Quantitative evaluation method for honeycomb aluminum structure against high-speed impact of space debris

Abstract

The application relates to a kind of honeycomb aluminum structure to space debris ultra-high speed collision protection quantitative evaluation method, belong to numerical simulation technical field.Based on the actual ultra-high speed collision situation, establish the simulation model of ultra-high speed collision, respectively extract the backplane and the back effect plate of honeycomb aluminum structure after ultra-high speed collision Perforation damage image, and extract the coordinate information of SPH particle of fragment cloud produced after space debris collides with honeycomb aluminum structure;The extracted perforation damage image is sequentially grayed, binarized, then the damage area is identified and the area size is counted, and the identification and size statistics of the minimum circumscribed circle or the minimum circumscribed fitting ellipse are realized, and the damage degree and range are quantitatively evaluated based on the above statistical results;Based on the coordinate information of the extracted SPH particle, the density clustering algorithm is used to realize the identification and mass statistics of the center dangerous fragment of the fragment cloud formed after collision, which realizes the quantitative evaluation of protection efficiency, and greatly saves the research and development cost.

Description

A quantitative evaluation method for the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris. Technical Field

[0001] This invention relates to a quantitative evaluation method for the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris, belonging to the field of numerical simulation technology. Background Technology

[0002] With the continuous development of aerospace technology, the protection of spacecraft outer shells remains a key research topic. Current advancements in spacecraft protective materials and structures place increasingly stringent demands on space debris protection. Particularly in advanced protective structure design and optimization processes, it is necessary to optimize and engineer common protective structures, master rapid reliability evaluation techniques, and formulate accurate ballistic limit equations to provide fundamental information for spacecraft protective architecture design. This requires conducting numerous ultra-high-speed impact tests to collect the impact characteristics of protective structures, providing a basis for the subsequent improvement of protective architecture design.

[0003] Conducting ultra-high-speed experiments requires a high-speed collision vacuum environment, which presents complex and demanding conditions. The experiments suffer from drawbacks such as long preparation periods, high costs, and poor repeatability. Furthermore, during the fragmentation process, the resulting debris clouds exhibit diverse sizes, shapes, velocities, and distributions. The collisions are extremely short-lived, generating numerous flashes of light. High-speed photography struggles to capture the dispersion information of fragments in the air. While X-ray photography can provide relatively clear information on the dispersion of debris clouds to some extent, its high cost makes it difficult to characterize using conventional methods. In addition, current commercial software for simulating and evaluating the damage performance of honeycomb aluminum composite panels has very limited post-processing capabilities, particularly lacking techniques for automatically extracting damage parameters from honeycomb panels and aftereffect panels, and for quantitative statistical analysis of debris cloud distribution. Summary of the Invention

[0004] In view of this, the present invention provides a quantitative evaluation method for the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris. This method can quantitatively and statistically analyze the perforation size of the honeycomb aluminum structure and its aftereffect plates, as well as identify and statistically analyze dangerous debris at the center of the debris cloud. It can effectively predict the protective effect of the honeycomb aluminum structure against space debris and achieve quantitative evaluation of the protective performance, which greatly saves research and development costs and has certain significance in the field of ultra-high-speed collision protection.

[0005] The objective of this invention is achieved through the following technical solutions.

[0006] A quantitative assessment method for the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris includes the following steps:

[0007] (i) Based on the actual ultra-high speed collision situation, establish an ultra-high speed collision simulation model, extract the perforation damage images of the back plate and the aftereffect plate of the honeycomb aluminum structure after the ultra-high speed collision, and extract the coordinate information (including node number, spatial position coordinates and mass information) of the debris cloud SPH particles generated after the space debris collides with the honeycomb aluminum structure.

[0008] (ii) The extracted images of the perforation damage of the back plate and the aftereffect plate are processed by grayscale and binarization respectively. Then, the damaged areas are identified and the area size is counted. The identification and size statistics of the minimum circumcircle (for head-on collision) or the minimum circumcircle fitted ellipse (for oblique collision) are realized. Based on the above statistical results, the degree and range of damage are quantitatively evaluated.

[0009] (iii) Based on the coordinate information of the extracted SPH particles, a density clustering algorithm is used to identify and perform mass statistics on dangerous fragments in the center of the debris cloud formed after the collision.

[0010] The honeycomb aluminum structure includes a front panel, a middle honeycomb core, and a rear back panel.

[0011] Further, in step (i), based on the actual physical model size parameters, a finite element model for simulating the protection of the honeycomb aluminum structure against hypervelocity collisions with space debris is established using ANSYS / LS-DYNA software; the SPH-FEM coupling algorithm is used to simulate the hypervelocity collision phenomenon and obtain the simulation result file (d3plot); the simulation result file is imported into the lsprepost post-processing software, and then the perforation damage images (.png) of the back plate and aftereffect plate of the honeycomb aluminum structure after the hypervelocity collision are exported, and the coordinate information text document (*.k file) of the SPH particles of the debris cloud generated after the space debris collides with the honeycomb aluminum structure is output through the Output function of the lsprepost post-processing software.

[0012] Furthermore, due to the complete disintegration and fragmentation of space debris during the ultra-high-speed collision with the honeycomb aluminum structure, resulting in extremely high deformation, and considering solution efficiency, SPH particles are used to model the projectile, which is automatically generated using the sphgen command in lsprepost software (all SPH models are generated in lsprepost); the panels of the honeycomb aluminum structure are modeled using a combination of solid elements and SPH particles. Specifically, areas where projectile impact may cause large deformations are finely discretized using SPH particles, while the remaining areas of the panel are discretized using solid elements; the honeycomb core of the honeycomb aluminum structure is parametrically modeled using the APDL high-level language, its base... The approach involves first establishing a honeycomb core unit cell, then complicating this unit cell in a specific direction. The number of complex elements is determined by the length and width of the honeycomb aluminum structure. The aadd command is then used to merge these elements to generate the honeycomb core model for simulation. The backplate of the honeycomb aluminum structure is modeled using the FEM-SPH adaptive coupling method. This method allows Lagrange solid elements to locally and adaptively transform into SPH particles under conditions of high distortion and inefficiency. This method can accurately capture different degrees of backplate damage, from small deformations to very large deformations. When the deformation strain exceeds a predetermined value, the deformed solid elements will transform into SPH particles, and it can simulate the damage to the backplate caused by debris peeling off the back of the honeycomb aluminum structure. The backplate is modeled using pure solid elements.

[0013] Furthermore, the simulation results file is imported into the lsprepost post-processing software, the display mode is set to no mesh and no shadow mode, and then the perforation damage images of the back plate and aftereffect plate of the honeycomb aluminum structure after the ultra-high speed collision are exported, which can reduce the influence of light and shadow color changes and mesh lines on the automatic recognition of subsequent perforation damage images.

[0014] Further, in step (ii), the perforation damage image extracted in step (i) is read using the OpenCV module in a Python program. Each pixel in the image is traversed and stored as a list [height, width, channel], where height represents the image height, width represents the image width, and channel represents the image's channels, with the channels in BGR format. Then, grayscale conversion is performed, typically using a weighted average method to convert the original BGR three-channel image to a single channel, resulting in a converted grayscale image. The grayscale image obtained after grayscale processing is then subjected to fixed-threshold image segmentation. This involves setting a fixed threshold, comparing the pixel value of each pixel in the entire image to this threshold, and if it is less than the threshold, changing the pixel value to a fixed value (usually black or white); if it is greater than or equal to the threshold, changing the pixel value to another fixed value (usually another color between black and white). The `cv2.THRESH_BINARY_INV` function is used for inverse thresholding to obtain a binary image. The `cv2.Sobel` function is then used to identify damage edge information in the binary image after binarization, obtaining the pixel location information of damage edges (pixel values ​​changing drastically), which serves as the data set for subsequent statistical analysis of damage area and extent. The `contouraArea` function, a built-in OpenCV algorithm for calculating closed regions, is used to calculate the area within the contour. Furthermore, to achieve extent detection of the entire damage area, the `np.vstack` function is used to stack arrays vertically (row order) to create a new array, stacking all identified damage edge contours. Then, the `minEnclosingCircle` and `fitEllipse` functions are used to identify and calculate the size of the minimum bounding circle or the minimum bounding fitted ellipse. Based on the statistical results, the degree and extent of damage are quantitatively assessed.

[0015] Furthermore, the grayscale image obtained after grayscale conversion is further processed using graphical operations such as mean filtering, dilation, and erosion to effectively remove noise and improve image clarity.

[0016] Further, in step (iii), the coordinate information of the SPH particles extracted in step (i) is read using a Python program to obtain the corresponding sample information data set of the particles. Then, a density clustering algorithm is used to group the set, so that similar objects are grouped into one class (cluster) and dissimilar objects are grouped into different classes (clusters). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) uses parameters (∈, MinPts) to describe the density of the sample distribution in the neighborhood. Here, ∈ describes the neighborhood distance threshold of a sample, and MinPts describes the threshold of the number of samples in the neighborhood of a sample at a distance of ∈. The core idea of ​​this algorithm is to use the number of neighboring points in the ∈ neighborhood of a point to measure the density of the space in which the point is located. As long as the density of points in a region is greater than the MinPts value, it is added to the cluster with similar density. By traversing all points in the space, irregularly shaped clusters that meet the expected results are found. Therefore, the values ​​of ∈ and MinPts are set according to the actual clustering effect required. The specific clustering process is as follows: By traversing unvisited points, the number of points within a radius ∈ N centered on that point is searched. If this value is greater than MinPts, it is marked as a core point and assigned to a new cluster C; otherwise, it is marked as a noise point. The remaining point set in the neighborhood N is traversed to determine if it is a core point. If it is a core point, a new neighborhood N' is generated, and the point set in N' needs to be traversed and ultimately assigned to cluster C. Otherwise, it is marked as a boundary point and directly assigned to C. This process continues until all points in cluster C are found, then other points are visited to find other clusters. Since the number of clusters does not need to be known beforehand during clustering, data mining analysis of SPH particles can be performed to obtain corresponding fragment statistics, such as the identification and quality statistics of dangerous fragments at the center of the fragment cloud formed after a collision.

[0017] Beneficial effects:

[0018] (1) This invention uses the Smooth Particle Algorithm (SPH) to simulate the protection of the honeycomb aluminum structure of the outer shell of a spacecraft satellite. It uses image recognition and density clustering algorithms to perform image recognition quantification and statistics on the damage characteristics of the honeycomb aluminum structure and its after-effect plates in the simulation results, as well as the identification and mass statistics of the central dangerous fragments in the debris cloud formed after collision and breakage. This solves the limitation of existing software simulation post-processing that cannot quantify the size of the perforation area of ​​the target plate. It also overcomes the problem of the SPH interface being blurry and unable to be identified, and realizes the identification and statistics of the central dangerous fragments in the debris cloud. It can effectively predict the protection effect of the honeycomb aluminum structure against space debris and realize the quantitative evaluation of the protection performance.

[0019] (2) This invention provides a new approach for the quantitative evaluation of the protection of space debris by the honeycomb aluminum structure of spacecraft shell under ultra-high speed collision conditions. It overcomes the problem of huge manpower and material costs in existing ultra-high speed experiments, greatly saves R&D costs, and can effectively predict the protection performance of the honeycomb aluminum structure of spacecraft shell. It has certain significance in the field of ultra-high speed collision protection.

[0020] (3) Numerical simulation provides good technical support for the method described in this invention, and the identification of damage characteristics through relevant algorithms helps to deeply understand the damage mechanism of the protective structure and provides strong theoretical and data support for the subsequent optimization of the protective structure.

[0021] (4) Different methods were used to model the projectile, panel, honeycomb core, back plate and aftereffect plate respectively, which greatly saved the solution time while improving the solution accuracy and optimizing the entire simulation calculation process. Attached Figure Description

[0022] Figure 1 is a schematic diagram of the modeling process of the honeycomb core in Example 1.

[0023] Figure 2 is a schematic diagram of the parameters of the honeycomb aluminum structure in Example 1.

[0024] Figure 3 shows the simulation model established in Example 1.

[0025] Figure 4 is a schematic diagram of the automatic identification of the size of the puncture and the extent of the damage in Example 1.

[0026] Figure 5 is a flowchart of the density clustering algorithm used in Example 1.

[0027] Figure 6 shows a three-dimensional image of the density clustering results in Example 1.

[0028] Figure 7 is a bar chart showing the statistical quality of fragments in the central fragment group after clustering in Example 1.

[0029] Figure 8 is a schematic diagram of the overall process of the quantitative evaluation method for the protection of space debris from ultra-high-speed collisions using honeycomb aluminum structures in Example 1. Detailed Implementation

[0030] The present invention will be further described below with reference to specific embodiments.

[0031] Example 1

[0032] Step 1: Based on the actual physical model dimensions, use ANSYS / LS-DYNA software to parametrically establish a finite element model for the spacecraft's outer shell honeycomb aluminum structure to withstand hypervelocity collisions with space debris. The specific modeling methods for different components are as follows: Space debris undergoes complete disintegration and fragmentation during a hypervelocity collision with the honeycomb aluminum structure, resulting in extremely high deformation. Considering solution efficiency, SPH particles are used to model the projectiles, automatically generated using the `sphgen` command in lsprepost software (SPH models are all generated in lsprepost). The panels of the honeycomb aluminum structure are modeled using a combination of solid elements and SPH particles. Specifically, 0.1mm... The SPH particles finely discretized the central circular region with a radius of 2 cm, which is the area where projectile impact could cause large deformations. The remaining areas of the panel were discretized using solid elements. The honeycomb core of the aluminum structure was modeled parametrically using the APDL high-level language. The basic idea was to create a single cell for the honeycomb core, then complicate it in either the X or Y direction, with the number of complexes controlled by the length and width of the honeycomb panel. The aadd command was then used to merge the complexes to generate the honeycomb core model for simulation, as shown in Figure 1. The backplate of the aluminum structure was modeled using the FEM-SPH adaptive coupling method, which allows Lagrange solid elements to operate under high distortion and low efficiency. The solid elements are locally and adaptively converted into SPH particles. This method can accurately capture back wall damage of different degrees, from small deformations to very large deformations. When the deformation strain exceeds a predetermined value, the deformed solid elements will be converted into SPH particles. It can also simulate the damage of the back plate caused by the spalling fragments from the back of the honeycomb aluminum structure. The back plate is modeled using pure solid elements. The honeycomb aluminum structure is an aluminum alloy honeycomb sandwich composite plate, which includes a front panel, a middle honeycomb core and a back plate. The constitutive models of the honeycomb aluminum structure and the space fragments are both modeled using the Johnson-Cook constitutive model. The material is 2A12T4. The overall structural parameters of the established finite element model of the honeycomb aluminum structure and the actual simulation model are shown in Figures 2 and 3.

[0033] The SPH-FEM coupling algorithm was used to simulate the hypervelocity collision phenomenon and obtain the simulation result file (d3plot). The simulation result file was imported into the lsprepost post-processing software, and the display mode was set to no mesh and no shadow mode. Then, the perforation damage images (.png) of the back plate and aftereffect plate of the honeycomb aluminum structure after the hypervelocity collision were exported. The output function of the lsprepost post-processing software was used to output the coordinate information (including node number, spatial position coordinates and mass information) of the SPH particles of the debris cloud generated after the space debris colliding with the honeycomb aluminum structure in a text document (*.k file).

[0034] Step 2: The OpenCV module in Python is used to read the perforation damage images of the backplate and aftereffect plate extracted in step (i). Each pixel in the image is traversed and stored as a list [height, width, channel], where height represents the image height, width represents the image width, and channel represents the image channel in BGR format. Then, a weighted average method (i.e., using the cv2.COLOR_RGB2GRAY function) is used to convert the original image's three BGR channels to a single channel to achieve grayscale conversion, resulting in a grayscale image. To facilitate subsequent thresholding and noise removal, a convolution kernel is defined using cv2.getStructuringElement, with a 5×5 kernel selected. Cv2.erode and cv2.morphologyex are used for erosion and opening / closing operations, respectively. Finally, a fixed threshold image segmentation is performed, setting a fixed threshold of 200. The pixel value of each pixel in the entire image is compared to this threshold; if it is less than the threshold, the pixel value is changed to a fixed value (i.e., set...). If the pixel value is greater than or equal to the threshold (i.e., set to white), the pixel value is changed to another fixed value. The cv2.THRESH_BINARY_INV function is used for reverse thresholding to obtain a binary image. Then, the cv2.Sobel function is used to identify the damage edge information of the binary image and obtain the pixel position information of the damage edge (pixel value changes drastically), which is used as a data set for subsequent statistics of damage area and range. Then, the cv2.contourArea function is used to calculate the area size within the contour, which is in units of pixels. In addition, in order to realize the range detection of the entire damage area, the np.vstack function is used to stack arrays vertically (row order) to form a new array, stacking all the identified damage edge contours. Then, the minEnclosingCircle and fitEllipse functions are used to realize the minimum bounding circle recognition (for head-on collisions) and the minimum bounding fitted ellipse recognition (for oblique collisions). The effect diagram is shown in Figure 4. The recognition result is that the damage area ratio is 1.7% and the circular damage range recognition radius is 0.75cm.

[0035] Step 3: Using a Python program, read the text document recording the SPH particle node number, spatial coordinates, and mass information, and input it into the particle sample information data set D for subsequent density clustering algorithm statistics. This set contains N SPH particles (p1, p2, ..., pN), and the spatial coordinates of each point particle pj (j = 1, 2, ..., N) are (xj, yj, zj). Then, the density clustering algorithm is used to group the set, so that similar objects are grouped into one class (cluster) and dissimilar objects are grouped into different classes (clusters). Set ∈ = 0.12 and MinPts = 0.1, where ∈ describes the neighborhood distance threshold of a sample and MinPts describes the neighborhood distance threshold of a sample. A threshold for the number of samples in the neighborhood of a given sample with a distance of ∈ was set. Density clustering was performed on the debris cloud formed after an ellipsoidal space debris penetrated a honeycomb aluminum structure at a speed of 6 km / s. The specific clustering process is shown in Figure 5. That is, by traversing unvisited points, the number of points in the neighborhood N with a radius of ∈ centered on the point is searched. If the value is greater than MinPts, it is marked as a core point and assigned to a new cluster C. Otherwise, it is marked as a noise point. The remaining set of points in the neighborhood N is traversed to determine whether it is a core point. If it is a core point, a new neighborhood N' is generated. The set of points in N' needs to be traversed and finally assigned to cluster C. Otherwise, it is marked as a boundary point and directly assigned to C. This process is repeated until all points in cluster C are found, and other points are visited to find other clusters. As shown in Figure 6, the density clustering 3D effect diagram shows that the clustering algorithm can classify SPH particles that are relatively close together in the debris cloud, that is, identify the central dangerous debris group. The black dots represent noise, indicating that the SPH particles are far away from the surrounding particles and are unlikely to become debris to damage the aftereffect plate. In the actual penetration process, part of the projectile material will melt or even vaporize, which will weaken its penetration ability on the aftereffect plate. Figure 7 shows the statistical analysis of the mass proportion of each fragment in the central dangerous debris group, which reflects the penetration ability on the aftereffect plate to a certain extent. The larger the proportion of the central dangerous fragment, the greater the threat to the aftereffect plate, and the less obvious the protective effect of the honeycomb aluminum structure.

[0036] The above steps enabled a quantitative assessment of the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris. The specific process is shown in Figure 8.

[0037] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A quantitative evaluation method for the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris, characterized in that: Specifically, the following steps are included: (i) A high-speed collision simulation model is established based on actual high-speed collision scenarios. Perforation damage images of the back plate and aftereffect plate of the honeycomb aluminum structure after the high-speed collision are extracted, and the coordinate information of the SPH particles of the debris cloud generated after the space debris collides with the honeycomb aluminum structure is extracted. (ii) The extracted perforation damage images of the back plate and aftereffect plate are processed by grayscale and binarization respectively. Then, the damaged areas are identified and the area size is counted. The minimum circumcircle or minimum circumcircle fitted ellipse is identified and its size is counted. The degree and range of damage are quantitatively evaluated based on the above statistical results. (iii) Based on the coordinate information of the extracted SPH particles, a density clustering algorithm is used to identify and count the mass of the dangerous fragments in the center of the debris cloud formed after the collision. The honeycomb aluminum structure includes a front panel, a middle honeycomb core and a rear back plate. The coordinate information of the SPH particles includes node number, spatial position coordinates and mass information.

2. The method for quantitatively evaluating the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris according to claim 1, characterized in that: In step (i), based on the actual physical model size parameters, a finite element model for simulating the protection of the honeycomb aluminum structure against ultra-high-speed collisions with space debris is established using ANSYS / LS-DYNA software. The SPH-FEM coupling algorithm is used to simulate the ultra-high-speed collision phenomenon and obtain the simulation result file. The simulation result file is imported into the lsprepost post-processing software, and then the perforation damage images of the back plate and aftereffect plate of the honeycomb aluminum structure after the ultra-high-speed collision are exported. The coordinate information of the SPH particles of the debris cloud generated after the space debris collides with the honeycomb aluminum structure is output as a text document through the Output function of the lsprepost post-processing software.

3. The method for quantitatively evaluating the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris according to claim 2, characterized in that: SPH particles were used to model the projectile, which was automatically generated using the sphgen command in lsprepost software. The panels of the honeycomb aluminum structure were modeled using a combination of solid elements and SPH particles. For areas where projectile impact might cause large deformations, SPH particles were used to finely discretize the corresponding areas of the panel, while the remaining areas of the panel were discretized using solid elements. The honeycomb core of the honeycomb aluminum structure was modeled parametrically using the APDL high-level language. First, a honeycomb core unit cell was created, and then the unit cell was complexed in a certain direction. The number of complexes was determined by the length and width of the honeycomb aluminum structure. The aadd command was then used to merge and generate the honeycomb core model for simulation. The backplate of the honeycomb aluminum structure was modeled using the FEM-SPH adaptive coupling method. The aftereffect plate was modeled using pure solid elements.

4. The method for quantitative evaluation of the protection against hypervelocity collisions with space debris using a honeycomb aluminum structure according to claim 2, characterized in that: The simulation results file was imported into the lsprepost post-processing software, the display mode was set to no mesh and no shadow mode, and then the perforation damage images of the back plate and aftereffect plate of the honeycomb aluminum structure after the ultra-high speed collision were exported.

5. A method for quantitatively evaluating the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris according to any one of claims 1 to 4, characterized in that: In step (ii), the perforation damage image extracted in step (i) is read using the OpenCV module in Python. Each pixel is iterated through and stored as a list [height, width, channel], where height represents the image height, width represents the image width, and channel represents the image's channels (in BGR format). A weighted average method is used to convert the original BGR three-channel image to a single channel for grayscale conversion, resulting in a grayscale image. Fixed-threshold image segmentation is then performed on the grayscale image. A fixed threshold is set, and the pixel value of each pixel in the entire image is compared to this threshold. If the value is less than the threshold, the pixel value is changed to a fixed value; otherwise, it is changed to another fixed value. The OpenCV module is then used to perform the segmentation.

2. The THRESH_BINARY_INV function performs inverse thresholding to obtain a binary image. The cv2.Sobel function is used to identify damage edge information in the binary image, obtaining the pixel position information of the damage edge, which serves as the data set for subsequent statistical analysis of the damage area and range. The area size within the contour is calculated using the contouraArea function, a built-in algorithm in OpenCV. In addition, the np.vstack function is used to stack arrays vertically to form a new array, stacking all the identified damage edge contours. Then, the minEnclosingCircle and fitEllipse functions are used to identify and count the minimum circumscribed circle or the minimum circumscribed fitted ellipse. Based on the statistical results, the degree and range of damage are quantitatively evaluated.

6. The method for quantitative evaluation of the protection against hypervelocity collisions of space debris using a honeycomb aluminum structure according to claim 5, characterized in that: The grayscale image obtained after grayscale conversion is first processed using graphical operations such as mean filtering, dilation, and erosion, and then image segmentation with a fixed threshold is performed.

7. A method for quantitatively evaluating the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris according to any one of claims 1 to 3, characterized in that: In step (iii), the coordinate information of the SPH particles extracted in step (i) is read by a Python program to obtain the sample information data set of the particles. Then, the density clustering algorithm is used to group the set, so that similar objects are grouped into one cluster and dissimilar objects are grouped into different clusters. Finally, through data analysis, the identification and mass statistics of dangerous fragments in the center of the debris cloud formed after the collision can be obtained.

8. A method for quantitatively evaluating the protection of honeycomb aluminum structures against ultra-high-speed collisions with space debris according to claim 7, characterized in that: Set the values ​​of ∈ and MinPts according to the desired actual clustering effect. ∈ describes the neighborhood distance threshold for a given sample, and MinPts describes the threshold for the number of samples in the neighborhood of a given sample at a distance of ∈. By traversing unvisited points, search for the number of points within a neighborhood N with a radius of ∈ centered on that point. If this value is greater than MinPts, mark it as a core point and assign it to a new cluster C; otherwise, mark it as a noise point. Traverse the remaining point set in neighborhood N to determine if it is a core point. If it is a core point, generate a new neighborhood N', traverse the point set in N', and finally assign it to cluster C. Otherwise, mark it as a boundary point and assign it directly to C. Continue until all points in cluster C are found, then visit other points to find other clusters.

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