Method for reconstructing three-dimensional structure of gradient porous medium based on random generation method

Abstract

The application provides a method for reconstructing a three-dimensional structure of a gradient porous medium based on a random generation method, comprising: porous medium generation, including: setting a preset size, porosity and solid core growth rate; obtaining N two-dimensional porous medium images according to the preset size, porosity and solid core growth rate; respectively performing median filter smoothing processing on the N two-dimensional porous medium images to obtain a picture group; the porous medium generation is performed at least three times, and the porosity is different each time, and a first picture group, a second picture group and a third picture group are obtained correspondingly; and the first picture group, the second picture group and the third picture group are stacked according to the generation order to obtain a three-dimensional structure of a gradient porous medium. Different porosity two-dimensional porous medium images are respectively constructed by a method combining random generation and cluster segmentation, and then the three-dimensional structure of the porous medium with a specified porosity gradient is reconstructed by stacking the different porosity two-dimensional porous medium images.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional structure reconstruction technology, and more particularly to a method for reconstructing three-dimensional structures of gradient porous media based on a random generation method. Background Technology

[0002] Porous media are media composed of multiple pores or capillaries, typically consisting of solids, liquids, or gases. These pores or capillaries can be continuous or discontinuous, and their size and shape can vary. Porous media have wide applications in nature and industrial production, such as soil, rocks, sponges, filters, adsorbents, and thermal insulation materials. In porous media, the movement of fluids (liquids or gases) is influenced by various factors such as pore size, shape, and connectivity, resulting in highly complex flow and mass transfer processes. Research on porous media involves many fields, including soil mechanics, rock physics, groundwater dynamics, petroleum engineering, chemical engineering, and environmental science. In numerical simulation studies in these fields, structural reconstruction of the corresponding porous media is an essential part of physical modeling.

[0003] However, existing technologies for three-dimensional reconstruction of porous media face numerous technical and cost challenges when using ion beam etching and scanning electron microscopy (FIB-SEM) or computed tomography (CT) to reconstruct the research object. For example:

[0004] 1. Resolution limitations: The details and microstructures of porous media are extremely complex, while FIB-SEM and CT techniques have limited resolution and cannot capture all the microstructure information, especially the details at the nanoscale.

[0005] 2. Limited imaging depth: FIB-SEM and CT imaging have limited depth, making it impossible to effectively image and reconstruct deep pores and details.

[0006] 3. FIB-SEM requires sample cutting and preparation, while CT requires sample scanning. The preparation and scanning process is relatively difficult for some porous materials.

[0007] 4. Requires complex data processing and analysis: The imaging data of FIB-SEM and CT are relatively large, requiring complex data processing and analysis to obtain useful information.

[0008] 5. Expensive instruments and high maintenance costs: FIB-SEM and CT are expensive instruments that require high maintenance costs and professional technicians for operation and maintenance, and sample characterization costs are also very high.

[0009] In view of this, the present invention provides a method for reconstructing three-dimensional structures of gradient porous media based on a random generation method. Summary of the Invention

[0010] To address the shortcomings of existing 3D reconstruction methods for porous media, this invention provides a method for reconstructing the 3D structure of porous media based on a random generation method. This invention primarily utilizes a combination of random generation and cluster segmentation to construct 2D porous media images with different porosities. These images are then imported into specialized software, 3D Slicer, for processing, thereby reconstructing a porous media structure with a specified pore gradient.

[0011] The technical means employed in this invention are as follows:

[0012] This invention provides a method for reconstructing three-dimensional structures of gradient porous media based on a random generation method, comprising:

[0013] The porous medium generation process includes: setting a preset size, porosity, and solid nucleus growth rate; obtaining N two-dimensional porous medium images based on the preset size, porosity, and solid nucleus growth rate; and performing median filtering smoothing on each of the N two-dimensional porous medium images to obtain an image group.

[0014] The porous medium is generated at least three times, with a different porosity each time, resulting in a first image group, a second image group, and a third image group.

[0015] The first image group, the second image group, and the third image group are stacked in the order of generation to obtain a gradient porous medium three-dimensional structure.

[0016] Furthermore, the two-dimensional porous medium pattern is obtained in the following manner:

[0017] An image and M solid particles are generated according to the preset size, and the solid particles are randomly used to fill the image.

[0018] Select any position in the image and determine whether the random number at that position is less than the solid nucleus growth rate;

[0019] If less than, the position is set to 1; if greater than or equal to, the position is set to 0.

[0020] The Hopcroft-Karp algorithm was used to segment the non-1 positions in the image, resulting in K clusters;

[0021] Traverse the K clusters, select any pixel in the cluster, and determine whether the pixel satisfies the following conditions: its upper, lower, left and right ends are all adjacent to the solid particles, and there are solid particles that overlap with the pixel.

[0022] If the condition is not met, the pixel location will be filled with the solid particles;

[0023] Determine whether the proportion of solid particles in the image meets the porosity requirement;

[0024] If the porosity is satisfied, the image is filled and converted into the two-dimensional porous medium image.

[0025] Furthermore, it also includes:

[0026] If the porosity is not satisfied, select any of the solid particles and determine whether there are any unfilled positions around it that meet the conditions for filling the solid particles, and the random number of the unfilled positions is less than the solid core growth rate.

[0027] If there are unfilled locations around the unfilled location that meet the conditions for filling the solid particles, and the random number of the unfilled location is less than the solid nucleus growth rate, determine whether the unfilled location is adjacent to the solid particles.

[0028] If the location to be filled is adjacent to the solid particle, the location to be filled is filled with the solid particle, the image is filled, and it is converted into the two-dimensional porous medium image.

[0029] Furthermore, the two-dimensional porous medium diagram is obtained by programming using Matlab software.

[0030] Furthermore, following the generation order of the first image group, the second image group, and the third image group, the first image group, the second image group, and the third image group are imported into 3D slicer software for stacking processing to obtain the three-dimensional structure of the gradient porous medium.

[0031] Furthermore, the N two-dimensional porous medium images are smoothed by median filtering using the medfilt2 function.

[0032] Further, N two-dimensional porous medium maps are obtained based on the preset size, the porosity, and the solid nucleus growth rate, including:

[0033] A for loop is used to generate N images of the two-dimensional porous medium.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] 1. The method for reconstructing the three-dimensional structure of gradient porous media based on random generation provided by this invention uses Matlab to construct two-dimensional porous media. The algorithm is simple and the pore size can be adjusted.

[0036] 2. The method for reconstructing the three-dimensional structure of gradient porous media based on random generation provided by the present invention uses 3DSlicer to stack the generated two-dimensional porous media, which can not only control the porosity of each gradient porous media, but also control the porosity of the entire gradient porous media.

[0037] 3. The method for reconstructing three-dimensional structures of gradient porous media based on random generation provided by this invention adopts a simple two-dimensional stacking method, saves a lot of computational resources, and reduces the cost of modeling. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a method for reconstructing three-dimensional structures of gradient porous media based on a random generation method, provided by the present invention.

[0040] Figure 2 This is a diagram of a two-dimensional porous medium with a porosity of 40% generated by the present invention.

[0041] Figure 3 This is a diagram of a two-dimensional porous medium with a porosity of 30% generated by the present invention.

[0042] Figure 4 This is a diagram of a two-dimensional porous medium with a porosity of 20% generated by the present invention.

[0043] Figure 5 This is a three-dimensional structural diagram of a gradient porous medium generated according to the present invention.

[0044] Figure 6 This is a three-dimensional structure diagram of another gradient porous medium generated by the present invention.

[0045] Figure 7 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention.

[0046] Figure 8 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention.

[0047] Figure 9 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention.

[0048] Figure 10 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention.

[0049] Figure 11 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0052] Combination Figures 1 to 5 , Figure 1 This is a flowchart of a method for reconstructing three-dimensional structures of gradient porous media based on a random generation method provided by the present invention. Figure 2 This is a diagram of a two-dimensional porous medium with a porosity of 40% generated by the present invention. Figure 3 This is a diagram of a two-dimensional porous medium with a porosity of 30% generated by the present invention. Figure 4 This is a diagram of a two-dimensional porous medium with a porosity of 20% generated by the present invention. Figure 5 This invention generates a three-dimensional structure diagram of a gradient porous medium, illustrating a specific embodiment of the method for reconstructing the three-dimensional structure of a gradient porous medium based on a random generation method provided by this invention, including:

[0053] The medium generation process includes: setting preset dimensions, porosity, and solid nucleus growth rate; obtaining N two-dimensional porous medium images based on the preset dimensions, porosity, and solid nucleus growth rate; and performing median filtering smoothing on each of the N two-dimensional porous medium images to obtain an image set.

[0054] The medium generation process was performed at least three times, with a different porosity each time, resulting in the first image group, the second image group, and the third image group.

[0055] The first, second, and third image groups were stacked to obtain a gradient porous medium three-dimensional structure.

[0056] Understandable Figure 2 This is just one two-dimensional image of a porous medium in the first image set, where the porosity is 40%. Figure 3 This is just one two-dimensional image of a porous medium from the second image set, where the porosity is 30%. Figure 4 This is just one two-dimensional porous medium image from the third image group, which has a porosity of 40%. The images are arranged in the order of the first image group first, the second image group in the middle, and the third image group last. After stacking, they form a gradient porous medium three-dimensional structure.

[0057] In some optional embodiments, N two-dimensional porous medium maps are obtained based on preset dimensions, porosity, and solid core growth rate, including:

[0058] A for loop is used to generate N two-dimensional porous medium maps.

[0059] In some optional embodiments, the two-dimensional porous medium pattern is obtained as follows:

[0060] Generate an image and M solid particles based on a preset size, and randomly fill the image with the solid particles.

[0061] Select any position in the image and determine whether the random number at that position is less than the solid nucleus growth rate. The random number refers to a value randomly generated at that position.

[0062] If less than, the position is set to 1; if greater than or equal to, the position is set to 0.

[0063] The Hopcroft-Karp algorithm (HK algorithm) is used to segment the positions of 0 in the image to obtain K clusters;

[0064] During segmentation, the bwlabel function is called to invert the filled solid particles and mark the connected regions, resulting in a label matrix and the number of clusters (num_clusters).

[0065] Traverse K clusters, select any pixel in the cluster, and determine whether the pixel satisfies the following conditions: its upper, lower, left and right ends are all adjacent to solid particles, and there are solid particles that overlap with the pixel.

[0066] Specifically, it determines whether a pixel satisfies the condition that its upper, lower, left, and right ends are all adjacent to solid particles, that is, whether the generated solid particles are surrounded by solid points.

[0067] If the condition is not met, fill the pixel location with solid particles;

[0068] Determine whether the proportion of solid particles in the image meets the porosity requirement;

[0069] Wherein, 1 represents the proportion of solid particles; 0 represents the proportion of pores.

[0070] If the porosity requirement is met, the image is filled and converted into a two-dimensional porous medium image.

[0071] If the porosity requirement is not met, select any solid particle and determine whether it has a location around it that meets the conditions for filling solid particles, and the random number of the location to be filled is less than the solid core growth rate.

[0072] Specifically, it is determined whether there are unfilled positions around the generated solid particles that meet the conditions for filling solid particles.

[0073] If there are unfilled locations around it that meet the conditions for filling solid particles, and the random number of the unfilled locations is less than the solid core growth rate, determine whether the unfilled location is adjacent to the solid particles.

[0074] If the area to be filled is adjacent to a solid particle, the area to be filled will be filled with solid particles, the image will be filled, and it will be converted into a two-dimensional porous medium image.

[0075] Specifically, the two-dimensional porous medium diagram was obtained by programming using Matlab software.

[0076] In some optional embodiments, the first image group, the second image group, and the third image group are imported into the 3D Slicer software (3D Slicer) for stacking processing in the order of generation of the first image group, the second image group, and the third image group to obtain a gradient porous medium three-dimensional structure.

[0077] In some optional embodiments, the N two-dimensional porous media images are smoothed by median filtering using the medfilt2 function.

[0078] In some alternative embodiments, refer to Figures 6 to 10 , Figure 6 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Figure 7 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Figure 8 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Figure 9 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Figure 10 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention. Figure 11 This is another three-dimensional structure diagram of a gradient porous medium generated by the present invention.

[0079] Understandable Figures 6 to 11 The three-dimensional structure diagrams of the gradient porous media shown all include three gradients, from top to bottom: the first gradient, the second gradient, and the third gradient. Figure 6 The diagram only shows the porosity of the first gradient as 30%, the second gradient as 20%, and the third gradient as 10%; Figure 7 The diagram only shows the porosity of the first gradient as 40%, the second gradient as 30%, and the third gradient as 20%; Figure 8 The diagram only shows the porosity of the first gradient as 50%, the second gradient as 40%, and the third gradient as 30%; Figure 9 The diagram only shows the porosity of the first gradient as 60%, the second gradient as 50%, and the third gradient as 40%; Figure 10 The diagram only shows the porosity of the first gradient as 70%, the second gradient as 60%, and the third gradient as 50%; Figure 11 The diagram only shows the porosity of the first gradient as 80%, the second gradient as 70%, and the third gradient as 60%.

[0080] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0081] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reconstructing the three-dimensional structure of gradient porous media based on a random generation method, characterized in that, include: The porous medium generation process includes: setting a preset size, porosity, and solid nucleus growth rate; obtaining N two-dimensional porous medium images based on the preset size, porosity, and solid nucleus growth rate; and performing median filtering smoothing on each of the N two-dimensional porous medium images to obtain an image group. The two-dimensional porous medium diagram is obtained as follows: An image and M solid particles are generated according to the preset size, and the solid particles are randomly used to fill the image. Select any position in the image and determine whether the random number at that position is less than the solid nucleus growth rate; If less than, the position is set to 1; if greater than or equal to, the position is set to 0. The Hopcroft-Karp algorithm was used to segment the locations of 0 in the image, resulting in K clusters; Traverse the K clusters, select any pixel in the cluster, and determine whether the pixel satisfies the following conditions: its upper, lower, left and right ends are all adjacent to the solid particles, and there are solid particles that overlap with the pixel. If the condition is not met, the pixel location will be filled with the solid particles; Determine whether the proportion of solid particles in the image meets the porosity requirement; If the porosity is satisfied, the image is filled and converted into the two-dimensional porous medium image; If the porosity is not satisfied, select any of the solid particles and determine whether there are any unfilled positions around it that meet the conditions for filling the solid particles, and the random number of the unfilled positions is less than the solid core growth rate. If there are unfilled locations around the unfilled location that meet the conditions for filling the solid particles, and the random number of the unfilled location is less than the solid core growth rate, determine whether the unfilled location is adjacent to the solid particles. If the position to be filled is adjacent to the solid particle, the position to be filled is filled with the solid particle, the image is filled and converted into the two-dimensional porous medium image; The porous medium is generated at least three times, with a different porosity each time, resulting in a first image group, a second image group, and a third image group. The first image group, the second image group, and the third image group are stacked in the order of generation to obtain a gradient porous medium three-dimensional structure.

2. The method for reconstructing three-dimensional structures of gradient porous media based on random generation according to claim 1, characterized in that, The two-dimensional porous medium diagram was obtained by programming using Matlab software.

3. The method for reconstructing three-dimensional structures of gradient porous media based on random generation according to claim 1, characterized in that, Following the generation order of the first image group, the second image group, and the third image group, the first image group, the second image group, and the third image group are imported into 3D slicer software for stacking processing to obtain the three-dimensional structure of the gradient porous medium.

4. The method for reconstructing three-dimensional structures of gradient porous media based on random generation according to claim 1, characterized in that, The N two-dimensional porous medium images are smoothed by median filtering using the medfilt2 function.

5. The method for reconstructing three-dimensional structures of gradient porous media based on random generation according to claim 1, characterized in that, Based on the preset size, the porosity, and the solid nucleus growth rate, N two-dimensional porous medium maps are obtained, including: A for loop is used to generate N two-dimensional porous medium images.

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