A three-dimensional fabric numerical simulation method based on microstructure simulation

Abstract

The present invention discloses a three-dimensional fabric numerical simulation method based on microstructure simulation, which relates to the technical field of fabric modeling and includes the following steps: S1, initial model establishment: using a gridless method, according to the microstructure information of the fabric, an initial model of the three-dimensional fabric is established, and its weaving process is simulated; S2, macro-mechanical analysis: converting the gridless model into a finite element model, and analyzing the overall deformation and stress distribution of the fabric when it is impacted; S3, micro-behavior simulation; in the present invention, by combining a variety of advanced simulation technologies, high-precision, multi-scale comprehensive analysis is achieved, which provides strong support for the design and performance evaluation of the fabric. At the same time, it also has the effects of flexibly handling complex structures and boundaries, in-depth research on microscopic phenomena, adaptive grid optimization and computational efficiency improvement, collaborative optimization and iterative improvement, and guiding new product development and design optimization.

Description

Technical Field

[0001] The present 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 various fields such as aerospace, automotive 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, thereby fully and deeply understanding 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 often find it difficult to accurately simulate their performance.

[0003] However, existing numerical simulation methods based on microstructure simulation often focus on simulating a single scale or a single physical field. They are unable to comprehensively and deeply analyze the performance of three-dimensional fabrics or fully consider the relationship between the fabric's microstructure and macroscopic properties, which limits the accuracy and reliability of the simulation results. Furthermore, these methods have certain limitations in dealing with the fabric's complex microstructure, nonlinear mechanical behavior, and the coupling of multiple physical fields.

[0004] Therefore, this paper proposes a three-dimensional fabric numerical simulation method based on microstructural simulation. This method combines multiple simulation techniques, including meshless methods, finite element methods, discrete element methods, and molecular dynamics simulation, to achieve a comprehensive analysis from the microscopic to the macroscopic, from the atomic to the overall structure. Through multi-scale simulation and iterative optimization, this method accurately simulates 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] 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, based on the microstructure information of the fabric, an 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-to-fiber interaction model;

[0013] S3.1.1.1. Introducing the friction model: Considering the effects of interfiber friction coefficient and surface roughness on sliding resistance;

[0014] S3.1.1.2. Adhesion force model is introduced to simulate the adhesion between fibers caused by van der Waals force and electrostatic force;

[0015] S3.1.1.3. Introducing fiber bending stiffness model: Considering the effect of fiber bending stiffness on inter-fiber interaction;

[0016] S3.1.2, fiber breakage criteria;

[0017] S3.1.2.1. Use a stress-based fracture criterion and 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 considers 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 interactions 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, 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 generated by the fracture.

[0030] Furthermore, in S3.1.3, when introducing the fiber slip model, 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 has slipped.

[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 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.

[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 for understanding and improving the performance of fabrics, and provides in-depth guidance for fabric design and optimization.

[0035] In macromechanical analysis, this method can automatically adjust the mesh density according to the magnitude of stress and strain, ensuring more detailed simulation of critical areas while avoiding the waste of computing 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.

[0036] Combining the results of macroscopic mechanical analysis and microscopic behavioral 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 re-simulated based on the results of multi-scale simulation. 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 a deeper understanding of the mechanical properties and failure mechanisms of fabrics, providing 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, thereby guiding the design and development process of new products and improving product performance and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

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

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

[0042] S1. Initial model establishment: Using the meshless method, based on the fabric's microstructural information, an initial model of the three-dimensional fabric is established, and its weaving process is simulated. The meshless method has advantages in dealing with complex shapes and irregular boundaries and is suitable for preliminary fabric modeling. The specific implementation steps are as follows:

[0043] Based on the collected microstructural information, a meshless method is used to define the initial position and shape of the fibers in three-dimensional space. This 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 accomplished 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 of the fibers. This involves fiber stretching, bending, interweaving, and other actions. Within the framework of the meshless method, these actions are achieved by updating the position and connection relationships of the fibers (i.e., points). This includes defining the movement rules of the fibers and applying external forces or constraints.

[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 number of time steps 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 experimental results or theoretical predictions to verify the accuracy of the simulation. If deviations or deficiencies are found in the simulation results, the model can be optimized and improved, such as adjusting the mesh density, modifying 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 as follows:

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

[0049] Based on the results of the preliminary calculation, an error estimate is performed by comparing the differences in the solutions between adjacent grid cells or using methods such as a posteriori error estimators. Based on the error estimation results, the areas that need to be densified or thinned are determined. In areas with large stresses and strains, the grid needs to be densified to improve the calculation accuracy; in areas with small stresses and strains, the grid can be appropriately thinned to save computing resources. Based on the determined densification / thinning areas, the grid is adjusted accordingly, adding nodes and cells in the densified 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: Finite element software usually has dedicated tools or modules to support adaptive meshing methods. You can enable the adaptive meshing function by setting corresponding parameters and options, and specify physical quantities such as stress and strain 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-to-fiber interaction model;

[0055] S3.1.1.1. Introducing a friction model: Consider the effects of inter-fiber friction coefficient and surface roughness on sliding resistance. Increased surface roughness increases the contact area, thereby increasing friction resistance. In DEM (discrete element method) simulations, different inter-fiber friction coefficients can be set to simulate different friction behaviors. For example, assuming the friction coefficient between fiber A and fiber B is μ, when fiber A attempts 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 coefficient of friction of A is 0.3 and the coefficient of friction of B is 0.5;

[0057] Data: Fibers A and B have the same surface roughness, but the friction coefficient is varied in the DEM simulation to observe the change in sliding resistance between the fibers. When fiber A slides on fiber B, the sliding resistance changes due to the different friction coefficients.

[0058] S3.1.1.2. Adhesion force model: simulate the adhesion between fibers caused by van der Waals and electrostatic forces. A specific contact model (such as the JKR contact model) is introduced to reflect the role of interparticle adhesion. 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 ,DEM simulation was used to observe the adhesion phenomenon between fibers due to van der Waals force when in contact, 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 inter-fiber interaction; in DEM simulation, the bending behavior of the fiber is simulated by setting the fiber bending stiffness parameter;

[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 DEM simulations, the deformation and stress distribution of fibers under external forces are observed by changing their bending stiffness parameters. In particular, when subjected to bending moments, the deformation and stress distribution of fibers A and B differ significantly.

[0064] S3.1.2, fiber breakage criteria;

[0065] S3.1.2.1. Use a stress-based fracture criterion to determine fiber fracture when the stress on the fiber exceeds its tensile strength. In DEM simulations, the stress distribution on the fiber can be monitored, and fiber fracture can be determined based on a 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 a DEM simulation, a fiber is considered broken when the stress on the fiber exceeds its set tensile strength. For example, in the simulation, it was observed that when fiber A was subjected to tension, it broke when the stress reached 100 MPa.

[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 fiber fracture behavior. Fiber fracture behavior is predicted by integrating the energy absorbed by the fiber during loading and comparing it with the fiber fracture energy.

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

[0070] Data: In 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 vests as an example, the following experiments and data analysis were carried out using the improved discrete element method:

[0074] Experimental setup: We selected body armor samples with different fiber types and structures. We used a high-speed camera to capture the dynamic behavior of the fibers during impact. We also used a high-precision force sensor to measure the forces acting on the body armor during impact.

[0075] Simulation Results: Detailed data on fiber interaction, fracture, and slippage under impact were obtained through improved discrete element method simulations. The simulations show that interfiber friction and adhesion significantly influence the performance of body armor, and the fiber fracture and slippage behaviors are consistent with experimental observations.

[0076] Comparison of experimental data with simulation results: The simulation results were compared with the experimental data, and good consistency was found between the two 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 was 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 a bulletproof vest is X centimeters, and the simulation result is X ± 0.1 centimeters, which is within an acceptable error range. In the simulation, a fiber was observed to break after impact, and the fracture location matched the experimental fracture location. The simulation also recorded data such as the slip distance and friction between fibers, providing strong support for further optimizing the bulletproof vest design.

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

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

[0081] B. Define the potential energy function: The potential energy function describes the interactions between molecules. For fiber systems, it may be necessary to consider intermolecular van der Waals forces, covalent bond forces, hydrogen bond forces, etc. Select an appropriate potential energy function and parameterize it to match the specific properties of the fiber;

[0082] C. Calculating 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 (e.g., the classical equations of motion);

[0084] E. 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 requires finding a balance between computational accuracy and computational efficiency.

[0085] F. Analyze simulation results: Observe the changes in the fiber microstructure during the simulation, such as bond lengths, bond angles, and the positions of atoms or molecules; analyze intermolecular interactions, such as van der Waals forces and charge interactions; and use statistical methods to process data, such as calculating radial distribution functions (RDFs) and diffusion coefficients.

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

[0087] Example description: Carbon fiber is selected as the simulation object, considering its graphite sheet structure composed of carbon atoms; the initial temperature is set to room temperature (about 300K), and the temperature is gradually increased 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 C-C bonds formed increases, resulting in a relatively fixed relative position of the graphite sheets and less sliding. 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, detailed data such as the carbon fiber microstructure image, bond length, bond angle, and changes in intermolecular interaction forces at different temperatures can be recorded.

[0089] S4. Multi-scale simulation: Combining 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, a more comprehensive understanding of the performance characteristics of fabrics can be achieved.

[0090] Example description:

[0091] Suppose we want to study the performance characteristics of a new type of composite fabric. 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, we can follow the following steps: Macro-mechanical analysis: Perform tensile and tear tests on the new composite fabric to measure its mechanical performance parameters (such as tensile strength, tear strength, etc.);

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

[0093] Multiscale modeling: Build a finite element model of the novel composite fabric at the macroscale and simulate the behavior of fibers and yarns at the microscale. Then, couple the two to form a multiscale model.

[0094] Multiscale simulation: Use multiscale models for simulation and analysis. For example, simulate the mechanical response and failure process of fabrics under different loads and conditions to evaluate their performance and durability. Comparing simulation results with experimental results can verify the accuracy of multiscale simulations and optimize model parameters.

[0095] 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.

[0096] Example description:

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

[0098] Results 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 improve the overall strength of the fabric, especially in areas where strength is insufficient;

[0100] Optimize model design: At the microscale, adjust the arrangement and density of fibers and increase the interweaving points between fibers to improve the interaction between fibers and the overall strength; at the macroscale, 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: Multi-scale simulations were performed again using the optimized model. The simulation results showed that the optimized fabric had significant improvements in areas with insufficient strength and improved overall strength.

[0102] Iterative optimization: If insufficient strength still exists 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 (FEM) is used to analyze the overall deformation and stress distribution of the fabric when it is impacted. The FEM performs well when dealing with continuous media problems and is suitable for macro-scale mechanical analysis. Assuming that the performance of a bulletproof fabric is to be analyzed when impacted by a bullet, the following is an example of using the FEM for analysis:

[0104] Model building: Establish a geometric model of the bulletproof fabric in 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 and high strength, and define appropriate material properties for the matrix layer, such as elastic modulus and Poisson's ratio;

[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 concentrations.

[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 on one side of the fabric to simulate the impact of a bullet by defining the velocity and shape of the bullet;

[0108] Solution and post-processing: Finite element software is used 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 maps, 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 the bullet-proof fabric, such as impact resistance and energy absorption capacity.

[0109] Furthermore, in S2.1, adaptive mesh refinement technology is introduced to automatically adjust the mesh density and shape according to the needs of the calculation process, taking into account and simulating the heterogeneity and nonlinearity of three-dimensional fabrics, while also introducing multi-physics field coupling analysis. One challenge of the finite element method is mesh generation and refinement. For three-dimensional fabrics with complex geometries and stress concentration areas, adaptive mesh refinement can automatically adjust the mesh density according to the magnitude of stress or strain. In areas with large stress or strain, the mesh is refined to provide more accurate results.

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

[0111] Considering the heterogeneity and nonlinearity of materials: Three-dimensional fabrics are usually composed of layers of different materials, fiber orientations, and densities, which leads to the macroscopic heterogeneity and nonlinearity of the material. In finite element analysis, this heterogeneity and nonlinearity can be simulated by introducing position-dependent functions of material properties 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 and Poisson's ratio) can be defined based on actual material data. By comparing these properties with tensile or compression test data from real experiments, the accuracy of the material heterogeneity and nonlinearity in the simulation can be verified.

[0113] Introducing multi-physics coupling analysis: In practical applications, three-dimensional fabrics may be affected by multiple physical fields (such as temperature fields, electromagnetic fields, etc.). 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 high-temperature environments, the influence of the temperature field on material properties can be introduced, and the coupling between the temperature field and the stress field can be simulated. By comparing with thermal stress test data from real experiments, 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 coefficient of friction is introduced because temperature changes may change the friction characteristics of the material;

[0116] Example description:

[0117] Suppose you are simulating a three-dimensional fabric for use in a high-temperature environment. The fabric must withstand high temperatures and high friction. 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 the following linear relationship exists between the friction coefficient (μ) and temperature (T): μ = μ0 - k × (T - T0); where μ0 is the friction coefficient at a reference temperature T0, and k is the slope of the friction coefficient as it changes 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); in each simulation step, update the temperature inside the fabric according to the environmental conditions (such as heat source, convection, etc.), which can be achieved through heat conduction equations or other thermodynamic models;

[0119] Applying a temperature-dependent friction coefficient: When calculating inter-fiber interactions, 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, the calculated friction coefficient is used 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. This fabric 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: The range of hydrogen bonding is set 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 moisture introduction: 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 decreases because water molecules occupy the fiber surface, reducing the formation of hydrogen bonds between fibers.

[0125] Simulation process: In the discrete element method simulation, for each pair of interacting polyester fibers, it is first checked whether the distance between them is within the range of hydrogen bonding; if so, the strength of the hydrogen bond is calculated based on the chemical properties of the fiber surface and the current moisture concentration; the calculated hydrogen bond strength is used in combination 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, the position and velocity of the fibers are updated 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 not only elastic deformation but also plastic deformation and creep.

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

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

[0131] Creep: Nylon fibers may creep when subjected to constant stress over a long period of time. In the simulation, a time-dependent creep model is introduced that describes the deformation behavior of the fiber under stress over a long period of time. For example, a creep rate is defined that is related to the fiber's stress, temperature, and time. During the simulation, the creep rate is calculated based on the current stress, temperature, and time, and the fiber deformation 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 more accurately simulated.

[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 fiber strength 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 simultaneously.

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

[0136] Apply the 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] Tracking stress-strain history and calculating cumulative damage: The stress-strain history of the fiber in each simulation step is recorded, and the Palmgren-Miner linear cumulative damage criterion is used to calculate the cumulative damage. Assuming that the tensile strength of the fiber is 100 MPa, after a series of stress cycles, the cumulative damage reaches 0.8 (not yet reaching 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 instantaneously exceed the tensile strength. At this time, the fiber is immediately judged to have tensile fractured, and the simulation of the fiber is terminated. Through this example, it can be seen that comprehensively considering the multiaxial 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, which is the maximum energy that a fiber can absorb during fracture, and the dynamic behavior of the fiber during fracture is considered, including the fracture velocity and the sound produced by 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: Based on experimental data or material properties, set the fracture toughness of the polyester fiber 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 impact load. In each time step, calculate and accumulate the energy absorbed by the fiber;

[0143] Fracture determination: When the cumulative energy reaches or exceeds 50J / cm 3 When the fiber breaks, the time point of the breakage is recorded and the breakage speed is calculated;

[0144] Evaluating 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 compared 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 subjected to impact.

[0145] Furthermore, in S3.1.3, when introducing the fiber slip model, the inter-fiber slip friction coefficient is considered and an appropriate value is set according to the properties of the fiber material and the contact surface. The concept of slip distance is also introduced. When the relative displacement between fibers exceeds the set slip distance, the fiber is judged to have slipped.

[0146] Example description:

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

[0148] Set the sliding friction coefficient: Based on the contact properties between cotton fibers and air (or other fibers in the fabric), set the sliding friction coefficient to 0.3. This means that when the cotton fibers slide on the contact surface, they will experience a resistance equivalent to 0.3 times their normal pressure.

[0149] Setting the critical slip distance: Based on the microstructure of cotton fibers and the adhesion between fibers, the critical slip distance is set to 0.1 micron. This means that when the relative displacement between cotton fibers exceeds 0.1 micron, the fibers are considered 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 friction coefficient is applied to calculate the slip resistance. 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. Through this method, the fiber slip behavior of three-dimensional fabrics under external forces can be simulated more accurately, 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 microstructural simulation combines meshless methods, finite element methods, discrete element methods, and molecular dynamics simulations to achieve comprehensive analysis from the microscopic to the macroscopic, from atomic to the overall structure. 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 handling of complex structures and boundaries: The meshless method enables flexible handling of three-dimensional fabrics with complex shapes and irregular boundaries, overcoming the limitations of traditional meshing methods in handling complex models. Furthermore, the method can accurately simulate the weaving process of the fabric, providing an accurate foundation 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-fiber interactions, fiber breakage, and fiber slippage. This microscopic information is crucial for understanding and improving fabric performance, providing in-depth guidance for fabric design and optimization.

[0156] Adaptive Mesh Optimization and Computational Efficiency Improvement: In macromechanical analysis, this method automatically adjusts mesh density based on stress and strain, ensuring more detailed simulations in critical areas while avoiding the waste of computational resources caused by over-refinement in non-critical areas. This adaptive mesh optimization strategy significantly improves computational efficiency, making the simulation process more efficient and reliable.

[0157] Collaborative Optimization and Iterative Improvement: Combining the results of macroscopic mechanical analysis and microscopic behavioral simulation to conduct multi-scale simulations allows for the collaborative optimization of simulation methods at different scales. Simultaneously, the initial model is optimized based on the results of multi-scale simulations and re-simulated for verification. 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 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 gain a deeper understanding of the mechanical properties and failure mechanisms of fabrics, providing strong support for new product development and design optimization. Through simulation analysis, researchers can predict fabric performance, identify potential problems and areas for improvement, and thus guide the design and development of new products, improving their performance and competitiveness.

[0159] In summary, this method, by combining multiple advanced simulation techniques, achieves high-precision, multi-scale comprehensive analysis, providing strong support for fabric design and performance evaluation. Furthermore, it offers advantages such as flexible handling of complex structures and boundaries, in-depth study of microscopic phenomena, adaptive mesh optimization and computational efficiency improvements, collaborative optimization and iterative enhancements, and guidance for new product development and design optimization.

[0160] The supplementary summary is:

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

[0162] During macromechanical analysis, adaptive mesh refinement technology automatically adjusts the mesh density and shape to accommodate the heterogeneity and nonlinearity of the fabric, thereby improving analysis accuracy. Adaptive mesh adjustment also effectively improves computational efficiency, avoiding resource waste caused by over-refinement in non-critical areas.

[0163] In-depth simulation of microscopic phenomena:

[0164] The discrete element method (DEM) simulates microscopic phenomena such as interfiber interactions, fiber breakage, and fiber slip, providing deeper insights into fabric performance. The influence of various factors, such as temperature, moisture, and lubricants, on interfiber friction and adhesion is considered, resulting in simulation results that are more realistic. The fiber bending stiffness model accounts for the nonlinear behavior of fibers under load, such as plastic deformation and creep, further improving simulation accuracy.

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

[0166] The introduction of stress- and energy-based fracture criteria, along with a fiber slip model, enables more accurate simulation of fiber fracture and slip processes, providing deeper guidance for fabric design and optimization. The fracture criteria consider factors such as fiber compressive strength, shear strength, and fatigue life, resulting in more comprehensive simulation results.

[0167] Multi-scale simulation and iterative optimization:

[0168] Combining the results of macroscopic mechanical analysis and microscopic behavioral simulation to conduct multi-scale simulations can fully leverage the advantages of different scale simulation methods and achieve collaborative optimization. Through iterative optimization, the reliability and accuracy of the simulation can be gradually improved, providing a more reliable basis for fabric design and performance evaluation.

[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 optimal solution can be quickly screened, shortening product development cycles and reducing costs.

[0171] In summary, this method achieves a comprehensive analysis from macro to micro by combining multiple 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, based on the microstructure information of the fabric, an 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 mesh density based on stress and strain. Specifically, by introducing adaptive mesh refinement technology and automatically adjusting the mesh density and shape 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 also introduced. 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-to-fiber interaction model; S3.1.1.

1. Introducing the friction model: Considering the effects of interfiber friction coefficient and surface roughness on sliding resistance; S3.1.1.

2. Adhesion force model is introduced to simulate the adhesion between fibers caused by van der Waals force and electrostatic force; S3.1.1.

3. Introducing fiber bending stiffness model: Considering the effect of fiber bending stiffness on inter-fiber interaction; S3.1.2, fiber breakage criteria; S3.1.2.

1. Use a stress-based fracture criterion and 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 considers 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 interactions 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. The 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 subjected to impact.

3. 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.

4. 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 and hydrogen bonds, and the effects of moisture and lubricants on adhesion are introduced.

5. 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.

6. 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.

7. 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.

8. The three-dimensional fabric numerical simulation method based on microstructure simulation according to claim 1, characterized in that: In S3.1.3, when introducing the fiber slip model, 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. The concept of slip distance is also introduced. When the relative displacement between fibers exceeds the set slip distance, it is determined that the fiber has slipped.

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