Method, system and equipment for simulating impact energy absorption of textile composite material and medium

Abstract

The invention provides a textile composite material impact energy absorption simulation method, system and device and a medium, and the method comprises the steps: carrying out the connected domain analysis of a tomography image, and determining the equivalent diameter distribution characteristics of pore defects in a textile composite material; performing stochastic simulation based on the equivalent diameter distribution characteristics in combination with a mesoscopic geometric model of the textile composite material to obtain stochastic pore space distribution of the textile composite material in the resin enrichment area; geometric difference is carried out on the random pore space distribution and the mesoscopic geometric model, and then a mesoscopic finite element model containing pore defects is constructed; and endowing the mesoscopic finite element model with material attributes and carrying out explicit kinetic analysis on impact load, and further determining the impact energy absorption performance of the textile composite material prepared in the batch according to an analysis result. By adopting the scheme of the invention, systematic deviation of impact energy absorption performance prediction caused by model idealization can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of impact energy absorption simulation, and more particularly to a textile composite impact energy absorption simulation method, system, device and medium. BACKGROUND

[0002] Impact energy absorption simulation refers to a series of calculation processes such as constructing a material constitutive model, setting boundary conditions, applying dynamic loads and solving dynamic equations, which converts the nonlinear deformation behavior, energy conversion path and failure mechanism of the material under impact conditions into visualized quantitative data, thereby revealing the internal law of energy dissipation of the protective structure from the initial stress to the final failure process.

[0003] Textile composite impact energy absorption simulation refers to a series of numerical analysis methods such as constructing an anisotropic material model and defining the interface properties of yarns and matrix, which converts the complex damage modes such as fiber fracture, matrix cracking and delamination expansion of the textile structure under impact load into a quantifiable energy dissipation process, thereby revealing the internal mechanism of energy absorption from micro yarn sliding to macro structure deformation. In the traditional textile composite impact energy absorption simulation method, the finite element calculation is mostly dependent on idealized undamaged geometric models, which makes it difficult to effectively distinguish between the intrinsic mechanical properties of the material and the performance degradation caused by manufacturing defects, resulting in systematic deviations between the energy absorption values predicted by simulation and the experimental measurement results. For example, the traditional method completely ignores the pore defects inevitably produced in the actual production process when constructing the finite element model. However, these pores, as stress concentration sources and damage initiation points, can significantly change the energy dissipation path and failure mode during impact, ultimately leading to significant differences between the simulation model and physical experiments in predicting peak load, damage propagation behavior and total energy absorption. Therefore, how to reduce the systematic deviation of impact energy absorption performance prediction caused by model idealization has become a difficult problem in the industry. SUMMARY

[0004] The present application provides a textile composite impact energy absorption simulation method, system, device and medium, which can reduce the systematic deviation of impact energy absorption performance prediction caused by model idealization.

[0005] In a first aspect, the present application provides a textile composite impact energy absorption simulation method, comprising the following steps: Obtaining a tomographic image of a textile composite prepared in the same batch; Performing connected component analysis on the tomographic image, and then determining the equivalent diameter distribution characteristics of the pore defects in the textile composite through the analysis results; Based on the equivalent diameter distribution characteristics and the mesoscopic geometric model of the textile composite, a random simulation is performed to obtain the random pore spatial distribution of the textile composite in the resin-rich region. geometrically differ the random pore space distribution from the meso-geometric model to obtain a meso-finite element model containing pore defects; assign material properties to the meso-finite element model and perform explicit dynamic analysis under impact load, and then determine the impact energy absorption performance of the batch of prepared textile composites according to the analysis result.

[0006] In some embodiments, the connected domain analysis on the tomographic image specifically includes: performing filtering and noise reduction processing on the tomographic image to obtain a denoised tomographic image; processing the denoised tomographic image using a threshold segmentation algorithm to generate a pore region binary image; performing three-dimensional connected domain labeling on the pore region binary image, and outputting a pore labeling image containing the coordinates and voxel set of each pore space as an analysis result.

[0007] In some embodiments, determining the equivalent diameter distribution characteristics of the pore defects in the textile composite material through the analysis result specifically includes: determining the equivalent spherical diameter of each pore in the pore labeling image based on the analysis result; constructing a probability distribution function of the equivalent diameter through the equivalent spherical diameters of all pores; determining the equivalent diameter distribution characteristics of the pore defects in the textile composite material according to the probability distribution function of the equivalent diameter.

[0008] In some embodiments, the random pore space distribution of the textile composite material in the resin-rich region is obtained by random simulation based on the equivalent diameter distribution characteristics and the meso-geometric model of the textile composite material, specifically including: obtaining a meso-geometric model of the textile composite material; obtaining a random pore size sequence based on the equivalent diameter distribution characteristics and random number generation; determining a preferred area of pore distribution in the resin-rich region according to the fiber bundle arrangement structure in the meso-geometric model; generating a corresponding pore center point coordinate sequence according to the preferred area; matching and combining the random pore diameter sequence and the pore center point coordinate sequence to obtain the random pore space distribution of the textile composite material in the resin-rich region.

[0009] In some embodiments, the random pore space distribution is geometrically differentiated from the meso-geometric model to obtain a meso-finite element model containing pore defects, specifically including: generating a corresponding set of pore geometry entities based on the pore diameter and coordinate parameters in the random pore space distribution; geometrically differencing the set of pore geometry entities from the meso-geometry model to obtain a geometry model containing pore defects; performing finite element meshing on the geometry model to obtain a meso-finite element model containing pore defects.

[0010] In some embodiments, the explicit dynamics analysis on the meso-finite element model endowed with material properties and set with impact parameters specifically includes: defining corresponding material properties at different interface regions in the meso-finite element model; specifying impact contact regions on the surface of the meso-finite element model and setting impact velocity parameters; performing explicit dynamics analysis on the meso-finite element model endowed with material properties and set with impact parameters to obtain analysis results.

[0011] In some embodiments, determining the impact energy absorption performance of the batch of textile composites according to the analysis results specifically includes: extracting internal energy time history data from the analysis results obtained from the explicit dynamics analysis; calculating internal energy increment during the impact process based on the internal energy time history data; inputting the internal energy increment into a macro-homogenization model to obtain the impact energy absorption performance of the batch of textile composites.

[0012] In a second aspect, the present application provides a textile composite impact energy absorption simulation system, comprising: an acquisition module configured to acquire tomographic images of textile composites prepared in the same batch; a processing module configured to perform connected domain analysis on the tomographic images, and further determine equivalent diameter distribution characteristics of pore defects in the textile composites through analysis results; the processing module is further configured to perform random simulation based on the equivalent diameter distribution characteristics in combination with a meso-geometry model of the textile composites to obtain a random pore space distribution of the textile composites in a resin-rich region; the processing module is further configured to geometrically differ the random pore space distribution from the meso-geometry model to further construct a meso-finite element model containing pore defects; an execution module configured to perform explicit dynamics analysis on the meso-finite element model endowed with material properties and set with impact load, and further determine the impact energy absorption performance of the batch of textile composites according to analysis results.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the textile composite impact energy absorption simulation method.

[0014] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the textile composite impact energy absorption simulation method.

[0015] The technical scheme provided by the embodiments of the present application has the following beneficial effects: In the textile composite impact energy absorption simulation method, system, device and medium provided by the present application, firstly, the random simulation is performed based on the equivalent diameter distribution characteristics and the mesoscopic geometric model of the textile composite material to obtain the random pore space distribution of the resin-rich region. This process can introduce the actual pore statistical characteristics obtained by quantifying the tomographic image into the ideal geometric model, and reproduce the pore distribution with similar statistical rules as the real material through random simulation, thereby providing the defect space configuration in the actual process state for subsequent construction of a high-fidelity model. Then, the random pore space distribution is geometrically differentiated with the mesoscopic geometric model to construct a mesoscopic finite element model containing pore defects. This process can accurately etch the spatial morphology of the random pores at the geometric level, so that the finally generated finite element grid naturally contains these geometric defects as initial damages, thereby truly reproducing the physical behavior of the pores as stress concentration sources and damage preferential initiation points in the simulation. This process provides a real mesoscopic structure basis necessary for impact damage evolution simulation. Furthermore, in the explicit dynamics analysis of the impact load, the mesoscopic finite element model containing pore defects can simulate behaviors such as early cracking of the matrix, debonding of the fiber / matrix interface and asymmetric expansion of the damage initiated at the pores. This process can change the energy dissipation path in the simulation by introducing initial defects, so that the prediction of the impact response is no longer based on the ideal assumption of a material without defects, but reflects the performance degradation caused by defects. This process provides a simulation model verified by manufacturing reality for impact energy absorption performance prediction through quantitative characterization and accurate embedding of defect geometry, so that the simulation result is no longer a theoretical value under an ideal model, but engineering practical data with similar defect states as the physical samples in the same batch, thereby significantly reducing the difference with the experiment, and effectively suppressing the systematic deviation caused by idealization of the model. In summary, the scheme can reduce the systematic deviation of the impact energy absorption performance prediction caused by idealization of the model. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of a textile composite impact energy absorption simulation method according to some embodiments of the present application; Figure 2 is a flowchart of implementing connected domain analysis according to some embodiments of the present application; Figure 3 is a flowchart of determining equivalent sphere diameter according to some embodiments of the present application; Figure 4 is a structural diagram of a textile composite impact energy absorption simulation system according to some embodiments of the present application; Figure 5 is an internal structure diagram of a computer device for implementing a textile composite impact energy absorption simulation method according to some embodiments of the present application. DETAILED DESCRIPTION

[0017] In order to better understand the technical solutions in the embodiments, the technical solutions in the embodiments will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Reference Figure 1 The figure is a flowchart of a textile composite impact energy absorption simulation method according to some embodiments of the present application, which mainly includes the following steps: In step 101, the tomographic images of textile composites prepared in the same batch are obtained.

[0019] In specific implementation, an industrial computed tomography device can be used to scan a representative textile composite sample selected from the same production batch; preferably, a micro-focus X-ray computed tomography (CT) system can be used to perform 360-degree rotational scanning on a mechanically processed textile composite sample under a preset parameter setting, so as to obtain sequence two-dimensional projection images that can clearly distinguish fibers, resins and pore defects, and obtain the tomographic images of the textile composite sample through a three-dimensional reconstruction algorithm.

[0020] It should be noted that the tomographic images of the textile composite material described in the present application refer to three-dimensional gray-scale image data representing the internal microstructure of the material obtained by X-ray computed tomography technology; its physical nature reflects the differential absorption characteristics of X-rays by each component (fiber bundle, resin matrix and pore) in the material; wherein the same batch means that the material is made by using the same raw materials, layering scheme and curing process cycle.

[0021] In step 102, the tomographic images are subjected to connected domain analysis, and the equivalent diameter distribution characteristics of the pore defects in the textile composite material are determined through the analysis results.

[0022] In some embodiments, referenceFigure 2 As shown in the figure, the figure is a flowchart for implementing connected component analysis according to some embodiments of the present application. The connected component analysis of the tomographic image can be implemented by the following steps: First, in step 1021, the tomographic image is filtered and denoised to obtain a denoised tomographic image. Then, in step 1022, the denoised tomographic image is processed using a threshold segmentation algorithm to generate a pore region binary image. Finally, in step 1023, the pore region binary image is labeled in three dimensions to output a pore label image containing the coordinates of each pore space and a set of voxels as the analysis result.

[0023] In a specific implementation, the tomographic image is filtered and denoised to obtain a denoised tomographic image, which can be implemented in the following manner, for example: first, the tomographic image is taken as input and processed using a Gaussian filter algorithm: by defining a Gaussian kernel function, the weight values of each pixel in the kernel region are calculated, and the weight is used to calculate the weighted average of all pixels in the kernel region, and the calculation result is taken as the new gray value of the center pixel; the image obtained after the convolution operation is completed by traversing all pixels of the image in a sliding window manner, which is the denoised tomographic image; wherein the standard deviation parameter of the Gaussian kernel can be preset according to the noise level of the image, and a larger standard deviation is set to enhance the smoothing effect when the noise is larger, and a smaller standard deviation is set to retain more detail features when the noise is smaller.

[0024] It should be noted that the pore region binary image in the present application refers to a digital image obtained by image segmentation processing, which represents the spatial distribution of the internal pore space of the textile composite material with binary pixel values, and is used to clearly distinguish the spatial position relationship between the pore defects and the solid material in the material, and to provide basic data for subsequent three-dimensional connected component analysis.

[0025] In a specific implementation, the denoised tomographic image is processed using a threshold segmentation algorithm to generate a pore region binary image, which can be implemented in the following manner, for example: first, the denoised tomographic image is taken as input and processed using the Otsu threshold segmentation algorithm: by traversing all possible gray threshold values, the inter-class variance of the foreground (pore region) and background (material matrix) corresponding to each threshold value is calculated, and the gray value that maximizes the inter-class variance is selected as the optimal segmentation threshold; then, all pixels in the image with a gray value lower than the optimal segmentation threshold are set to 1, indicating the pore region, and all pixels with a gray value higher than the threshold are set to 0, indicating the non-pore region; the image finally obtained containing only two pixel values of 0 and 1 is the pore region binary image.

[0026] In a specific implementation, the three-dimensional connected domain labeling of the pore region binary image and outputting a pore labeled image containing the three-dimensional coordinates and voxel set of each pore as the analysis result can be achieved in the following manner: first, the three-dimensional connected domain labeling algorithm based on 26-adjacent relationship is used to process the pore region binary image: all voxels in the binary image are scanned layer by layer, when a voxel with a value of 1 is encountered, 26 voxels adjacent to it in the previous layer and the current layer are checked, if there is a labeled connected region, the current voxel is assigned to the region, otherwise a new region label is created; after the scanning of all voxels is completed, each connected pore region is assigned a unique label value; then, the three-dimensional coordinates of all voxels contained in each label region are recorded to form a voxel set of the pore; and finally, the generated multi-channel image is the pore labeled image, in which the value of each pixel represents the pore label to which it belongs, and the spatial coordinates and voxel set data of the corresponding pore are stored in association.

[0027] In some embodiments, the determination of the equivalent diameter distribution characteristics of the pore defects in the textile composite material based on the analysis result can be achieved in the following steps: determining the equivalent spherical diameter of each pore in the pore labeled image based on the analysis result; constructing a probability distribution function of the equivalent diameters based on the equivalent spherical diameters of all pores; determining the equivalent diameter distribution characteristics of the pore defects in the textile composite material based on the probability distribution function of the equivalent diameters.

[0028] In a specific implementation, referring to FIG. 6, Figure 3 FIG. 6 is a flow diagram for determining the equivalent spherical diameter according to some embodiments of the present application, and the determination of the equivalent spherical diameter of each pore in the pore labeled image based on the analysis result can be achieved in the following manner: for each pore region labeled by a unique label in the analysis result, the number of voxels contained in the region is counted; then, according to the spatial resolution parameter (i.e., the physical volume of a single voxel) of the computed tomography device, the physical volume of a single voxel is multiplied by the number of voxels to obtain the actual volume of each pore; finally, based on the spherical volume formula, the diameter of a sphere with the same volume as the pore is calculated, and the diameter value is taken as the equivalent spherical diameter of the pore; wherein the physical volume of a single voxel is calculated by the voxel size set during scanning, which is determined by the scanning resolution and reconstruction parameters of the CT device.

[0029] It should be noted that the equivalent spherical diameter in the present application refers to the diameter parameter of an ideal sphere with the same volume as the irregularly shaped pore, which is used to quantify the complex-shaped pore defects into standard geometric characteristics, facilitating the statistical analysis and comparison of the size characteristics of different pores.

[0030] In a specific implementation, the probability distribution function of the equivalent diameters can be constructed by the equivalent spherical diameters of all the pores in the following manner, for example: first, the distribution of the equivalent spherical diameters of all the pores is analyzed by a histogram statistical method, preferably, the diameter statistical intervals can be set, the number of pores in each interval is calculated, and the number of pores in each interval is divided by the total number of pores to obtain the corresponding probability density; then the probability densities of the statistical intervals are connected to form a complete probability distribution curve; the width of the diameter statistical interval can be preset according to the distribution range of the pore size, and when the pore size distribution is wide, a wider statistical interval is used to ensure the smoothness of the distribution curve, and when the pore size distribution is concentrated, a narrower statistical interval is used to retain the detail characteristics of the distribution.

[0031] It should be noted that the probability distribution function of the equivalent diameters in the present application refers to a mathematical function describing the statistical distribution law of the equivalent diameters of all the pores in the textile composite material, which is used to represent the overall distribution characteristics of the pore size in the material.

[0032] In a specific implementation, the equivalent diameter distribution characteristics of the pore defects in the textile composite material can be determined according to the probability distribution function of the equivalent diameters in the following manner, for example: first, the mathematical expectation of the probability distribution function is calculated as the mean parameter of the pore equivalent diameters, which represents the concentration trend of the pore size; then the standard deviation of the probability distribution function is calculated as the dispersion parameter of the pore equivalent diameters, which represents the fluctuation range of the pore size; finally, the mean parameter and the dispersion parameter are combined as the equivalent diameter distribution characteristics, and in other embodiments, another method can also be used to achieve this, which is not limited here.

[0033] It should be noted that the equivalent diameter distribution characteristics in the present application refer to the combination of statistical parameters representing the concentration trend and dispersion degree of the pore size extracted from the probability distribution function, which is used to quantitatively describe the typical characteristics of the pore size distribution of the batch of materials.

[0034] In step 103, random simulation is performed based on the equivalent diameter distribution characteristics and the mesoscopic geometric model of the textile composite material to obtain the random pore spatial distribution of the textile composite material in the resin-rich region.

[0035] In some embodiments, the random simulation based on the equivalent diameter distribution characteristics and the mesoscopic geometric model of the textile composite material to obtain the random pore spatial distribution of the textile composite material in the resin-rich region can be achieved in the following steps: obtaining the mesoscopic geometric model of the textile composite material; generating a random number based on the equivalent diameter distribution characteristics to obtain a random pore size sequence; determining a preferred region of the pore distribution in the resin-rich region according to the fiber bundle arrangement structure in the meso-geometric model; generating a corresponding sequence of pore center point coordinates according to the preferred region; matching and combining the sequence of random pore diameters with the sequence of pore center point coordinates to obtain a random pore spatial distribution of the textile composite material in the resin-rich region.

[0036] It should be noted that the meso-geometric model of the textile composite material in the present application refers to a three-dimensional digital model representing the internal yarn weaving structure of the material constructed by a parameterized modeling method, which reflects the meso-geometric characteristics of the textile composite material under ideal manufacturing conditions in its physical nature. The meso-geometric model can be constructed by a special textile modeling software. The process parameters completely consistent with the actual material need to be input during modeling, including but not limited to warp density, weft density, yarn cross-sectional shape, textile structure type and lay-up angle. The process parameters can be obtained from the preparation process file of the same batch of materials to ensure that the model is consistent with the actual material in structure. In specific implementation, special textile modeling software such as TexGen and WiseTex can be used to automatically create a three-dimensional model of the textile structure containing complete yarn path and cross-sectional variation by calling the graphical interface to input the above process parameters and running the geometric generation algorithm. The meso-geometric model needs to clearly distinguish the geometric boundary between the fiber bundle region and the resin-rich region, and output in a standard three-dimensional geometric file format (such as STEP, IGES) to provide an accurate geometric basis for subsequent pore spatial distribution generation. In other embodiments, the meso-geometric model can also be obtained based on micro-CT scanning data through image processing and geometric reconstruction methods, which are not limited in the present application.

[0037] In specific implementation, the random pore size sequence can be obtained by generating random numbers based on the equivalent diameter distribution characteristics, which can be realized in the following way, for example: first, obtain the mean parameter and the dispersion parameter in the equivalent diameter distribution characteristics as the basic statistical characteristics of random number generation; then use a random number generation algorithm based on normal distribution to generate a sequence of random pore diameter values conforming to the statistical characteristics, using the mean parameter as the center position parameter of the probability distribution and the dispersion parameter as the width control parameter of the probability distribution. In the generation process, a reasonable upper and lower limit of the pore diameter is set to ensure that the generated pore size conforms to the physical reality, wherein the upper limit value is determined according to the minimum spatial size of the resin-rich region to avoid generating pores that exceed the capacity of the region, and the lower limit value is determined according to the resolution ability of the tomographic image to avoid generating micro-pores that cannot be identified. Finally, a random pore size sequence containing multiple pore diameter values is output. In other embodiments, another method can also be used to realize it, which is not limited here.

[0038] It should be noted that the random pore size sequence in the present application refers to a sequence of pore diameter values generated by random sampling based on the equivalent diameter distribution characteristics, which is used to reproduce the pore size distribution consistent with the statistical characteristics of the actual material in the geometric model.

[0039] In a specific implementation, the preferred region of pore distribution in the resin-rich region can be determined according to the arrangement structure of the fiber bundle in the meso-geometric model in the following manner, for example: first, the meso-geometric model is spatially meshed to generate voxelized three-dimensional grid data; then, the spatial distance from each voxel to the nearest fiber bundle boundary is calculated to construct a three-dimensional distance field of the resin-rich region; next, based on a preset distance threshold range, a set of voxels whose distance values are within the threshold range are filtered out from the three-dimensional distance field, and the distance threshold range is determined according to the pore genesis analysis and experimental statistical results of the same batch of materials, and is used to represent the fiber bundle interstitial size where pores are more likely to occur; finally, the three-dimensional space region formed by the set of filtered voxels is defined as the preferred region of pore distribution.

[0040] It should be noted that the preferred region of pore distribution in the present application refers to a resin region where pores are more likely to occur, which is determined by distance field analysis based on the correlation between fiber bundle spatial arrangement and pore genesis.

[0041] In a specific implementation, the corresponding pore center point coordinate sequence can be generated according to the preferred region in the following manner, for example: first, the spatial envelope boundary of the three-dimensional voxel set of the preferred region is extracted to obtain the minimum and maximum coordinate values in the X, Y, and Z axis directions; then, a three-dimensional non-uniform random sampling algorithm is used to generate candidate pore center point coordinates within the voxel set of the preferred region, wherein the sampling probability can be weighted according to the distance of the voxel to the fiber bundle, and a position moderately distant from the fiber bundle surface is given a higher sampling weight; for each generated candidate coordinate point, whether it is indeed located within the preferred region is verified by a geometric position judgment algorithm, and the specific judgment method includes querying whether the voxel where the coordinate point is located belongs to the preferred region voxel set, and if the coordinate point is not within the preferred region, it is regenerated until a sufficient number of valid pore center point coordinates are obtained; finally, these verified coordinate points are arranged in the order of generation as the corresponding pore center point coordinate sequence.

[0042] It should be noted that the random pore size sequence in the present application refers to a sequence of pore diameter values generated by random sampling based on the equivalent diameter distribution characteristics, which is used to reproduce the pore size distribution consistent with the statistical characteristics of the actual material in the geometric model.

[0043] In a specific implementation, the random pore diameter sequence and the pore center point coordinate sequence are matched and combined to obtain the random pore spatial distribution of the textile composite material in the resin-rich region. The following methods can be used to achieve this, for example: first, the pore diameter sequence and the pore center point coordinate sequence are matched one by one according to the same index order to establish the correspondence between the diameter value and the spatial position of each pore, forming an initial pore spatial distribution; then perform a pore spatial interference check, traverse all the matched pores, and detect whether there is spatial overlap between any two pores. Specifically, whether interference occurs is determined by calculating the distance between the two pore center points and comparing it with the sum of their radii; if overlap is found, the interference is eliminated by adjusting the spatial position of one of the pores or regenerating the coordinates of the pore until all pores meet the non-overlapping condition; finally, the pore data that passes the interference check is arranged into a structured random pore spatial distribution data set, thereby obtaining the random pore spatial distribution of the textile composite material in the resin-rich region. In other embodiments, other methods can also be used to achieve this, which are not limited here.

[0044] It should be noted that the random pore spatial distribution described in this application refers to a structured data set containing pore diameter and spatial position coordinates, which is used to define a pore defect spatial configuration that conforms to actual statistical rules in a mesoscopic geometric model, and provides input for constructing a finite element model containing defects.

[0045] In step 104, the random pore spatial distribution is geometrically differentiated with the mesoscopic geometric model to construct a mesoscopic finite element model containing pore defects.

[0046] In some embodiments, the random pore spatial distribution is geometrically differentiated with the mesoscopic geometric model to construct a mesoscopic finite element model containing pore defects, which can be achieved by the following steps: Generate a corresponding set of pore geometric entities based on the pore diameter and coordinate parameters in the random pore spatial distribution; Geometrically differentiate the set of pore geometric entities with the mesoscopic geometric model to obtain a geometric model containing pore defects; Perform finite element meshing on the geometric model to obtain a mesoscopic finite element model containing pore defects.

[0047] In a specific implementation, the generation of the corresponding set of pore geometric entities based on the pore diameter and coordinate parameters in the random pore space distribution can be implemented in the following manner, for example: first, traverse the random pore space distribution dataset, and read the diameter D and three-dimensional coordinates (X, Y, Z) in each pore record in sequence; take (X, Y, Z) as the spherical center coordinates and D / 2 as the radius, and generate the geometric entity of a single pore through the sphere creation function of the geometric modeling kernel; the function constructs a sphere through the following steps: first, determine the position according to the spherical center coordinates, and then calculate the discrete point set on the surface of the sphere according to the radius value, and the number of discrete points is controlled by a preset surface subdivision parameter, and the subdivision parameter is set according to the required geometric accuracy, and a larger subdivision parameter is set when the accuracy requirement is high, and a smaller subdivision parameter is set when the accuracy requirement is low; finally, connect the discrete point set into a continuous curved surface mesh through a triangulation algorithm to generate a sphere entity with geometric boundary representation; repeat the process until all pore records are processed to form a complete set of pore geometric entities.

[0048] It should be noted that the set of pore geometric entities in the present application refers to a combination of three-dimensional entities representing the geometric shape of a single pore generated according to the random pore space distribution, which is used as a tool entity in geometric difference operation to remove the space occupied by the pores from the ideal geometric model.

[0049] In a specific implementation, the geometric difference between the set of pore geometric entities and the mesoscopic geometric model to obtain a geometric model containing pore defects can be implemented in the following manner, for example: load the mesoscopic geometric model as the main entity into the geometric modeling environment; read each sphere entity in the set of pore geometric entities as a tool entity in sequence; preferably, the geometric difference operation specifically includes: first, determine the intersection region of the tool entity and the main entity through spatial position relationship judgment, and adopt a two-level detection mechanism of coarse detection based on bounding box and precise detection based on curve intersection; then remove the part intersecting with the tool entity from the boundary representation of the main entity through the Boolean difference algorithm of the boundary representation model, specifically including: calculating the intersection line of the two entities, dividing the original entity into multiple sub-regions according to the intersection line, retaining the region belonging to the main entity but not belonging to the tool entity, and deleting the overlapping region; when processing multiple pore entities, a batch Boolean operation optimization strategy is adopted to localize the operation region through spatial partitioning technology, thereby improving the calculation efficiency; finally, a geometric model containing pore cavities is generated, and the geometric model maintains the consistency of geometric topology.

[0050] It should be noted that the geometric model containing pore defects in the present application refers to a three-dimensional geometric model containing pore cavities consistent with the statistical characteristics of the actual material, which is obtained through geometric difference processing and is used to accurately reflect the influence of manufacturing process defects on the geometric structure of the material.

[0051] In a specific implementation, the finite element meshing of the geometric model to obtain the mesoscopic finite element model containing the porosity defect can be implemented in the following manner, for example: first, import the geometric model containing the porosity defect into a meshing module; first, perform geometric cleaning and repair to ensure that the model has good meshing conditions; then set meshing parameters, including element type selection, global element size setting, and local encryption region designation; generate surface mesh using the front propagation method: start from the geometric boundary, generate triangular elements in sequence, and adjust the element density according to the curvature in the porosity boundary and other regions with large curvature; after the surface mesh is generated, generate body mesh using the constrained Delaunay tetrahedralization method: generate tetrahedral mesh through point insertion and element connection operations under the constraint of the surface mesh; set interface elements in the interface region between the fiber bundle and the resin matrix to ensure correct expression of the material discontinuity surface; finally, perform mesh quality inspection and optimization to improve mesh quality through node smoothing and element topology optimization, and output the finite element model meeting the requirements of finite element analysis as the mesoscopic finite element model containing the porosity defect. In other embodiments, another method can also be used to implement this, which is not limited here.

[0052] It should be noted that the mesoscopic finite element model in the present application refers to a finite element mesh model obtained by discretizing the geometric model containing the porosity defect, which is used to carry material attribute parameters and boundary conditions.

[0053] In step 105, material attributes are assigned to the mesoscopic finite element model and explicit dynamics analysis under impact load is performed, and then the impact energy absorption performance of the batch of prepared textile composites is determined according to the analysis results.

[0054] In some embodiments, the explicit dynamics analysis under impact load of the mesoscopic finite element model with material attributes can be implemented in the following steps: Define corresponding material attributes in different interface regions of the mesoscopic finite element model; Specify impact contact regions on the surface of the mesoscopic finite element model and set impact velocity parameters; Perform explicit dynamics analysis on the mesoscopic finite element model with material attributes and impact parameters to obtain the analysis results.

[0055] In a specific implementation, the definition of the corresponding material properties of the different interface regions in the mesoscopic finite element model can be implemented in the following manner, for example: first, identify the different interface regions (fiber bundle unit set, resin matrix unit set and interface unit set) in the mesoscopic finite element model; then, based on the material performance test data, assign transversely isotropic elastic material parameters to the fiber bundle unit set, including the elastic modulus along the fiber direction, the elastic modulus perpendicular to the fiber direction and the corresponding Poisson's ratio parameters; assign isotropic elastic-plastic material parameters to the resin matrix unit set, including the elastic modulus, the yield strength and the hardening modulus; assign cohesive force model parameters to the interface unit set, including the normal stiffness, the tangential stiffness and the critical fracture energy; wherein each material parameter is obtained by standard material testing and is assigned to the corresponding finite element unit according to the unit set identification. In other embodiments, another method can also be used to implement it, which is not limited here.

[0056] In a specific implementation, the definition of the corresponding material properties of the different interface regions in the mesoscopic finite element model can be implemented in the following manner, for example: first, identify the different interface regions (fiber bundle unit set, resin matrix unit set and interface unit set) in the mesoscopic finite element model; then, based on the material performance test data, assign transversely isotropic elastic material parameters to the fiber bundle unit set, including the elastic modulus along the fiber direction, the elastic modulus perpendicular to the fiber direction and the corresponding Poisson's ratio parameters; assign isotropic elastic-plastic material parameters to the resin matrix unit set, including the elastic modulus, the yield strength and the hardening modulus; assign cohesive force model parameters to the interface unit set, including the normal stiffness, the tangential stiffness and the critical fracture energy; wherein each material parameter is obtained by standard material testing and is assigned to the corresponding finite element unit according to the unit set identification. In other embodiments, another method can also be used to implement it, which is not limited here.

[0057] In a specific implementation, the explicit dynamic analysis of the mesoscopic finite element model endowed with material properties and impact parameters is performed, and the analysis results can be obtained by using the following methods, for example: first, a dynamic explicit analysis step is created in the explicit dynamic analysis software, the total analysis time is set to meet the requirements of the complete evolution of the impact process, and the data types to be output are specified in the field variable output request, including stress components, strain components, displacement components, element internal energy and system total internal energy; then, the analysis job is submitted, and the explicit dynamic solver solves the dynamic equations of the system based on the central difference time integration method, and in each time increment step, the element strain increment is calculated first, the element stress state is updated according to the material constitutive relation, the element internal force is calculated and the global node force vector is assembled, then the node acceleration and velocity are solved, the node displacement is updated, and the internal energy contribution of each element is calculated and accumulated to update the system total internal energy; during the analysis process, the solver writes the element stress tensor, strain tensor, node displacement vector and system total internal energy value of each time step into the output database; finally, the obtained analysis results include complete stress field time series, strain field time series, displacement field time series and internal energy time history data as analysis results, and preferably, the analysis results can be stored in the result database with time steps as the index.

[0058] In some embodiments, the determination of the impact energy absorption performance of the batch of textile composites according to the analysis results can be achieved by using the following steps: extracting internal energy time history data from the analysis results obtained from the explicit dynamic analysis; calculating the internal energy increment in the impact process based on the internal energy time history data; inputting the internal energy increment into the macroscopic homogenization model to obtain the impact energy absorption performance of the batch of textile composites.

[0059] In a specific implementation, the extraction of internal energy time history data from the analysis results obtained from the explicit dynamic analysis can be achieved by using the following methods, for example: first, access the result database file output by the explicit dynamic solver, and locate the data table where the system total internal energy data is stored through the database query interface; then read all records in the data table in the order of time steps, each record contains a time value and a system total internal energy value corresponding to the time step; finally, organize these data into an internal energy time history data sequence in the order of time, which is stored in the form of a two-dimensional array, the first column is the time value, and the second column is the system total internal energy value, thereby obtaining the internal energy time history data.

[0060] It should be noted that the internal energy time history data in this application refers to a numerical sequence recording the change of the system total internal energy with time during the impact process, which is used to represent the dynamic process of energy absorption through deformation and damage of the material under impact load.

[0061] In a specific implementation, the internal energy increment during the impact process can be calculated based on the internal energy time history data, for example, by: first, determining the starting time point of the impact process by detecting the change in the kinetic energy of the system, which corresponds to the time when the kinetic energy starts to decrease significantly and the internal energy starts to increase; then determining the ending time point of the impact process, which corresponds to the time when the internal energy value reaches a steady state and no longer changes significantly; next, extracting the initial internal energy value corresponding to the starting time point and the final internal energy value corresponding to the ending time point from the internal energy time history data sequence; finally, calculating the difference between the final internal energy value and the initial internal energy value to obtain the internal energy increment during the impact process; as a preferred embodiment, the impact energy absorption performance of the batch of textile composites can be determined based on the internal energy increment, for example, by: directly using the calculated internal energy increment as a quantitative indicator of the impact energy absorption performance of the batch of textile composites, which represents the comprehensive ability of the material to absorb energy through elastic deformation, plastic deformation, damage evolution, and failure mechanisms during the impact process; the internal energy increment indicator can be used to evaluate the energy absorption characteristics of the batch of materials under impact loading, and in other embodiments, other methods can also be used, which are not limited here.

[0062] It should be noted that the internal energy increment in this application refers to the net increase in the internal energy of the material system during the impact process, which is used to quantitatively represent the total amount of mechanical energy absorbed and converted into deformation energy and damage energy by the batch of materials during the impact event; in addition, the impact energy absorption performance refers to a quantitative indicator of the ability of textile composites to absorb mechanical energy under impact loading, which is used to evaluate the effectiveness and reliability of the batch of materials in impact protection applications.

[0063] In a specific implementation, the internal energy increment is input into a macro-homogenization model to obtain the impact energy absorption performance of the batch of textile composites, which can be achieved by, for example: first, obtaining a pre-defined macro-homogenization model, which is a parameterized mathematical model representing the mapping relationship between the mesoscopic scale energy absorption characteristics and the macroscopic equivalent performance of textile composites; next, dividing the calculated internal energy increment by the volume of the mesoscopic finite element model to obtain the unit volume energy absorption at the mesoscopic scale; then, inputting the unit volume energy absorption as a key input parameter into the macro-homogenization model; based on the energy equivalence principle, the macro-homogenization model converts the mesoscopic unit volume energy absorption into impact energy absorption performance indicators of interest in macroscopic engineering applications through linear or nonlinear mapping functions, including but not limited to macroscopic equivalent energy absorption efficiency, unit mass energy absorption, or critical failure energy threshold; finally, outputting the impact energy absorption performance of the batch of textile composites.

[0064] It should be noted that the macro-homogenization model in the present application refers to a calculation model based on the principle of energy equivalence, which is used to convert the local energy absorption obtained by meso-scale finite element analysis into macro-scale material or structural performance indicators. Its role is to apply the meso-scale simulation results to macro-scale engineering design and performance evaluation. As a preferred embodiment, the macro-homogenization model can be obtained by obtaining a textile composite sample with a known macro-impact energy absorption performance standard value, which is measured by a drop hammer impact testing machine or a Hopkinson bar experimental device. For each sample, the same textile composite impact energy absorption simulation method as in the present application is used to perform the whole process calculation from the tomographic image acquisition to the explicit dynamics analysis, and finally the corresponding meso-scale unit volume energy absorption is obtained. By linear regression or nonlinear fitting algorithm, a quantitative mapping relationship between the meso-scale unit volume energy absorption and the macro-impact energy absorption performance standard value is established, thereby constructing the macro-homogenization model. The present application does not limit this. In addition, the impact energy absorption performance of the textile composite material refers to the ability of the material to irreversibly convert external impact mechanical energy into internal energy forms such as material deformation energy, surface energy and heat energy through a series of damage and failure mechanisms such as fiber fracture, matrix cracking, delamination and fiber / matrix interface debonding under impact load.

[0065] In addition, another aspect of the present application, in some embodiments, the present application provides a textile composite impact energy absorption simulation system, referring to Figure 4 The figure is a structural schematic diagram of a textile composite impact energy absorption simulation system according to some embodiments of the present application. The textile composite impact energy absorption simulation system 200 includes an acquisition module 201, a processing module 202 and an execution module 203, which are described as follows: The acquisition module 201 is mainly used to acquire the tomographic images of the textile composite materials prepared in the same batch in the present application; The processing module 202 is mainly used to perform connected component analysis on the tomographic images in the present application, and then determine the equivalent diameter distribution characteristics of the pore defects in the textile composite material through the analysis results; In addition, the processing module 202 in the present application is also used to perform random simulation based on the equivalent diameter distribution characteristics combined with the meso-scale geometric model of the textile composite material to obtain the random pore space distribution of the textile composite material in the resin-rich region; In addition, the processing module 202 in the present application is also used to perform geometric difference between the random pore space distribution and the meso-scale geometric model, and then construct the meso-scale finite element model containing pore defects; The execution module 203 is mainly used for assigning material properties to the mesoscopic finite element model and performing explicit dynamic analysis of impact load, and then determining the impact energy absorption performance of the batch of prepared textile composites according to the analysis result.

[0066] In addition, the present application also provides a computer device, which comprises a memory and a processor, the memory stores codes, and the processor is configured to acquire the codes and execute the textile composite impact energy absorption simulation method.

[0067] In some embodiments, reference is made to Figure 5 The figure is an internal structure diagram of a computer device for implementing the textile composite impact energy absorption simulation method according to some embodiments of the present application. The textile composite impact energy absorption simulation method in the above embodiments can be implemented by the computer device shown in the figure, which comprises at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304. Figure 5

[0068] The processor 301 can be a general central processing unit (CPU), an application-specific integrated circuit (ASIC) or one or more for controlling the execution of the textile composite impact energy absorption simulation method in the present application.

[0069] The communication bus 302 is used for transmitting information between the above components.

[0070] ​The memory 303 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk or other magnetic storage device, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation. The memory 303 can exist independently of the processor 301, and can be connected to the processor 301 via the communication bus 302. The memory 303 can also be integrated with the processor 301.

[0071] The memory 303 is configured to store program codes of the embodiments of the present application, and the processor 301 is configured to execute the program codes stored in the memory 303. The program codes can include one or more software modules. The textile composite impact energy absorption simulation method in the above-described embodiments can be implemented by the processor 301 and one or more software modules in the program codes in the memory 303.

[0072] The communication interface 304 is configured to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), and the like, using any transceiver-like mechanism.

[0073] In a specific implementation, as an example, the computer device can include multiple processors, and each of the processors can be a single-CPU processor or a multi-CPU processor. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0074] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0075] In addition, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the textile composite impact energy absorption simulation method described above.

[0076] To sum up, in the textile composite impact energy absorption simulation method, system, device and medium disclosed by the embodiments of the present application, the tomographic images of the textile composites prepared in the same batch are obtained; the tomographic images are subjected to connected domain analysis, and then the equivalent diameter distribution characteristics of the pore defects in the textile composites are determined according to the analysis results; the random simulation is performed based on the equivalent diameter distribution characteristics in combination with the mesoscopic geometric model of the textile composites, so as to obtain the random pore space distribution of the textile composites in the resin-rich region; the random pore space distribution is subjected to geometric difference with the mesoscopic geometric model, and then the mesoscopic finite element model containing the pore defects is constructed; the material properties are given to the mesoscopic finite element model, and the explicit dynamics analysis of the impact load is performed, and then the impact energy absorption performance of the textile composites prepared in the same batch is determined according to the analysis results; and the systematic deviation caused by the idealization of the impact energy absorption performance prediction model can be reduced.

[0077] Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to the embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including all the changes and modifications falling within the scope of the present application.

[0078] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for simulating impact energy absorption of textile composite materials, characterized in that, Includes the following steps: Obtain tomographic images of textile composite materials prepared in the same batch; Connectivity analysis is performed on the tomographic scan images, and the equivalent diameter distribution characteristics of pore defects in textile composite materials are determined based on the analysis results. Based on the equivalent diameter distribution characteristics and combined with the microscopic geometric model of the textile composite material, a random simulation was performed to obtain the random pore space distribution of the textile composite material in the resin-rich region. The random pore spatial distribution is geometrically differiated from the microscopic geometric model to construct a microscopic finite element model containing pore defects. Material properties are assigned to the micro-finite element model and explicit dynamic analysis of impact load is performed. Then, the impact energy absorption performance of the textile composite material prepared in this batch is determined based on the analysis results.

2. The method as described in claim 1, characterized in that, The connected component analysis of the tomographic scan image specifically includes: The tomographic scan image is filtered and denoised to obtain a denoised tomographic scan image; The denoised tomographic image is processed using a threshold segmentation algorithm to generate a binary image of the pore region. The binary image of the pore region is labeled with three-dimensional connected components, and the pore-labeled image containing the spatial coordinates and voxel set of each pore is output as the analysis result.

3. The method as described in claim 1, characterized in that, The analysis results determine the equivalent diameter distribution characteristics of pore defects in textile composites, specifically including: The equivalent sphere diameter of each pore in the pore marking image is determined based on the analysis results; Construct a probability distribution function for the equivalent diameter using the equivalent sphere diameters of all pores; The distribution characteristics of the equivalent diameter of pore defects in textile composite materials are determined based on the probability distribution function of the equivalent diameter.

4. The method as described in claim 1, characterized in that, Based on the equivalent diameter distribution characteristics and combined with the microscopic geometric model of the textile composite material, a stochastic simulation was performed to obtain the specific random pore space distribution of the textile composite material in the resin-rich region, including: Obtain the mesoscopic geometric model of textile composite materials; Based on the equivalent diameter distribution characteristics, random numbers are generated to obtain a random pore size sequence; The preferred regions for pore distribution in the resin enrichment region are determined based on the fiber bundle arrangement structure in the micro-geometric model. Generate a corresponding sequence of pore center point coordinates based on the preferred region; By matching and combining the random pore diameter sequence with the pore center point coordinate sequence, the random pore spatial distribution of the textile composite material in the resin-rich region is obtained.

5. The method as described in claim 1, characterized in that, The specific steps for constructing a micro-finite element model containing porosity defects by geometrically differencing the random pore spatial distribution with the micro-geometric model include: Generate a set of corresponding pore geometric entities based on the pore diameter and coordinate parameters in the random pore spatial distribution; Geometric difference is performed between the set of pore geometric entities and the mesoscopic geometric model to obtain a geometric model containing pore defects; The geometric model is meshed using finite element methods to obtain a mesoscopic finite element model containing pore defects.

6. The method as described in claim 1, characterized in that, Assigning material properties to the micro-finite element model and performing explicit dynamic analysis of impact loads specifically includes: In the micro-finite element model, define the corresponding material properties for different interface regions; Specify the impact contact area and set the impact velocity parameters on the surface of the micro-finite element model. Explicit dynamic analysis was performed on a mesoscopic finite element model with assigned material properties and set impact parameters to obtain the analysis results.

7. The method as described in claim 1, characterized in that, Based on the analysis results, the impact energy absorption performance of the textile composite materials prepared in this batch was determined to include: Extract internal energy time history data from the analysis results obtained from the explicit dynamic analysis; The internal energy increment during the impact process is calculated based on the internal energy time history data. The internal energy increment is input into the macroscopic homogenization model to obtain the impact energy absorption performance of the textile composite material prepared in this batch.

8. A simulation system for impact energy absorption of textile composite materials, characterized in that, include: The acquisition module is used to acquire tomographic scan images of textile composite materials prepared in the same batch; The processing module is used to perform connected component analysis on the tomographic scan image, and then determine the equivalent diameter distribution characteristics of pore defects in textile composite materials based on the analysis results. The processing module is also used to perform random simulation based on the equivalent diameter distribution characteristics combined with the microscopic geometric model of the textile composite material to obtain the random pore space distribution of the textile composite material in the resin-rich region. The processing module is also used to perform geometric difference between the random pore spatial distribution and the microscopic geometric model, thereby constructing a microscopic finite element model containing pore defects. The execution module is used to assign material properties to the micro-finite element model and perform explicit dynamic analysis of impact loads, and then determine the impact energy absorption performance of the textile composite material prepared in this batch based on the analysis results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the textile composite material impact energy absorption simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the textile composite material impact energy absorption simulation method as described in any one of claims 1 to 7.

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