it is to 0, the smaller the porosity of the model. The porosity (Φ) of a two-dimensional slice is... s ) is determined by the pore area of ​​the slice (S) pore ), and the total area of ​​the slice (S) s The percentage of ) is obtained as follows: φ s The closer a value is to 1, the higher the face area ratio of the slice; the closer a value is to 0, the lower the face area ratio of the slice.

[0015] 8. Pore Connectivity Analysis: Using the "Axis Connectivity" function, perform connectivity analysis on the 3D pore rendering models at each volume scale from step 6. If the "Data Info" in the analysis results shows "min-max:0...1", it indicates that the corresponding model has connected pores, i.e., connectivity exists; if the "Data Info" in the analysis results shows "min-max:0...0", it indicates that the corresponding model does not have connected pores, i.e., connectivity does not exist.

[0016] 9. Connectivity Porosity Analysis: If the 3D rendering model of the pores in step 8 contains connected pores, further, using the "Volume Rendering" function, the connectivity porosity is calculated based on the connected pore data volume obtained in step 8. The calculation method is: using the voxel volume (V) occupied by the 3D rendering model of the connected pores... tp ), and the voxel volume (V) occupied by each main 3D rendering model in step 5. model The percentage of interconnected porosity (φ) is used to obtain the interconnected porosity. t ): φ t The closer a value is to 1, the stronger the connectivity of the model; the closer a value is to 0, the weaker the connectivity of the model.

[0017] 10. Pore Structure Parameter Analysis: Based on the 3D rendering models of pores at various volume scales obtained in step 6, the "Label Analysis" function is used to calculate the pore structure parameters according to the output results. The pore structure parameters are the volume of a single pore (V). pi ), equivalent diameter (D) e ), shape factor (G) p The calculation method is: pore volume (V) pi The equivalent diameter (D) is directly derived from the volume of the voxel occupied by a single pore; e ) by regard to the volume of a single pore (V pi The formula is derived from: Shape factor (G) p Then, it is obtained from the definition of pore cross-sectional area (S) and pore perimeter (P):

[0018] Preferably, in step 1, the particle size of the porous soil and rock material is recommended to be 20-40 mm.

[0019] Preferably, in step 2, the high-resolution X-CT scanner is recommended to have a resolution of 10-30 μm.

[0020] Preferably, in step 5, the initial three-dimensional model is divided into its volume. Scale, i.e. Scale, further, the volumetric scale is Scale, i.e. A three-dimensional model of this scale needs to be derived from a volumetric scale. Inside the 3D model, and so on, the volume scale is... Right now A three-dimensional model of this scale needs to be derived from a volumetric scale. Inside the 3D model, the volume scale is Right now The 3D model needs to be taken from a volume scale of The simulation is performed within the three-dimensional model. The step-by-step scaling method ensures that the simulation is based on the same fragmented block, thus guaranteeing the accuracy of the analysis.

[0021] Preferably, in step 7, the obtained initial main body model is further utilized. Total porosity (φ) on a volume scale v ), establish φ v The corresponding curves between the particle size and volume at different scales are obtained to determine the impact of multi-scale particle breakage on total porosity, and to ascertain whether the method of the invention exhibits a scale effect. Specifically, for homogeneous granular materials, under ideal conditions... φ at the volume scale v They should be consistent.

[0022] Preferably, in step 9, the obtained initial main body model is further utilized. Viscous porosity (φ) on a volume scale t ), establish interconnected porosity (φ) t The relationship between the curves and the corresponding volumes at different scales was obtained, thereby revealing the changes in the connectivity of the pore structure during the multi-scale crushing of particles.

[0023] Preferably, in step 10, the single pore volume (V) obtained in step 10 is further utilized. pi ), equivalent diameter (D) e ), shape factor (G) p Establish frequency distribution diagrams of pore volume and equivalent diameter, and average equivalent diameter (D). e ), shape factor (G) p The curves showing the change between particle size and volume at various scales are used to understand the impact of particle breakage on pore structure.

[0024] This invention proposes a simulation method for the evolution of microscopic pore structure during the crushing process of porous and fragile particles, based on X-CT technology and AVIZO software. Utilizing high-resolution tomographic data and combining the denoising, segmentation, and modeling capabilities of AVIZO software, the pore structure changes during particle crushing are accurately extracted. Through three-dimensional reconstruction and pore structure analysis of individual particles at the microscale, as well as the overall simulation of the crushing behavior of particle groups at the microscale, the evolution of porosity and its key parameters (such as pore size distribution and connectivity) is dynamically quantified. A quantitative correlation model between the degree of crushing and porosity changes is established, providing data support for the performance prediction and engineering applications of porous particulate materials.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] 1. By combining X-CT technology with AVIZO software, high-precision non-destructive monitoring of dynamic porosity changes during particle crushing can be achieved without conducting macroscopic mechanical tests, thus improving work efficiency, while the test results have high accuracy.

[0027] 2. This invention establishes a multi-scale simulation framework at the microscale (evolution of pores within a single particle) and the microscale (overall behavior of a particle population), which can comprehensively characterize the impact of particle breakage on porosity and other key parameters (such as pore size distribution, connectivity, shape factor, etc.). It has the advantages of clear principles and obtaining multiple experimental parameters at the same time.

[0028] 3. The process of this invention is standardized, automated, and easy to operate. The method principle is clear, the operation is simple, and the cost is low. Researchers do not need to master complex professional knowledge to use it, which reduces the analysis threshold and labor costs. At the same time, compared with traditional experimental methods, sample preparation is simple and the experimental cost is significantly reduced.

[0029] 4. This invention has a wide scope and is applicable to a variety of porous and fragile particulate materials, such as carbonate particles and coral coarse particles. It has significant advantages, especially in engineering research that requires precise characterization of porosity evolution and prediction of particle properties.

[0030] 5. This invention fills the gap in the existing technology that cannot dynamically quantify the evolution law of particle porosity. By combining a multi-scale framework with high-resolution dynamic analysis, and using X-CT technology and AVIZO software, the three-dimensional pore structure of particles can be reconstructed non-destructively and rapidly, and the multi-scale changes of porosity can be accurately quantified. This provides an efficient and reliable solution for the performance research and engineering application of porous and fragile particulate materials; and provides new tools and new paths for the performance research and engineering application of porous particulate materials. Attached Figure Description

[0031] Figure 1 A flowchart illustrating a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles;

[0032] Figure 2 This is a schematic diagram of step 5, multi-scale simulation, of a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles.

[0033] Figure 3 Real-world images and three-dimensional main body model and three-dimensional pore model at the initial volume scale of 1, 2 and 3, of an embodiment of a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles;

[0034] Figure 4The following is a comparative analysis of the total porosity variation with simulated particle volume scale in Examples 1, 2, and 3 of a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles, and a comparative experiment based on mercury intrusion porosimetry (MIP).

[0035] Figure 5 Comparative analysis of two-dimensional slice face rate results from Examples 1, 2, and 3 of a simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles, and a comparative experiment based on scanning electron microscopy (SEM) combined with image digitization technology.

[0036] Figure 6 The figure shows a comparative analysis of the average pore equivalent diameter as a function of the simulated particle volume scale in Examples 1, 2, and 3 of a simulation method for the micropore structure evolution during the crushing process of porous and fragile particles, and a comparative experiment based on scanning electron microscopy (SEM) combined with image digitization technology.

[0037] Figure 7 The diagram shows the results of the interconnected porosity as a function of particle volume scale in Examples 1, 2, and 3 of the simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles. Detailed Implementation

[0038] The following, in conjunction with the accompanying drawings, provides a detailed description of a simulation method for the microstructure evolution of porous and fragile particles during the crushing process, based on three embodiments and comparative experiments of the present invention.

[0039] Example 1:

[0040] The test subject in this embodiment is a cylindrical coarse carbonate particle (as shown in the actual photo). Figure 2 (As shown), the sample was taken from the strata of a construction site in Sanya City, Hainan Province, and is a common granular material in the local strata. For ease of understanding, a flowchart of the simulation method for the micropore structure evolution during the crushing process of porous and fragile particles in this embodiment is shown below. Figure 1 As shown.

[0041] according to Figure 1 , 2 It can be seen that the simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles involves the following steps:

[0042] 1. Sample preparation: Dry the sample in an oven at 110℃. When the moisture content is 0%, take out the dried sample, pass it through a sieve with a 2mm aperture, select particles with a diameter d>2mm, and use a fine brush to remove dust and impurities from the sample surface to obtain the sample to be tested.

[0043] 2. X-CT Scan: The sample is attached to the test stage of the high-resolution X-CT scanner with double-sided tape, so that the long axis of the sample is perpendicular to the test stage. The sample is scanned using the X-CT scanner with a scanning voltage of 120kV, a current of 150μA, and a resolution of 20μm to obtain a particle tomographic image data file in TIFF format. The resolution of the TIFF format file must exceed 1000 pixels.

[0044] 3. Image processing: Import the TIFF format file into AVIZO (2022 version) software, use the "Non-LocalMeans" algorithm to remove noise generated during the scanning process, use the "Volume Edit" function to cut the image area, remove the extra space voxels outside the solid interface, and obtain the analysis volume;

[0045] 4. 3D Reconstruction: Using the "Thresholding" tool on the analysis volume obtained in step 3, a threshold is set to separate the pores from the main particle body; the "Watershed" algorithm is used to further refine the boundaries of the pore region; and the "VolumeRendering" function is used to generate a 3D rendering model of the main particle body based on the processed image sequence, such as... Figure 3 As shown;

[0046] 5. Multi-scale simulation: To simulate the reduction in particle volume caused by particle breakage, the "Extract Subvolume" function is used to divide the initial 3D model from step 4 into volumes. Scale, the initial 3D model is divided into its volume Scale, further, the volumetric scale is The 3D model needs to be taken from a volume scale of Inside the 3D model, and so on, the volume scale is... The 3D model needs to be taken from a volume scale of Inside the 3D model, the volume scale is The 3D model needs to be taken from a volume scale of The interior of the 3D model. Four main 3D rendering models were created using the "Volume Rendering" function. The initial volume of the 3D main model is as follows: Figure 3 As shown;

[0047] 6. Pore Extraction: For the four different scale 3D rendering models of the main body obtained in step 5, pore thresholding is performed using the "Thresholding" tool. The 3D pore rendering model is then obtained using the "Volume Rendering" function. The 3D pore model under the initial volume is shown below. Figure 3 As shown;

[0048] 7. Porosity Analysis: Using the "Volume Fraction" module, calculate the total porosity of the model and the porosity of the two-dimensional slice. The calculation method is: Total porosity (φ) v ) is the voxel volume (V) occupied by each pore model in step 6. pore ), and the voxel volume (V) occupied by each main model in step 5. model The percentages between ) are obtained as follows: φ v The closer a value is to 1, the greater the porosity of the model; the closer it is to 0, the smaller the porosity of the model. The porosity (φ) of a two-dimensional slice is... s ) is determined by the pore area of ​​the slice (S) pore ), and the total area of ​​the slice (S) s The percentage of ) is obtained as follows: φ s The closer a value is to 1, the higher the face area ratio of the slice; the closer a value is to 0, the lower the face area ratio of the slice. This is used in the initial subject model. Total porosity (φ) on a volume scale v ), establish φ v The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 4 As shown; using the two-dimensional slice surface ratio (φ) s ) data, establish φ s The curve relationship between the number of 2D slices N (from bottom to top) is as follows: Figure 5 As shown;

[0049] 8. Pore Connectivity Analysis: Using the "Axis Connectivity" function, perform connectivity analysis on the 3D pore rendering models at each volume scale from step 6. If the "Data Info" in the analysis results shows "min-max:0...1", it indicates that the corresponding model has connected pores, i.e., connectivity exists; if the "Data Info" in the analysis results shows "min-max:0...0", it indicates that the corresponding model does not have connected pores, i.e., connectivity does not exist.

[0050] 9. Connectivity Porosity Analysis: If the 3D rendering model of the pores in step 8 contains connected pores, further, using the "Volume Rendering" function, the connectivity porosity is calculated based on the connected pore data volume obtained in step 8. The calculation method is: using the voxel volume (V) occupied by the 3D rendering model of the connected pores... tp ), and the voxel volume (V) occupied by each main 3D rendering model in step 5. model The percentage of interconnected porosity (Φ) is obtained by calculating the percentage of interconnected porosity. t ): Φ tThe closer a value is to 1, the stronger the connectivity of the model; the closer it is to 0, the weaker the connectivity of the model. Using the obtained initial main model... Viscous porosity at the volume scale (Φ) t ), establish Φ t The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 7 As shown;

[0051] 10. Pore Structure Parameter Analysis: Based on the 3D rendering models of pores at various volume scales obtained in step 6, the "Label Analysis" function is used to calculate the pore structure parameters according to the output results. The pore structure parameters are the volume of a single pore (V). pi ), equivalent diameter (D) e ), shape factor (G) p The calculation method is: pore volume (V) pi The equivalent diameter (D) is directly derived from the volume of the voxel occupied by a single pore; e ) by regard to the volume of a single pore (V pi The formula is derived from: Shape factor (G) p Then, it is obtained from the definition of pore cross-sectional area (S) and pore perimeter (P): Using the obtained initial main body model Equivalent diameter (D) on a volume scale e Establish the curve relationship between the average equivalent diameter of pores and different volumetric scales, as follows: Figure 6 As shown.

[0052] The results of this embodiment are shown below. Figure 3-7 As shown. In Figure 3 The three-dimensional model and pore model schematic diagram intuitively demonstrate the three-dimensional structure of cylindrical coarse calcium carbonate particles and its internal pore distribution characteristics. The results show that the simulation results are highly consistent with the actual camera images, indicating that the model obtained by the simulation method of this invention has good fidelity and intuitiveness. Figure 4 , Figure 6 The results show that the total porosity (Φ) during particle crushing is v The pore size is distributed at around 10%, with an average equivalent diameter (D) of pores. e The values ​​are distributed within the 10-15 μm range, and both show almost no change with the model's volumetric scale, indicating that the simulation method does not exhibit scale effects. Figure 5 Surface area ratio (Φ) of two-dimensional slices s The results show that the pores in the sample are relatively concentrated in the middle of the long axis, which is consistent with... Figure 3 The pore distribution shown in the figure is consistent with the accuracy of the invention. Figure 7Reflects the interconnected porosity (φ) t The porosity increases as the simulated volumetric scale decreases. This, combined with the numerous pores present in the sample's center, enhances connectivity due to the internal pores connecting to the external environment caused by particle breakage, resulting in a near-linear change in connectivity porosity. The simulation method of this invention demonstrates a high degree of accuracy and rationality in the multi-scale evolution of pore structure during the breakage of porous, fragile particles.

[0053] Example 2:

[0054] The test subject in this embodiment is a dendritic coral grain (actual photo shown). Figure 2 (As shown), the sample was taken from the strata of a construction site in Sanya City, Hainan Province, and is a common granular material in the local strata. For ease of understanding, the flowchart of this embodiment is shown below. Figure 1 As shown.

[0055] according to Figure 1 , 2 It can be seen that the simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles involves the following steps:

[0056] 1. Sample preparation: Dry the sample in an oven at 110℃. When the moisture content is 0%, take out the dried sample, pass it through a sieve with a 2mm aperture, select particles with a diameter d>2mm, and use a fine brush to remove dust and impurities from the sample surface to obtain the sample to be tested.

[0057] 2. X-CT Scan: The sample is attached to the test stage of the high-resolution X-CT scanner with double-sided tape, so that the long axis of the sample is perpendicular to the test stage. The sample is scanned using the X-CT scanner with a scanning voltage of 120kV, a current of 150μA, and a resolution of 20μm to obtain a particle tomographic image data file in TIFF format. The resolution of the TIFF format file must exceed 1000 pixels.

[0058] 3. Image processing: Import the TIFF format file into AVIZO (2022 version) software, use the "Non-LocalMeans" algorithm to remove noise generated during the scanning process, use the "Volume Edit" function to cut the image area, remove the extra space voxels outside the solid interface, and obtain the analysis volume;

[0059] 4. 3D Reconstruction: Using the "Thresholding" tool on the analysis volume obtained in step 3, a threshold is set to separate the pores from the main particle body; the "Watershed" algorithm is used to further refine the boundaries of the pore region; and the "VolumeRendering" function is used to generate a 3D rendering model of the main particle body based on the processed image sequence, such as... Figure 3 As shown;

[0060] 5. Multi-scale simulation: To simulate the reduction in particle volume caused by particle breakage, the "Extract Subvolume" function is used to divide the initial 3D model from step 4 into volumes. Scale, the initial 3D model is divided into its volume Scale, further, the volumetric scale is The 3D model needs to be taken from a volume scale of Inside the 3D model, and so on, the volume scale is... The 3D model needs to be taken from a volume scale of Inside the 3D model, the volume scale is The 3D model needs to be taken from a volume scale of The interior of the 3D model. Four main 3D rendering models were created using the "Volume Rendering" function. The initial volume of the 3D main model is as follows: Figure 3 As shown;

[0061] 6. Pore Extraction: For the four different scale 3D rendering models of the main body obtained in step 5, pore thresholding is performed using the "Thresholding" tool. The 3D pore rendering model is then obtained using the "Volume Rendering" function. The 3D pore model under the initial volume is shown below. Figure 3 As shown;

[0062] 7. Porosity Analysis: Using the "Volume Fraction" module, calculate the total porosity of the model and the porosity of the two-dimensional slice. The calculation method is: Total Porosity (Φ v ) is the voxel volume (V) occupied by each pore model in step 6. pore ), and the voxel volume (V) occupied by each main model in step 5. model The percentages between ) are obtained as follows: Φ v The closer a value is to 1, the greater the porosity of the model; the closer it is to 0, the smaller the porosity of the model. The porosity (Φ) of a two-dimensional slice is... s ) is determined by the pore area of ​​the slice (S) pore ), and the total area of ​​the slice (S) s The percentage of ) is obtained as follows: Φ s The closer a value is to 1, the higher the face area ratio of the slice; the closer a value is to 0, the lower the face area ratio of the slice. This is used in the initial subject model. Total porosity (Φ) on a volume scale v ), establish Φ v The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 4 As shown; using the two-dimensional slice surface ratio (Φ)s ) data, establish Φ s The curve relationship between the number of 2D slices N (from bottom to top) is as follows: Figure 5 As shown;

[0063] 8. Pore Connectivity Analysis: Using the "Axis Connectivity" function, perform connectivity analysis on the 3D pore rendering models at each volume scale from step 6. If the "Data Info" in the analysis results shows "min-max:0...1", it indicates that the corresponding model has connected pores, i.e., connectivity exists; if the "Data Info" in the analysis results shows "min-max:0...0", it indicates that the corresponding model does not have connected pores, i.e., connectivity does not exist.

[0064] 9. Connectivity Porosity Analysis: If the 3D rendering model of the pores in step 8 contains connected pores, further, using the "Volume Rendering" function, the connectivity porosity is calculated based on the connected pore data volume obtained in step 8. The calculation method is: using the voxel volume (V) occupied by the 3D rendering model of the connected pores... tp ), and the voxel volume (V) occupied by each main 3D rendering model in step 5. model The percentage of interconnected porosity (Φ) is obtained by calculating the percentage of interconnected porosity. t ): Φ t The closer a value is to 1, the stronger the connectivity of the model; the closer it is to 0, the weaker the connectivity of the model. Using the obtained initial main model... Viscous porosity at the volume scale (Φ) t ), establish Φ t The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 7 As shown;

[0065] 10. Pore Structure Parameter Analysis: Based on the 3D rendering models of pores at various volume scales obtained in step 6, the "Label Analysis" function is used to calculate the pore structure parameters according to the output results. The pore structure parameters are the volume of a single pore (V). pi ), equivalent diameter (D) e ), shape factor (G) p The calculation method is: pore volume (V) pi The equivalent diameter (D) is directly derived from the volume of the voxel occupied by a single pore; e ) by regard to the volume of a single pore (V pi The formula is derived from: Shape factor (G) p Then, it is obtained from the definition of pore cross-sectional area (S) and pore perimeter (P): Using the obtained initial main body model Equivalent diameter (D) on a volume scale e Establish the curve relationship between the average equivalent diameter of pores and different volumetric scales, as follows: Figure 6 As shown.

[0066] The results of this embodiment are shown below. Figure 3 , 4 As shown in Figures 5, 6, and 7. Figure 3 The three-dimensional model and pore model schematic diagrams intuitively demonstrate the three-dimensional structure of dendritic coral coarse grains and the distribution characteristics of their internal pores. The results show that the simulation results are highly consistent with the actual camera images, indicating that the model obtained by the simulation method of this invention has good fidelity and intuitiveness. Figure 4 , Figure 6 The results show that the total porosity (φ) during particle crushing is v The pore size is distributed at around 10%, with an average equivalent diameter (D) of pores. e The values ​​are distributed within the 10-15 μm range, and both show almost no change with the model's volumetric scale, indicating that the simulation method does not exhibit scale effects. Figure 5 Two-dimensional slice surface ratio (φ) s The results showed that the pore distribution of the sample was irregular, with multiple peaks, which was consistent with... Figure 3 The distribution of pores shown in the figure is consistent with the distribution along its branches, reflecting the accuracy of the invention. Figure 7 Reflects the interconnected porosity (φ) t The pore size increases as the simulated volumetric scale decreases, and the numerous pores present on the sample branches, due to particle breakage, connect the internal pores with the external environment, thus improving connectivity. The simulation method of this invention yields a highly accurate and reasonable multi-scale evolution law of pore structure during the breakage of porous and fragile particles.

[0067] Example 3:

[0068] The test subject in this embodiment is a coarse-grained calcareous sand in a dissolved state (actual photo shown). Figure 2 (As shown), the sample was taken from the strata of a construction site in Sanya City, Hainan Province, and is a common granular material in the local strata. For ease of understanding, the flowchart of this embodiment is shown below. Figure 1 As shown.

[0069] according to Figure 1 , 2 It can be seen that the simulation method for the evolution of micropore structure during the crushing process of porous and fragile particles involves the following steps:

[0070] 1. Sample preparation: Dry the sample in an oven at 110℃. When the moisture content is 0%, take out the dried sample, pass it through a sieve with a 2mm aperture, select particles with a diameter d>2mm, and use a fine brush to remove dust and impurities from the sample surface to obtain the sample to be tested.

[0071] 2. X-CT Scan: The sample is attached to the test stage of the high-resolution X-CT scanner with double-sided tape, so that the long axis of the sample is perpendicular to the test stage. The sample is scanned using the X-CT scanner with a scanning voltage of 120kV, a current of 150μA, and a resolution of 20μm to obtain a particle tomographic image data file in TIFF format. The resolution of the TIFF format file must exceed 1000 pixels.

[0072] 3. Image processing: Import the TIFF format file into AVIZO (2022 version) software, use the "Non-LocalMeans" algorithm to remove noise generated during the scanning process, use the "Volume Edit" function to cut the image area, remove the extra space voxels outside the solid interface, and obtain the analysis volume;

[0073] 4. 3D Reconstruction: Using the "Thresholding" tool on the analysis volume obtained in step 3, a threshold is set to separate the pores from the main particle body; the "Watershed" algorithm is used to further refine the boundaries of the pore region; and the "VolumeRendering" function is used to generate a 3D rendering model of the main particle body based on the processed image sequence, such as... Figure 3 As shown;

[0074] 5. Multi-scale simulation: To simulate the reduction in particle volume caused by particle breakage, the "Extract Subvolume" function is used to divide the initial 3D model from step 4 into volumes. Scale, the initial 3D model is divided into its volume Scale, further, the volumetric scale is The 3D model needs to be taken from a volume scale of Inside the 3D model, and so on, the volume scale is... The 3D model needs to be taken from a volume scale of Inside the 3D model, the volume scale is The 3D model needs to be taken from a volume scale of The interior of the 3D model. Four main 3D rendering models were created using the "Volume Rendering" function. The initial volume of the 3D main model is as follows: Figure 3 As shown;

[0075] 6. Pore Extraction: For the four different scale 3D rendering models of the main body obtained in step 5, pore thresholding is performed using the "Thresholding" tool. The 3D pore rendering model is then obtained using the "Volume Rendering" function. The 3D pore model under the initial volume is shown below. Figure 3 As shown;

[0076] 7. Porosity Analysis: Using the "Volume Fraction" module, calculate the total porosity of the model and the porosity of the two-dimensional slice. The calculation method is: Total porosity (φ) v ) is the voxel volume (V) occupied by each pore model in step 6. pore ), and the voxel volume (V) occupied by each main model in step 5. model The percentages between ) are obtained as follows: φ v The closer a value is to 1, the greater the porosity of the model; the closer it is to 0, the smaller the porosity of the model. The porosity (Φ) of a two-dimensional slice is... s ) is determined by the pore area of ​​the slice (S) pore ), and the total area of ​​the slice (S) s The percentage of ) is obtained as follows: Φ s The closer a value is to 1, the higher the face area ratio of the slice; the closer a value is to 0, the lower the face area ratio of the slice. This is used in the initial subject model. Total porosity (φ) on a volume scale v ), establish Φ v The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 4 As shown; using the two-dimensional slice surface ratio (φ) s ) data, establish Φ s The curve relationship between the number of 2D slices N (from bottom to top) is as follows: Figure 5 As shown;

[0077] 8. Pore Connectivity Analysis: Using the "Axis Connectivity" function, perform connectivity analysis on the 3D pore rendering models at each volume scale from step 6. If the "Data Info" in the analysis results shows "min-max:0...1", it indicates that the corresponding model has connected pores, i.e., connectivity exists; if the "Data Info" in the analysis results shows "min-max:0...0", it indicates that the corresponding model does not have connected pores, i.e., connectivity does not exist.

[0078] 9. Connectivity Porosity Analysis: If the 3D rendering model of the pores in step 8 contains connected pores, further, using the "Volume Rendering" function, the connectivity porosity is calculated based on the connected pore data volume obtained in step 8. The calculation method is: using the voxel volume (V) occupied by the 3D rendering model of the connected pores... tp ), and the voxel volume (V) occupied by each main 3D rendering model in step 5. model The percentage of interconnected porosity (Φ) is obtained by calculating the percentage of interconnected porosity. t ): Φ t The closer a value is to 1, the stronger the connectivity of the model; the closer it is to 0, the weaker the connectivity of the model. Using the obtained initial main model... Viscous porosity at the volume scale (Φ) t ), establish φ t The curve relationship between different volume scales and their corresponding values ​​is as follows: Figure 7 As shown;

[0079] 10. Pore Structure Parameter Analysis: Based on the 3D rendering models of pores at various volume scales obtained in step 6, the "Label Analysis" function is used to calculate the pore structure parameters according to the output results. The pore structure parameters are the volume of a single pore (V). pi ), equivalent diameter (D) e ), shape factor (G) p The calculation method is: pore volume (V) pi The equivalent diameter (D) is directly derived from the volume of the voxel occupied by a single pore; e ) by regard to the volume of a single pore (V pi The formula is derived from: Shape factor (G) p Then, it is obtained from the definition of pore cross-sectional area (S) and pore perimeter (P): Using the obtained initial main body model Equivalent diameter (D) on a volume scale e Establish the curve relationship between the average equivalent diameter of pores and different volumetric scales, as follows: Figure 6 As shown.

[0080] The results of this embodiment are shown below. Figure 3 , 4 As shown in Figures 5, 6, and 7. Figure 3 The three-dimensional model and pore model schematic diagram intuitively demonstrate the three-dimensional structure of the dissolved calcareous sand coarse grains and its internal pore distribution characteristics. The results show that the simulation results are highly consistent with the actual camera images, indicating that the model obtained by the simulation method of this invention has good fidelity and intuitiveness. Figure 4 , Figure 6 The results show that the total porosity (φ) during particle crushing is v ), average equivalent diameter of pores (D) e The increase in the simulated volumetric scale is due to the presence of large-area interconnected pores in the center of the sample, which does not indicate the absence of scale effects in the simulation method; on the contrary, it is consistent with the internal structure. Figure 5 Surface area ratio (Φ) of two-dimensional slices s It shows the characteristic of concentrated distribution in the central part, and Figure 3 The pores shown in the figure are mainly concentrated in the middle part, which is consistent with the accuracy of the invention. Figure 7 Reflects the interconnected porosity (φ) t The pore size increases as the simulated volume scale decreases, and the numerous pores present in the middle of the sample, due to particle breakage, connect the internal pores with the outside, thus improving connectivity. The simulation method of this invention yields a highly accurate and reasonable multi-scale evolution law of pore structure during the breakage of porous and fragile particles.

[0081] Comparative example:

[0082] The comparative experiment used sheet-like coarse-grained carbonate material, taken from the strata of a construction site in Yulin, Sanya City, Hainan Province, which is a common granular material in the local strata. The comparative experiment was conducted using scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP), divided into two groups: a comparative experiment (SEM) and a comparative experiment (MIP). Image digitization technology (existing method) was used to test the total porosity and equivalent pore radius distribution of porous and fragile rock and soil granular materials. This method is based on the "Microscopic Experimental Study on Pore Characteristics of Coral Debris Particles" (Zhang Yu, Ding Xuanming, Peng Yu, Jiang Chunyong, Microscopic Experimental Study on Pore Characteristics of Coral Debris Particles, Journal of Disaster Prevention and Mitigation Engineering, 2020, 41:3). The steps of this method are briefly summarized as follows:

[0083] 1. Dry the flake-shaped carbonate coarse particles in an oven at 110℃. When the moisture content is 0%, take out the dried sample and use a fine brush to remove the dust and impurities from the surface of the sample to obtain the sample to be tested.

[0084] 2. Using the displacement method, the volume difference of the sample before and after immersion in the graduated cylinder is used, combined with the density parameter of water (ρ = 1 g / cm³). 3 ), to obtain the total volume of the sample (V) v );

[0085] 3. After repeating step 1, perform scanning electron microscopy (SEM) on the sample with a resolution of 1.0 nm and a magnification of 12 to 10,000 times. Use non-local mean filtering to retain effective image information and remove noise interference to the greatest extent.

[0086] 4. Utilizing image digitization technology, threshold segmentation and binarization methods are employed to extract internal pore information, obtaining a clear pore distribution map. Based on scanning imaging at different magnifications (500–4000x), the pore diameter (D) of the surface micro-circular holes is determined. e ) and two-dimensional slice face ratio (Φ s Quantitative measurement is performed. The principle is: the aperture ratio (Φ) of a two-dimensional slice... s ) is determined by the pore area of ​​the slice (S) pore ) and total slice area (S) s The percentage of ) is obtained as follows: Φ s The closer a value is to 1, the higher the face ratio of the slice; the closer a value is to 0, the lower the face ratio of the slice.

[0087] 5. Through the mercury intrusion porosimetry (MIP) test, pressure is gradually increased to allow mercury to penetrate the pores of the particles. When the surface tension of the mercury is in equilibrium with the applied pressure, the volume of pores penetrated by the mercury is a function of the pressure, thus yielding the pore volume (V). pore The specific calculation formula is: P×r=-2σcosθ, where P is the applied pressure, r is the pore radius, σ is the surface tension coefficient of mercury (taken as 0.48N / m), and θ is the contact angle of mercury with the material (taken as 140°). Further, the total porosity (Φ) is obtained. v ): Φ v The closer a value is to 1, the greater the porosity of the model; the closer a value is to 0, the smaller the porosity of the model.

[0088] 6. Clean the sample and crush it in three stages, keeping the crushing size as small as possible relative to its volume. To determine the scale, repeat steps 1-5 after each smashing and obtain the corresponding data.

[0089] 7. Utilize the obtained initial main body model Total porosity (Φ) on a volume scale v ), 2D slice face rate (Φ) s ), aperture (D) e ), establish Φ v Φ s D e The curves showing the change in particle volume scale are as follows: Figure 4 , 5 As shown in Figure 6.

[0090] The results of the comparative experiment are shown in Figure 4 , 5 As shown in Figure 6. Figure 4 , Figure 6 Total porosity (Φ) v), average pore diameter (D) e The size varies with the volumetric size of the sample, but no non-uniform porosity was found in the sample during the crushing process. Therefore, this is obviously inconsistent with the size effect, indicating that the analysis is unreasonable. Figure 5 Two-dimensional slice face ratio (Φ) s This method can only display data at four volumetric scales and cannot be used to explore the spatial distribution characteristics of pores in the sample. Comparative analysis shows that the simulation method of this invention can construct the sample morphology and pore structure in three dimensions. The resulting model has good reproducibility and intuitiveness, and the multi-scale evolution law of pore structure during the breakage of porous and fragile particles obtained by the simulation method has high accuracy and rationality.

Claims

1. A method for simulating the evolution of micropore structure during the crushing process of porous and fragile particles, characterized in that, Includes the following steps: 1) Sample preparation: Dry the sample at 110℃, sieve it, select porous soil particles with a diameter d>2mm, remove surface dust and impurities, and obtain the sample to be tested; 2) X-CT scan: The sample to be tested obtained in step 1) is scanned using an X-CT scanner with a scanning voltage of 120kV, a current of 150μA, and a resolution of 20μm to obtain particle tomographic image data files. 3) Image processing: Import the particle tomographic image data file obtained in step 2) into AVIZO software to obtain the analysis volume; use the "Non-Local Means" algorithm to remove noise generated during the scanning process, and use the "Volume Edit" function to cut the image area and remove the extra space voxels outside the solid interface; 4) 3D Reconstruction: The analysis volume obtained in step 3) is processed to generate a 3D rendering model of the particle body; the "Thresholding" tool is used to set a threshold to separate the pores from the particle body; the "Watershed" algorithm is used to further refine the boundary of the pore region to obtain the processed image sequence; the "Volume Rendering" function is used to generate a 3D rendering model of the particle body based on the processed image sequence. 5) Multi-scale simulation: To simulate the reduction in particle volume caused by particle breakage, the initial 3D model in step 4) is divided into progressively smaller volume scales using the "Extract Subvolume" function, and multiple 3D rendering models of different scales are created using the "Volume Rendering" function. 6) Pore extraction: For the multiple 3D rendering models of the main body at different scales obtained in step 5), the "Thresholding" tool is used to perform pore threshold segmentation, and the "Volume Rendering" function is used to obtain the 3D pore rendering model. 7) Porosity Analysis: Using the "Volume Fraction" module, calculate the total porosity Φ of the model. v Face ratio of two-dimensional slices; 8) Pore connectivity analysis: Using the "Axis Connectivity" function, perform connectivity analysis on the 3D rendering models of pores at each volume scale in step 6); 9) Connectivity porosity analysis: If the 3D rendering model of the pores in step 8) contains connected pores, the connectivity porosity is calculated based on the connected pore data volume obtained in step 8) using the "VolumeRendering" function. 10) Pore structure parameter analysis: Based on the 3D rendering models of pores at various volume scales obtained in step 6), the "LabelAnalysis" function is used to calculate the pore structure parameters according to the output results.

2. The simulation method according to claim 1, characterized in that, In step 1), the particle size of the porous soil and rock material is 20-40 mm.

3. The simulation method according to claim 1, characterized in that, In step 2), the X-CT scanner has a resolution of 10-30 μm.

4. The simulation method according to claim 1, characterized in that, In step 5), the step-by-step scale division method is as follows: the initial 3D model is divided into its volume... Scale, i.e. Scale, further, the volumetric scale is Scale, i.e. A three-dimensional model of this scale needs to be derived from a volumetric scale. Within the 3D model, and so on, the volume scale is... Right now A three-dimensional model of this scale needs to be derived from a volumetric scale. Inside the 3D model, the volume scale is Right now The 3D model is taken from a volume scale of Inside the three-dimensional model.

5. The simulation method according to claim 1, characterized in that, The calculation method for step 7) is: total porosity Φ v It is the voxel volume V occupied by each pore model in step 6). pore And the voxel volume V occupied by each main model in step 5). model The percentages between them are obtained as follows: Two-dimensional slice face ratio φ s It is determined by the pore area S of the slice pore and the total area S of the slice s The percentage is obtained as follows:

6. The simulation method according to claim 4, characterized in that, Step 7) utilizes the obtained initial main body model Total porosity Φ on a volume scale v Establish Φ v The curves showing the relationship between volume at different scales were used to obtain the influence of multi-scale particle breakage on total porosity. For homogeneous granular materials, under ideal conditions, Φ at the volume scale v Consistent.

7. The simulation method according to claim 6, characterized in that, Step 9) utilizes the obtained initial main body model Viable porosity φ at the volume scale t Establish a connected porosity φ t The relationship between curves and volumes at different scales was obtained to determine the changes in pore structure connectivity during multi-scale particle fragmentation. The calculation method involves using the voxel volume V occupied by the 3D rendering model of the connected pores. tp And the voxel volume V occupied by each main 3D rendering model in step 5. model The percentage of interconnected porosity φ is obtained. t :

8. The simulation method according to claim 7, characterized in that, The pore structure parameter in step 10) is the volume of a single pore, V. pi Equivalent diameter D e Shape factor G p The calculation method is as follows: pore volume V pi The equivalent diameter D is derived directly from the volume of the voxel occupied by a single pore. e From the perspective of the volume of a single pore V pi The formula yields: Shape factor G p Then, based on the definitions of pore cross-sectional area S and pore perimeter P, we can obtain:

9. The simulation method according to claim 8, characterized in that, Using the single pore volume V obtained in step 10) pi Equivalent diameter D e Shape factor G p Establish frequency distribution diagrams of pore volume and equivalent diameter, and the average equivalent diameter D. e Shape factor G p Its variation curves with volumes at various scales.

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