Three-dimensional fabric numerical simulation method based on microstructure simulation

Abstract

The invention discloses a three-dimensional fabric numerical simulation method based on microstructure simulation, which relates to the technical field of fabric modeling, and comprises the following steps: S1, initial model establishment: establishing an initial model of a three-dimensional fabric according to microstructure information of the fabric by using a meshless method, and simulating a weaving process of the three-dimensional fabric; s2, macroscopic mechanical analysis: converting the meshless model into a finite element model, and analyzing overall deformation and stress distribution of the fabric when the fabric is impacted; s3, microcosmic behavior simulation; according to the invention, by combining a plurality of advanced simulation technologies, high-precision and multi-scale comprehensive analysis is realized, powerful support is provided for design and performance evaluation of fabrics, and meanwhile, the detection accuracy is improved. And the method also has the effects of flexibly processing complex structures and boundaries, deeply researching microscopic phenomena, optimizing the self-adaptive grids, improving the calculation efficiency, cooperatively optimizing, iteratively improving and guiding new product development and design optimization.

Description

Technical Field

[0001] The invention relates to the technical field of fabric modeling, and in particular to a three-dimensional fabric numerical simulation method based on microstructure simulation. Background Art

[0002] In the textile industry, three-dimensional fabrics are widely used in many fields, such as aerospace, automobile manufacturing, and biomedicine, due to their unique structure and properties. However, due to their complex microstructure and mechanical behavior, the design and performance evaluation of three-dimensional fabrics has always been a challenging problem. Although traditional experimental methods can provide intuitive results, they are often limited by experimental conditions, time and cost, and cannot fully and deeply analyze the properties of fabrics. In recent years, with the development of computer technology, numerical simulation methods have gradually become a powerful tool for studying the properties of three-dimensional fabrics. Through numerical simulation, researchers can simulate the microstructure and mechanical behavior of fabrics in a virtual environment, so as to fully and deeply understand their properties. However, due to the complex microstructure, nonlinear mechanical behavior and multiple physical field coupling characteristics of three-dimensional fabrics, traditional numerical simulation methods are often difficult to accurately simulate their performance.

[0003] However, existing numerical simulation methods based on microstructure simulation often only focus on the simulation of a single scale or a single physical field, and cannot comprehensively and deeply analyze the performance of three-dimensional fabrics, nor can they fully consider the relationship between the microstructure and macroscopic performance of fabrics, resulting in limited accuracy and reliability of simulation results. In addition, these methods still have certain limitations in dealing with the complex microstructure, nonlinear mechanical behavior and coupling of multiple physical fields of fabrics.

[0004] Therefore, this paper proposes a three-dimensional fabric numerical simulation method based on microstructure simulation. This method combines multiple simulation techniques such as meshless method, finite element method, discrete element method and molecular dynamics simulation to achieve a comprehensive analysis from micro to macro, from atoms to the overall structure. Through multi-scale simulation and iterative optimization, this method can accurately simulate the microstructure and mechanical behavior of fabrics, providing strong support for fabric design and performance evaluation. Summary of the invention

[0005] The purpose of the present invention is to provide a three-dimensional fabric numerical simulation method based on microstructure simulation to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides a three-dimensional fabric numerical simulation method based on microstructure simulation, comprising the following steps:

[0007] S1. Initial model establishment: Using the meshless method, according to the microstructure information of the fabric, the initial model of the three-dimensional fabric is established, and its weaving process is simulated;

[0008] S2. Macromechanical analysis: convert the meshless model into a finite element model to analyze the overall deformation and stress distribution of the fabric when it is impacted;

[0009] S2.1. Automatically adjust the density of the grid according to the magnitude of stress and strain;

[0010] S3, Microscopic behavior simulation: Based on the results of macroscopic mechanical analysis, key areas and locations of interest are determined;

[0011] S3.1, Discrete Element Method: In these areas, the discrete element method is used to simulate the microscopic phenomena of fiber interaction, fiber breakage, and fiber slip;

[0012] S3.1.1, fiber-fiber interaction model;

[0013] S3.1.1.1. Introduce friction model: consider the influence of inter-fiber friction coefficient and surface roughness on sliding resistance;

[0014] S3.1.1.2, introduce the adhesion model: simulate the adhesion between fibers caused by van der Waals force and electrostatic force;

[0015] S3.1.1.3, Introduce fiber bending stiffness model: consider the effect of fiber bending stiffness on the interaction between fibers;

[0016] S3.1.2, fiber breakage criteria;

[0017] S3.1.2.1. Use a stress-based fracture criterion to determine that a fiber has fractured when the stress on the fiber exceeds its tensile strength;

[0018] S3.1.2.2, introduce an energy-based fracture criterion that takes into account the energy absorbed by the fiber before fracture;

[0019] S3.1.3, introduce the fiber slip model;

[0020] S3.2, Molecular dynamics simulation: Molecular dynamics simulation is used to deeply study the microstructure of fibers and the interaction between molecules;

[0021] S4. Multi-scale simulation: Combine the results of macroscopic mechanical analysis and microscopic behavior simulation to perform multi-scale simulation;

[0022] S5. Result optimization and feedback: Based on the results of multi-scale simulation, the initial model is optimized and re-simulated for verification; through continuous iterative optimization, more accurate and reliable simulation results are obtained.

[0023] Furthermore, in S2, the finite element method is used to analyze the overall deformation and stress distribution of the fabric when it is impacted.

[0024] Furthermore, in S2.1, by introducing adaptive mesh refinement technology and automatically adjusting the density and shape of the mesh according to the needs of the calculation process, the heterogeneity and nonlinearity of three-dimensional fabrics are considered and simulated, and multi-physics field coupling analysis is introduced.

[0025] Furthermore, in S3.1.1.1, the effect of temperature on the friction coefficient is introduced.

[0026] Furthermore, in S3.1.1.2, other types of adhesion are considered, including chemical bonds, hydrogen bonds, etc., and the effects of moisture and lubricants on adhesion are introduced.

[0027] Furthermore, in S3.1.1.3, the fiber bending stiffness model also considers the nonlinear behavior of the fiber during stress, including plastic deformation and creep.

[0028] Furthermore, in S3.1.2.1, the stress-based fracture criterion also considers the compressive strength and shear strength of the fiber, and introduces the concept of fatigue life to consider the reduction in strength of the fiber after long-term stress.

[0029] Furthermore, in S3.1.2.2, the concept of fracture toughness is introduced, that is, the maximum energy that the fiber can absorb during the fracture process, and the dynamic behavior of the fiber during the fracture process is considered, including the fracture speed and the sound generated by the fracture.

[0030] Furthermore, in S3.1.3, when the fiber slip model is introduced, the slip friction coefficient between fibers is considered, and an appropriate value is set according to the properties of the fiber material and the contact surface; and the concept of slip distance is introduced. When the relative displacement between fibers exceeds the set slip distance, it is determined that the fiber slips.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This method achieves a comprehensive analysis from micro to macro, from atoms to overall structure by combining the meshfree method, finite element method, discrete element method and molecular dynamics simulation. This multi-scale simulation strategy ensures high precision and accuracy of the simulation results, providing strong support for fabric design and performance evaluation.

[0033] The application of the meshless method enables this method to flexibly handle three-dimensional fabrics with complex shapes and irregular boundaries, overcoming the limitations of traditional grid methods in dealing with complex models; at the same time, this method can also accurately simulate the weaving process of the fabric, providing an accurate basis for subsequent mechanical analysis.

[0034] Through discrete element method and molecular dynamics simulation, this method can deeply study microscopic phenomena such as fiber interaction, fiber breakage, and fiber slippage; this microscopic information is crucial to understanding and improving the performance of fabrics, and provides in-depth guidance for fabric design and optimization.

[0035] In macro-mechanical analysis, this method can automatically adjust the density of the grid according to the magnitude of stress and strain, ensuring a more detailed simulation of key areas while avoiding the waste of computing resources caused by over-refinement of non-critical areas; this adaptive grid optimization strategy significantly improves the computational efficiency of the simulation, making the simulation process more efficient and reliable.

[0036] Combining the results of macroscopic mechanical analysis and microscopic behavior simulation to perform multi-scale simulation can achieve the coordinated optimization of simulation methods at different scales. At the same time, the initial model is optimized and designed based on the results of multi-scale simulation, and the simulation verification is re-performed. Through continuous iterative optimization, the reliability and accuracy of the simulation can be gradually improved. This iterative optimization process ensures the continuous optimization and improvement of the simulation results.

[0037] The high-precision, multi-scale simulation results provided by this method can help researchers gain an in-depth understanding of the mechanical properties and failure mechanisms of fabrics, and provide strong support for the development and design optimization of new products. Through simulation analysis, researchers can predict the performance of fabrics, identify potential problems and areas for improvement, and thus guide the design and development process of new products and improve product performance and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The schematic diagram is a three-dimensional fabric numerical simulation method based on microstructure simulation according to the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 , the present invention provides a technical solution:

[0041] See also Figure 1 As shown, a three-dimensional fabric numerical simulation method based on microstructure simulation includes the following steps:

[0042] S1. Initial model establishment: Use the meshless method to establish the initial model of the three-dimensional fabric based on the microstructure information of the fabric and simulate its weaving process; the meshless method has advantages in dealing with complex shapes and irregular boundaries and is suitable for the initial modeling of fabrics; the specific implementation steps are:

[0043] Based on the collected microstructure information, the meshless method is used to define the initial position and shape of the fiber in three-dimensional space, which is accomplished by distributing a series of points (representing the fibers) in space, and defining the interactions and connections between these points; this step is implemented using existing meshless method software (such as LS-dyna, Abaqus, Radioss, etc.); after the initial model is established, the weaving process of the fabric is simulated by simulating the movement and interweaving process of the fibers, involving the stretching, bending, interweaving and other actions of the fibers, which are implemented by updating the position and connection relationship of the fibers (i.e., points) within the framework of the meshless method; this includes defining the movement rules of the fibers, applying external forces or constraints, etc.;

[0044] S2. Macro-mechanical analysis: Convert the meshless model into a finite element model, including data extraction, meshing and model setting. That is, first, extract the key information of the fabric such as the geometric shape, material properties, boundary conditions, etc. from the meshless model. Then, use finite element software (such as ANSYS, Abaqus, COMSOL, etc.) to mesh the fabric. The density and shape of the mesh should be determined according to the complexity of the fabric and the analysis accuracy requirements. Finally, apply the extracted data (such as material properties, boundary conditions, etc.) to the finite element model; analyze the overall deformation and stress distribution of the fabric when it is impacted, that is, define the type of impact loading (such as point loading, surface loading, volume loading, etc.) and loading parameters (such as Loading speed, loading time, etc.), set the impact loading area and loading method in the finite element model; select the appropriate solver (such as implicit solver, explicit solver, etc.) and solution parameters (such as time step, convergence criterion, etc.), and set the termination conditions of the solution, such as reaching the maximum time step or meeting specific convergence conditions; run the solver to solve the finite element model, and use post-processing tools to view and analyze the solution results, including the overall deformation, stress distribution, strain distribution, etc. of the fabric; compare the simulation results with the experimental results or theoretical predictions to verify the accuracy of the simulation. If the simulation results are found to be biased or insufficient, the model can be optimized and improved, such as adjusting the mesh density, modifying the material properties, optimizing the loading method, etc.;

[0045] S2.1. Automatically adjust the density of the grid according to the magnitude of stress and strain; the implementation steps are:

[0046] Select or implement an adaptive mesh algorithm: Commonly used adaptive mesh methods include h-type method, p-type method, r-type method and hybrid method. The h-type method refines the mesh by adding nodes in the high error area; the p-type method improves the accuracy by increasing the order of the interpolation polynomial. Select or implement a suitable adaptive mesh algorithm based on specific problems and requirements;

[0047] Perform preliminary calculations: perform calculations on an initial grid to obtain the distribution of physical quantities such as stress and strain;

[0048] Error estimation and mesh refinement / sparseness:

[0049] According to the results of preliminary calculations, error estimation is performed by comparing the differences in solutions between adjacent grid cells or using methods such as a posteriori error estimators. According to the results of error estimation, the areas that need to be encrypted or sparse are determined. In areas with large stresses and strains, the grid needs to be encrypted to improve the calculation accuracy. In areas with small stresses and strains, the grid can be appropriately sparse to save computing resources. According to the determined encrypted / sparse areas, the grid is adjusted accordingly, adding nodes and cells in the encrypted areas or deleting nodes and cells in the sparse areas.

[0050] Repeat calculation and grid adjustment: recalculate on the adjusted grid, and perform error estimation and grid adjustment again; repeat this process until a certain accuracy requirement is met or a predetermined number of iterations is reached;

[0051] Implementation in finite element software: In finite element software, there are usually special tools or modules to support adaptive mesh methods. The adaptive mesh function can be enabled by setting corresponding parameters and options, and physical quantities such as stress and strain can be specified as the basis for mesh adjustment;

[0052] S3, Microscopic behavior simulation: Based on the results of macroscopic mechanical analysis, key areas and locations of interest are determined;

[0053] S3.1, Discrete Element Method: In these areas, the discrete element method is used to simulate the microscopic phenomena of fiber interaction, fiber breakage, and fiber slip;

[0054] S3.1.1, fiber-fiber interaction model;

[0055] S3.1.1.1. Introduce friction model: Consider the influence of friction coefficient between fibers and surface roughness on sliding resistance; the increase of surface roughness will increase the contact area, thereby increasing friction resistance. In DEM (discrete element method) simulation, the friction coefficient between different fibers can be set to simulate different friction behaviors. For example, assuming that the friction coefficient between fiber A and fiber B is μ, when fiber A tries to slide on fiber B, the sliding resistance (i.e. friction force) can be calculated as F_f=μ×F_n, where F_n is the normal contact force;

[0056] Example: Consider two different fibers A and B, the friction coefficient of A is 0.3 and the friction coefficient of B is 0.5;

[0057] Data: The surface roughness of fibers A and B is set to be the same, but the friction coefficient is changed in the DEM simulation to observe the change in the sliding resistance between fibers. When fiber A slides on fiber B, the sliding resistance will change due to the different friction coefficients.

[0058] S3.1.1.2, Introduce adhesion model: simulate the adhesion between fibers caused by van der Waals force and electrostatic force; introduce a specific contact model (such as JKR contact model) to reflect the effect of adhesion between particles. The JKR model introduces the concept of surface energy into the classic Hertz model to simulate the adhesion effect between particles;

[0059] Example: In the simulation, van der Waals force is introduced as the source of adhesion force, and the electrostatic force between fibers is considered negligible;

[0060] Data: The van der Waals force coefficient between fibers is set to 0.01 N / m 2 , the adhesion phenomenon caused by van der Waals force between fibers when in contact was observed through DEM simulation, especially the stress distribution near the fiber contact point;

[0061] S3.1.1.3. Introduce fiber bending stiffness model: consider the effect of fiber bending stiffness on the interaction between fibers; in DEM simulation, the bending behavior of the fiber is simulated by setting the bending stiffness parameters;

[0062] Example: The bending stiffness of fiber A is 1 GPa, and the bending stiffness of fiber B is 0.8 GPa;

[0063] Data: In the DEM simulation, the deformation and stress distribution of the fiber when subjected to external force are observed by changing the bending stiffness parameters of the fiber; especially when subjected to bending moment, the deformation and stress distribution of fibers A and B will be significantly different;

[0064] S3.1.2, fiber breakage criteria;

[0065] S3.1.2.1. Use a stress-based fracture criterion to determine that the fiber is broken when the stress on the fiber exceeds its tensile strength. In the DEM simulation, the stress distribution on the fiber can be monitored, and the fiber can be judged to be broken based on the set tensile strength threshold.

[0066] Example: The tensile strength of fiber A is 100 MPa, and the tensile strength of fiber B is 80 MPa;

[0067] Data: In the DEM simulation, when the stress of the fiber exceeds its set tensile strength, the fiber is judged to be broken; for example, in the simulation, it is observed that when fiber A is subjected to tension, when the stress reaches 100MPa, fiber A breaks;

[0068] S3.1.2.2. Introduce an energy-based fracture criterion that considers the energy absorbed by the fiber before fracture to more accurately predict the fracture behavior of the fiber; predict the fracture behavior of the fiber by integrating the energy absorbed by the fiber during loading and comparing it with the fracture energy of the fiber;

[0069] Example: Considering the energy absorbed by the fiber before it breaks, the breaking energy of fiber A is set to 5 J / m 2 , the breaking energy of fiber B is 4 J / m 2 ;

[0070] Data: In the DEM simulation, the energy absorbed by the fiber during loading is integrated and compared with the set fracture energy. When the energy absorbed by the fiber reaches its fracture energy, the fiber is judged to be broken. For example, in the simulation, it is observed that when fiber B is subjected to cyclic loading, the cumulative absorbed energy reaches 4 J / m 2 When , fiber B breaks;

[0071] S3.1.3, introduce the fiber slip model;

[0072] Examples and experimental data:

[0073] Taking the simulation of three-dimensional fabric in bulletproof vest as an example, the following experiments and data analysis were carried out using the improved discrete element method:

[0074] Experimental setup: We selected bulletproof vest samples with different fiber types and structures. We used a high-speed camera to capture the dynamic behavior of the fibers when they were impacted. We used a high-precision force sensor to measure the force on the bulletproof vest during the impact.

[0075] Simulation results: Through improved discrete element method simulation, detailed data on the interaction, fracture and slip of fibers under impact were obtained; the simulation results show that the friction and adhesion between fibers have an important influence on the performance of bulletproof vests, and the fracture and slip behaviors of fibers are consistent with experimental observations;

[0076] Comparison between experimental data and simulation results: The simulation results were compared with the experimental data, and it was found that the two had good consistency in terms of ballistic trajectory, penetration depth and fiber fracture location;

[0077] By comparing the simulation results of bulletproof vests with different fiber types and structures, it is found that certain specific fiber types and structures can significantly improve the performance of bulletproof vests;

[0078] Detailed data example: Under certain experimental conditions, the penetration depth of the bulletproof vest is X cm, and the simulation result is X±0.1 ​​cm, with an acceptable error. In the simulation, it was observed that a certain fiber broke after being impacted, and the fracture position was consistent with the fracture position in the experiment. In the simulation, data such as the slip distance and friction between fibers were also recorded, providing strong support for further optimizing the design of bulletproof vests.

[0079] S3.2, Molecular dynamics simulation: Molecular dynamics simulation is used to deeply study the microstructure of fibers and the interaction between molecules; the specific implementation steps are as follows:

[0080] A. Determine the simulation system: select the type of fiber to be studied (such as carbon fiber, polymer fiber, etc.); determine the initial structure of the fiber, such as the arrangement of atoms or molecules, initial position, speed, etc.; obtain it through experimental data, theoretical calculations or other simulation results;

[0081] B. Define potential energy function: The potential energy function describes the interaction between molecules. For fiber systems, it may be necessary to consider the van der Waals force, covalent bond force, hydrogen bond force, etc. between molecules; select a suitable potential energy function and parameterize it to match the specific properties of the fiber;

[0082] C. Calculate force: Calculate the magnitude and direction of the force acting on each molecule or atom based on the potential energy function; based on Newton's second law (F=ma) and the principles of classical mechanics;

[0083] D. Update position and velocity: Use the obtained force information to update the position and velocity of each molecule or atom; this is done by integrating the equations of motion (such as the classical equations of motion);

[0084] E. Perform time stepping: Repeat steps C and D, and gradually simulate the time evolution of the system according to the selected time step; the selection of the time step needs to find a balance between calculation accuracy and calculation efficiency;

[0085] F. Analyze simulation results: observe the changes in the microstructure of the fiber during the simulation, such as bond length, bond angle, position of atoms or molecules, etc.; analyze the interactions between molecules, such as van der Waals forces, charge interactions, etc.; use statistical methods to process data, such as calculating radial distribution function (RDF), diffusion coefficient, etc.;

[0086] The following is a molecular dynamics simulation example and experimental data about carbon fiber:

[0087] Example description: Select carbon fiber as the simulation object, considering its graphite sheet structure composed of carbon atoms; set the initial temperature to room temperature (about 300K), and gradually increase the temperature to observe the changes in the microstructure and intermolecular interactions of the carbon fiber;

[0088] Experimental data: In the initial state, the microstructure of carbon fiber is characterized by an orderly arrangement of graphite sheets; as the temperature increases, it is observed that the contact opportunities between adjacent graphite sheets increase, and the number of CC bonds formed increases, resulting in a relatively fixed relative position of the graphite sheets and not easy to slide; when the temperature rises to a certain critical value (such as 2500K), it is observed that the vicinity of the carbon ring is conducive to the nucleation of new carbon rings, thereby forming a carbon ring network and forming a ring structure at the carbon fiber boundary; during the entire simulation process, the carbon fiber microstructure image, bond length, bond angle and other detailed data at different temperatures, as well as the changes in the intermolecular interaction force can be recorded;

[0089] S4. Multi-scale simulation: Combine the results of macroscopic mechanical analysis and microscopic behavior simulation to conduct multi-scale simulation. By simulating the mechanical properties and microstructure of fabrics at different scales, we can have a more comprehensive understanding of the performance characteristics of fabrics.

[0090] Example description:

[0091] Suppose that the performance characteristics of a new type of composite fabric are studied. This fabric is composed of high-strength fibers and a special woven structure, and has excellent mechanical properties and durability. In order to achieve multi-scale simulation, the following steps can be followed: Macro-mechanical analysis: tensile tests and tear tests are performed on the new composite fabric to measure its mechanical performance parameters (such as tensile strength, tear strength, etc.);

[0092] Microscopic behavior simulation: Use molecular dynamics simulation or discrete element method to simulate microscopic phenomena such as the interaction between fibers and yarns, fracture and slip, which can help understand the mechanical properties and failure mechanisms of fibers and yarns;

[0093] Multiscale modeling: Building a finite element model of the novel composite fabric at the macroscale and simulating the behavior of fibers and yarns at the microscale, then coupling the two to form a multiscale model;

[0094] Multiscale simulation: Use multiscale models for simulation and analysis, for example, to simulate the mechanical response and failure process of fabrics under different loads and conditions to evaluate their performance and durability. By comparing simulation results with experimental results, the accuracy of multiscale simulation can be verified and model parameters can be optimized.

[0095] S5. Result optimization and feedback: Based on the results of multi-scale simulation, the initial model is optimized and re-simulated and verified; more accurate and reliable simulation results are obtained through continuous iterative optimization;

[0096] Example description:

[0097] Taking the strength optimization of fabric as an example, the specific implementation process is as follows:

[0098] Result analysis: In the multi-scale simulation, it was found that the strength of the fabric was obviously insufficient in some areas;

[0099] Determine optimization goals: The goal is to increase the overall strength of the fabric, especially in areas where strength is insufficient;

[0100] Optimize model design: on a micro scale, adjust the arrangement and density of fibers and increase the interweaving points between fibers to improve the interaction between fibers and the overall strength; on a macro scale, optimize the weaving structure and density of the fabric and increase the number of layers and thickness of the fabric to improve its overall strength and durability;

[0101] Re-simulation verification: Use the optimized model to re-run multi-scale simulation; the simulation results show that the optimized fabric has been significantly improved in areas with insufficient strength, and the overall strength has also been improved;

[0102] Iterative optimization: If there is still a problem of insufficient strength in certain specific areas, the fiber arrangement and density can be further adjusted, or the weaving structure and density of the fabric can be changed, and then the simulation verification process can be repeated until a satisfactory optimization effect is achieved.

[0103] Furthermore, in S2, the finite element method is used to analyze the overall deformation and stress distribution of the fabric when it is impacted. The finite element method performs well in dealing with continuous medium problems and is suitable for macro-scale mechanical analysis. Assuming that the performance of a bulletproof fabric is to be analyzed when it is impacted by a bullet, the following is an example of analysis using the finite element method:

[0104] Model building: Establish the geometric model of the bulletproof fabric in the finite element software, including the fiber layer, matrix layer, etc., and model it according to the actual structure and size of the fabric;

[0105] Material property definition: define the material properties of high-strength fibers for the fiber layer, such as high modulus, high strength, etc., and define appropriate material properties for the matrix layer, such as elastic modulus, Poisson's ratio, etc.;

[0106] Meshing: Divide the geometric model of the ballistic fabric into a fine finite element mesh. A denser mesh may be required at the interface between the fiber layer and the matrix layer to capture stress concentration phenomena.

[0107] Boundary conditions and load application: define the boundary conditions of the model, such as fixed constraints on the edge of the fabric, and then apply a load simulating a bullet impact on one side of the fabric by defining the velocity and shape of the bullet;

[0108] Solution and post-processing: Use finite element software to solve the model and obtain the overall deformation and stress distribution of the bullet-proof fabric under bullet impact. By drawing deformation diagrams and stress cloud diagrams, the tensile, compressive and shear deformations of the fiber layer, as well as the propagation and distribution of stress in the fabric can be observed. These results can help evaluate the performance of bullet-proof fabrics, such as impact resistance and energy absorption capacity.

[0109] Furthermore, in S2.1, by introducing adaptive mesh refinement technology and automatically adjusting the density and shape of the mesh according to the needs of the calculation process, the heterogeneity and nonlinearity of the three-dimensional fabric are considered and simulated, and multi-physics field coupling analysis is introduced; one of the challenges of the finite element method is the generation and refinement of the mesh. For three-dimensional fabrics with complex geometric shapes and stress concentration areas, adaptive mesh refinement can automatically adjust the density of the mesh according to the magnitude of the stress or strain; in areas with large stress or strain, the mesh will be refined to provide more accurate results;

[0110] Examples and experimental data: Suppose that when simulating the three-dimensional fabric in protective clothing, stress concentration is found in the shoulder area. Through adaptive mesh refinement, the mesh in the shoulder area is refined to obtain more accurate stress distribution data; the experimental data can be compared with the stress measurement data in the real experiment to verify the accuracy of the simulation;

[0111] Consider the heterogeneity and nonlinearity of materials: Three-dimensional fabrics are usually composed of layers of different materials, fiber directions and densities, which causes the material to exhibit heterogeneity and nonlinearity on a macro scale. In finite element analysis, this heterogeneity and nonlinearity can be simulated by introducing functions of material properties that vary with position or nonlinear constitutive relations;

[0112] Examples and experimental data: When simulating multi-layer composite fabrics, the material properties of each layer (such as elastic modulus, Poisson's ratio, etc.) can be defined based on actual material data. By comparing with the tensile or compression test data in real experiments, the accuracy of material heterogeneity and nonlinearity in the simulation can be verified;

[0113] Introducing multi-physics coupling analysis: Three-dimensional fabrics may be affected by multiple physical fields (such as temperature field, electromagnetic field, etc.) in practical applications. In finite element analysis, these effects can be simulated by introducing multi-physics coupling analysis;

[0114] Examples and experimental data: When simulating three-dimensional fabrics in a high-temperature environment, the influence of the temperature field on the material properties can be introduced, and the coupling between the temperature field and the stress field can be simulated. By comparing with the thermal stress test data in the real experiment, the accuracy of the multi-physics field coupling analysis in the simulation can be verified.

[0115] Furthermore, in S3.1.1.1, the effect of temperature on the friction coefficient is introduced because temperature changes may change the friction characteristics of the material;

[0116] Example description:

[0117] Assume that a three-dimensional fabric used in a high temperature environment is being simulated. The fabric needs to withstand high temperature and high friction conditions. In this case, the effect of temperature on the friction coefficient is particularly important. Define the temperature-friction coefficient relationship: Based on experimental data, assume that there is the following linear relationship between the friction coefficient (μ) and temperature (T): μ = μ0-k×(T-T0); where μ0 is the friction coefficient at the reference temperature T0, and k is the slope of the friction coefficient changing with temperature;

[0118] Temperature management during simulation: at the beginning of the simulation, set the ambient temperature T_env and the initial temperature T_i nit (which may be the same as the ambient temperature); at each simulation step, update the temperature inside the fabric according to the environmental conditions (such as heat sources, convection, etc.), which is achieved through heat conduction equations or other thermodynamic models;

[0119] Apply temperature-dependent friction coefficient: When calculating the interaction between fibers, first query the temperature T of the current simulation area; then, use the linear relationship defined above to calculate the corresponding friction coefficient μ; finally, use the calculated friction coefficient to calculate the sliding resistance between fibers; this method can more accurately simulate the effect of temperature on the friction coefficient, thereby more realistically reflecting the performance and behavior of fabrics in high temperature environments, which is of great significance for the design and evaluation of fabrics used in high temperature environments.

[0120] Furthermore, in S3.1.1.2, other types of adhesion are considered, including chemical bonds, hydrogen bonds, etc., and the effects of moisture and lubricants on adhesion are introduced;

[0121] Example description:

[0122] Suppose you are simulating a three-dimensional fabric made of polyester fibers that is used in a wet environment. There may be hydrogen bonding between the polyester fibers, and moisture can affect this adhesion.

[0123] Define the hydrogen bond model: set the range of hydrogen bonding to a certain threshold of the distance between fibers; the strength of hydrogen bonding is determined by factors such as the chemical properties of the fiber surface and temperature;

[0124] Effect of introducing moisture: Set a moisture concentration parameter, which represents the thickness or concentration of the moisture film on the fiber surface; when the moisture concentration increases, the strength of hydrogen bonds will decrease because moisture molecules will occupy the fiber surface and reduce the formation of hydrogen bonds between fibers;

[0125] Simulation process: In the discrete element method simulation, for each pair of interacting polyester fibers, first check whether the distance between them is within the range of hydrogen bonding; if within the range, calculate the strength of the hydrogen bond based on the chemical properties of the fiber surface and the current moisture concentration; use the calculated hydrogen bond strength, combined with other interaction forces (such as friction, van der Waals force, etc.) to calculate the total interaction force between the fibers; based on the total interaction force, update the position and velocity of the fiber to simulate the dynamic behavior of the fabric; this method can more accurately simulate the effect of moisture on the adhesion between fibers in three-dimensional fabrics, thereby better understanding the performance and behavior of the fabric.

[0126] Furthermore, in S3.1.1.3, the fiber bending stiffness model also considers the nonlinear behavior of the fiber during stress, including plastic deformation and creep;

[0127] For example:

[0128] Take a nylon fiber as an example. When subjected to external force, the nylon fiber may undergo plastic deformation and creep in addition to elastic deformation.

[0129] Bending stiffness: The bending stiffness of nylon fibers can be calculated based on their elastic modulus, cross-sectional area, and length. In the simulation, a bending stiffness coefficient related to these parameters can be defined and the corresponding bending moment can be calculated when the fiber is bent.

[0130] Plastic deformation: When the stress on nylon fiber exceeds its yield point, the fiber will undergo plastic deformation. In the simulation, a yield stress threshold can be set. When the fiber stress exceeds this threshold, the fiber is allowed to undergo plastic deformation. The accumulation of plastic deformation can be represented by the accumulated plastic strain, and the influence of this accumulated deformation on the fiber behavior is considered in subsequent calculations.

[0131] Creep: Nylon fibers may creep when subjected to constant stress for a long time. In the simulation, a time-dependent creep model is introduced, which describes the deformation behavior of the fiber under stress over a long period of time. For example, a creep rate is defined, which is related to the stress, temperature and time of the fiber. During the simulation, the creep rate is calculated based on the current stress, temperature and time, and the deformation of the fiber is updated. By integrating these mechanical models into the discrete element method simulation, the interaction between fibers and the nonlinear behavior of fibers under stress can be simulated more accurately.

[0132] Furthermore, in S3.1.2.1, the stress-based fracture criterion also considers the compressive strength and shear strength of the fiber, and introduces the concept of fatigue life to consider the reduction in strength of the fiber after long-term stress;

[0133] Example description:

[0134] Suppose the simulation is a fabric made of a certain synthetic fiber with known tensile strength, compressive strength and shear strength. During the simulation, the fiber is subjected to tensile, compressive and shear stresses at the same time.

[0135] Calculation of stress components: At each simulation step, the tensile, compressive and shear stresses on the fibers are calculated;

[0136] Application of fracture criteria: If the tensile stress exceeds the tensile strength, the fiber is judged to have fractured in tension; if the compressive stress exceeds the compressive strength, the fiber is judged to have fractured in compression (although this may be uncommon in some cases); if the shear stress exceeds the shear strength, the fiber is judged to have fractured in shear;

[0137] Track stress-strain history and calculate cumulative damage: record the stress-strain history of the fiber in each simulation step, and use the Palmgren-Miner linear cumulative damage criterion to calculate the cumulative damage; assuming that the tensile strength of the fiber is 100MPa, after a series of stress cycles, the cumulative damage reaches 0.8 (has not yet reached the critical value of 1); however, at a certain point in time, the fiber is suddenly subjected to an impact far exceeding its tensile strength, causing the tensile stress to instantly exceed the tensile strength. At this time, the fiber is immediately judged to have tensile fracture and the simulation of the fiber is terminated; through this example, it can be seen that the comprehensive consideration of the multi-axial stress state and fatigue life of the fiber can more accurately simulate the fracture behavior of the fiber in actual use.

[0138] Furthermore, in S3.1.2.2, the concept of fracture toughness is introduced, i.e., the maximum energy that a fiber can absorb during the fracture process, and the dynamic behavior of the fiber during the fracture process is considered, including the fracture velocity and the sound produced by the fracture;

[0139] Example description:

[0140] Suppose you are simulating the fracture behavior of a high-strength polyester fiber under impact loading;

[0141] Set fracture toughness: According to experimental data or material properties, the fracture toughness of the polyester fiber is set to 50J / cm 3 ;

[0142] Simulate the impact process: Use the discrete element method to simulate the deformation and stress changes of the fiber under the impact load. In each time step, the energy absorbed by the fiber is calculated and accumulated;

[0143] Fracture determination: When the accumulated energy reaches or exceeds 50J / cm 3 When the fiber breaks, the fiber is judged to be broken. At this time, the time point of the breakage is recorded and the breaking speed is calculated;

[0144] Evaluation of dynamic behavior: It can be observed that at the moment of fracture, the stress inside the fiber is suddenly released. This sudden release of stress can be likened to the emission source of sound waves, thereby indirectly evaluating the sound produced during fracture. In addition, the dynamic performance of the fiber can also be evaluated by analyzing the fracture speed. For example, if the fracture speed is very fast, it means that the fiber has good energy absorption and release capabilities when impacted.

[0145] Furthermore, in S3.1.3, when the fiber slip model is introduced, the slip friction coefficient between fibers is considered, and a suitable value is set according to the properties of the fiber material and the contact surface; and the concept of slip distance is introduced, and when the relative displacement between fibers exceeds the set slip distance, it is determined that the fiber slips;

[0146] Example description:

[0147] Suppose you are simulating the behavior of a cotton 3D fabric under shear force. In this simulation, you need to introduce a fiber slip model.

[0148] Set the sliding friction coefficient: According to the contact properties between cotton fiber and air (or other fibers in the fabric), the sliding friction coefficient is set to 0.3, which means that when the cotton fiber slides on the contact surface, it will be subject to a resistance equivalent to 0.3 times its positive pressure;

[0149] Setting critical slip distance: Based on the microstructure of cotton fibers and the adhesion between fibers, the critical slip distance is set to 0.1 microns, which means that when the relative displacement between cotton fibers exceeds 0.1 microns, the fibers are judged to have slipped;

[0150] Simulation process:

[0151] In the simulation, the position and state of each cotton fiber are tracked. When subjected to shear force, relative displacement occurs between the fibers. At each time step, the relative displacement between the fibers is calculated and compared with the critical slip distance. If the relative displacement exceeds the critical slip distance, the fiber is judged to have slipped, and the slip resistance is calculated using the slip friction coefficient. Based on the slip resistance and other mechanical parameters (such as the elastic modulus and tensile strength of the fiber), the position and state of the fiber are updated, and the simulation continues to the next time step. This method can more accurately simulate the fiber slip behavior of three-dimensional fabrics when subjected to external forces, thereby better understanding the mechanical properties and deformation mechanism of the fabric.

[0152] Summarize:

[0153] High-precision, multi-scale comprehensive analysis: This three-dimensional fabric numerical simulation method based on microstructure simulation achieves comprehensive analysis from micro to macro, from atoms to overall structure by combining meshless method, finite element method, discrete element method and molecular dynamics simulation. This multi-scale simulation strategy ensures high precision and accuracy of the simulation results, providing strong support for fabric design and performance evaluation.

[0154] Flexible processing of complex structures and boundaries: The application of the meshless method enables the method to flexibly process three-dimensional fabrics with complex shapes and irregular boundaries, overcoming the limitations of traditional mesh methods in processing complex models. At the same time, the method can also accurately simulate the weaving process of the fabric, providing an accurate basis for subsequent mechanical analysis.

[0155] In-depth study of microscopic phenomena: Through discrete element method and molecular dynamics simulation, this method can deeply study microscopic phenomena such as fiber interaction, fiber breakage, fiber slippage, etc. This microscopic information is crucial to understand and improve the performance of fabrics, and provides in-depth guidance for fabric design and optimization.

[0156] Adaptive mesh optimization and computational efficiency improvement: In macro-mechanical analysis, this method can automatically adjust the density of the mesh according to the magnitude of stress and strain, ensuring a more refined simulation of key areas while avoiding the waste of computational resources caused by over-refinement of non-critical areas. This adaptive mesh optimization strategy significantly improves the computational efficiency of the simulation, making the simulation process more efficient and reliable.

[0157] Collaborative optimization and iterative improvement: Combining the results of macroscopic mechanical analysis and microscopic behavior simulation to perform multi-scale simulation can achieve collaborative optimization of simulation methods at different scales. At the same time, the initial model is optimized and designed based on the results of multi-scale simulation, and the simulation verification is re-performed. Through continuous iterative optimization, the reliability and accuracy of the simulation can be gradually improved. This iterative optimization process ensures the continuous optimization and improvement of the simulation results.

[0158] Guiding new product development and design optimization: The high-precision, multi-scale simulation results provided by this numerical simulation method can help researchers deeply understand the mechanical properties and failure mechanisms of fabrics, and provide strong support for the development and design optimization of new products. Through simulation analysis, researchers can predict the performance of fabrics, find potential problems and improvement points, thereby guiding the design and development process of new products and improving product performance and competitiveness.

[0159] In summary, this method combines a variety of advanced simulation technologies to achieve high-precision, multi-scale comprehensive analysis, providing strong support for fabric design and performance evaluation. At the same time, this method also has the beneficial effects of flexibly handling complex structures and boundaries, in-depth research on microscopic phenomena, adaptive mesh optimization and computational efficiency improvement, collaborative optimization and iteration improvement, and guiding new product development and design optimization.

[0160] The supplementary summary is:

[0161] Adaptive mesh optimization and accuracy of macromechanical analysis:

[0162] In the process of macro-mechanical analysis, the introduction of adaptive mesh refinement technology can automatically adjust the density and shape of the mesh to adapt to the heterogeneity and nonlinear characteristics of the fabric, thereby improving the accuracy of the analysis. Adaptive adjustment of the mesh can also effectively improve the calculation efficiency and avoid the waste of resources caused by excessive refinement of non-critical areas.

[0163] In-depth simulation of microscopic phenomena:

[0164] The use of discrete element method to simulate microscopic phenomena such as fiber interaction, fiber breakage, and fiber slip provides more in-depth information for understanding the performance of fabrics. The influence of temperature, moisture, lubricant and other factors on the friction coefficient and adhesion between fibers is considered, making the simulation results closer to reality. The fiber bending stiffness model takes into account the nonlinear behavior of fibers in the force process, such as plastic deformation and creep, which further improves the accuracy of the simulation.

[0165] Detailed analysis of fracture criteria and slip models:

[0166] The introduction of stress- and energy-based fracture criteria and fiber slip models can more accurately simulate the fracture and slip processes of fibers, providing more in-depth guidance for fabric design and optimization. The fracture criteria take into account factors such as fiber compressive strength, shear strength, and fatigue life, making the simulation results more comprehensive.

[0167] Multi-scale simulation and iterative optimization:

[0168] Combining the results of macroscopic mechanical analysis and microscopic behavior simulation to conduct multi-scale simulation can give full play to the advantages of simulation methods at different scales and achieve collaborative optimization. Through iterative optimization, the reliability and accuracy of the simulation can be gradually improved, providing a more reliable basis for the design and performance evaluation of fabrics.

[0169] New product development and performance improvement:

[0170] The detailed information and in-depth analysis provided by this method can help researchers better understand the mechanical properties and failure mechanisms of fabrics, thereby guiding the development and optimization of new products. By simulating the performance of different design options, the best option can be quickly screened out, shortening the product development cycle and reducing costs.

[0171] In summary, this method achieves a comprehensive analysis from macro to micro by combining a variety of advanced simulation technologies, providing a high-precision and high-efficiency simulation tool for fabric design and performance evaluation, with significant beneficial effects.

Claims

1. A three-dimensional fabric numerical simulation method based on microstructure simulation, characterized in that: The following steps are involved: S1. Initial model establishment: Using the meshless method, according to the microstructure information of the fabric, the initial model of the three-dimensional fabric is established, and its weaving process is simulated; S2. Macromechanical analysis: convert the meshless model into a finite element model to analyze the overall deformation and stress distribution of the fabric when it is impacted; S2.

1. Automatically adjust the density of the grid according to the magnitude of stress and strain; S3, Microscopic behavior simulation: Based on the results of macroscopic mechanical analysis, key areas and locations of interest are determined; S3.1, Discrete Element Method: In these areas, the discrete element method is used to simulate the microscopic phenomena of fiber interaction, fiber breakage, and fiber slip; S3.1.1, fiber-fiber interaction model; S3.1.1.

1. Introduce friction model: consider the influence of inter-fiber friction coefficient and surface roughness on sliding resistance; S3.1.1.2, introduce the adhesion model: simulate the adhesion between fibers caused by van der Waals force and electrostatic force; S3.1.1.3, Introduce fiber bending stiffness model: consider the effect of fiber bending stiffness on the interaction between fibers; S3.1.2, fiber breakage criteria; S3.1.2.

1. Use a stress-based fracture criterion to determine that a fiber has fractured when the stress on the fiber exceeds its tensile strength; S3.1.2.2, introduce an energy-based fracture criterion that takes into account the energy absorbed by the fiber before fracture; S3.1.3, introduce the fiber slip model; S3.2, Molecular dynamics simulation: Molecular dynamics simulation is used to deeply study the microstructure of fibers and the interaction between molecules; S4. Multi-scale simulation: Combine the results of macroscopic mechanical analysis and microscopic behavior simulation to perform multi-scale simulation; S5. Result optimization and feedback: Based on the results of multi-scale simulation, the initial model is optimized and re-simulated for verification; through continuous iterative optimization, more accurate and reliable simulation results are obtained.

2. A three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S2, the finite element method is used to analyze the overall deformation and stress distribution of the fabric when it is impacted.

3. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S2.1, adaptive mesh refinement technology is introduced, and the density and shape of the mesh are automatically adjusted according to the needs of the calculation process. The heterogeneity and nonlinearity of three-dimensional fabrics are considered and simulated, and multi-physics field coupling analysis is introduced.

4. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.1.1, the effect of temperature on the coefficient of friction is introduced.

5. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.1.2, other types of adhesion are considered, including chemical bonds, hydrogen bonds, etc., and the effects of moisture and lubricants on adhesion are introduced.

6. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.1.3, the fiber bending stiffness model also considers the nonlinear behavior of the fiber during stress, including plastic deformation and creep.

7. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.2.1, the stress-based fracture criterion also considers the compressive strength and shear strength of the fiber, and introduces the concept of fatigue life to consider the reduction in strength of the fiber after long-term stress.

8. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.2.2, the concept of fracture toughness is introduced, which is the maximum energy that the fiber can absorb during the fracture process, and the dynamic behavior of the fiber during the fracture process is considered, including the fracture speed and the sound produced by the fracture.

9. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.3, when the fiber slip model is introduced, the slip friction coefficient between fibers is considered, and an appropriate value is set according to the properties of the fiber material and the contact surface; and the concept of slip distance is introduced. When the relative displacement between fibers exceeds the set slip distance, the fiber is judged to have slipped.

Images