A yarn fine modeling based permeability determination method and device

Abstract

This invention relates to the field of composite material molding technology, and particularly to a method and apparatus for determining permeability based on refined yarn modeling. The method includes: discretizing each yarn in a yarn fabric into multiple beam unit chains to obtain a loose fabric model, where each beam unit chain includes multiple beam unit nodes; compacting the loose fabric model to obtain a compacted fabric model including a yarn gap region and a yarn bundle interior region; simulating the resin flow process in the compacted fabric model to obtain a first flow velocity in the yarn gap region and a second flow velocity in the yarn bundle interior region; and obtaining the resin permeability in the compacted fabric model based on the first flow velocity, the second flow velocity, the highest and lowest point node information, and the leftmost and rightmost point node information. This technical solution effectively solves the problem of large permeability prediction errors caused by neglecting compaction deformation in traditional ideal modeling, thus improving the accuracy of permeability calculation.

Description

Technical Field

[0001] This invention relates to the field of composite material molding technology, and in particular to a method and apparatus for determining permeability based on fine yarn modeling. Background Technology

[0002] In the liquid molding process of composite materials, permeability is an important physical parameter for measuring the ease with which liquid resin flows in fiber fabric reinforcement. Therefore, accurate acquisition of permeability is the key to optimizing process design and realizing numerical simulation of the molding process.

[0003] In existing technologies, resin permeability is mainly predicted using ideal unit cell modeling, which assumes that the yarn path is smooth and the cross-sectional shape is regular and uniform, and uses computational fluid dynamics to simulate the flow of resin within the unit cell. However, this method is difficult to reflect the yarn compression, cross-sectional distortion, and fiber path bending deformation during the actual compaction process, resulting in low permeability prediction accuracy and large deviation from the actual molding, making it difficult to meet the high-precision simulation requirements for complex fabric molding.

[0004] Therefore, those skilled in the art urgently need to develop a new technical solution to address the above problems. Summary of the Invention

[0005] This invention provides a method and apparatus for determining permeability based on refined yarn modeling, which can effectively solve the problem of large permeability prediction error caused by neglecting compaction deformation in traditional ideal modeling, and improve the accuracy of permeability calculation.

[0006] In a first aspect, embodiments of the present invention provide a method for determining permeability based on refined yarn modeling, comprising: Based on the geometric information of the yarn fabric, each yarn in the yarn fabric is discretized into multiple beam unit chains to obtain a loose fabric model. Each beam unit chain includes multiple beam unit nodes. The loose fabric model is compacted to obtain a compacted fabric model, which includes a yarn gap region and a yarn bundle interior region. The process of resin flowing in the compacted fabric model is simulated. Based on the resin density, resin viscosity and pressure distribution inside the compacted fabric model, the first flow velocity of the resin flowing in the yarn gap region and the second flow velocity of the resin flowing in the region inside the yarn bundle are obtained. Based on the compacted fabric model, the highest point node information of the topmost beam unit chain, the lowest point node information of the bottommost beam unit chain, the leftmost end node information of the leftmost beam unit chain, and the rightmost end node information of the rightmost beam unit chain are obtained. Based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information, the resin permeation rate in the compacted fabric model is obtained.

[0007] Secondly, embodiments of the present invention provide a permeability determination device based on refined yarn modeling, comprising: The loose model construction module, based on the geometric information of the yarn fabric, discretizes each yarn in the yarn fabric into multiple beam element chains to obtain a loose fabric model. Each beam element chain includes multiple beam element nodes. A compaction model construction module is connected to the loose model construction module to compact the loose fabric model and obtain a compacted fabric model. The compacted fabric model includes a yarn gap region and a yarn bundle internal region. The resin flow rate calculation module is connected to the compaction model construction module. It simulates the process of resin flowing in the compacted fabric model. Based on the resin density, resin viscosity and pressure distribution inside the compacted fabric model, it obtains the first flow rate of resin flowing in the yarn gap region and the second flow rate of resin flowing in the region inside the yarn bundle. The contour information extraction module is connected to the resin flow rate calculation module. Based on the compacted fabric model, it obtains the highest point node information of the top beam unit chain, the lowest point node information of the bottom beam unit chain, the leftmost end node information of the leftmost beam unit chain, and the rightmost end node information of the rightmost beam unit chain. The permeability calculation module, connected to the contour information extraction module, obtains the resin permeability in the compacted fabric model based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information.

[0008] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in the first aspect of the present invention.

[0009] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method described in the first aspect of the present invention.

[0010] The present invention provides a method and apparatus for determining permeability based on fine-grained yarn modeling. By discretizing the yarn into beam unit chains to construct a loose model, and then compacting it to obtain a compacted fabric model including the yarn gaps and the internal region of the filament bundle, the method can realistically reproduce the deformation characteristics and dual-region flow structure of the compacted yarn. This accurately reflects the actual flow behavior of resin within the fabric, effectively solving the problems of large permeability prediction errors and low accuracy caused by neglecting compaction deformation in traditional ideal modeling. It significantly improves the accuracy of permeability calculation and better meets the simulation and design needs of composite material molding processes. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for determining permeability based on yarn fine modeling, provided by an embodiment of the present invention; Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention; Figure 3 This is a structural diagram of a permeability determination device based on yarn fine modeling, provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating a loose fabric model according to one embodiment; Figure 5 This is a schematic diagram illustrating a compacted fabric model according to one embodiment; Figure 6 This is a schematic diagram illustrating the effect of yarn geometry reconstruction according to one embodiment; Figure 7 This is a schematic diagram illustrating the resin content at different times in a compacted fabric model according to an embodiment. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1This invention provides a method for determining permeability based on refined yarn modeling, the method comprising: Step 100: Based on the geometric information of the yarn fabric, each yarn in the yarn fabric is discretized into multiple beam unit chains to obtain a loose fabric model. Each beam element chain includes multiple beam element nodes; Step 102: Compact the loose fabric model to obtain a compacted fabric model; The compacted fabric model includes the yarn gap region and the internal region of the yarn bundle; Step 104: Simulate the process of resin flowing in the compacted fabric model. Based on the resin density, resin viscosity and pressure distribution inside the compacted fabric model, obtain the first flow velocity of resin flowing in the yarn gap region and the second flow velocity of resin flowing in the internal region of the yarn bundle. Step 106: Based on the compacted fabric model, obtain the highest point node information of the top beam element chain, the lowest point node information of the bottom beam element chain, the leftmost node information of the leftmost beam element chain, and the rightmost node information of the rightmost beam element chain. Step 108: Based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information, obtain the resin permeability in the compacted fabric model.

[0015] In this embodiment, based on the geometric information of the yarn fabric, each yarn in the fabric is discretized into multiple beam unit chains, and each beam unit chain has multiple first-order beam unit nodes, constructing a loose fabric model. In a preferred embodiment, a bundle of yarn is discretized into 50 beam unit chains. A pressure load is applied to the loose fabric model for compaction, resulting in a compacted fabric model. The yarn gap region and the yarn bundle interior region are distinguished within the compacted fabric model. The yarn gap region is the free-flow region where resin flows through the yarn gaps within the model, while the yarn bundle interior region is the seepage region where resin penetrates the yarn interior. The flow process of resin within the compacted fabric model is simulated. Combining the resin density, viscosity, and internal pressure distribution, the first flow velocity of resin in the yarn gap region and the second flow velocity in the yarn bundle interior region are calculated. The boundary node information of the top, bottom, leftmost, and rightmost beam unit chains is extracted from the compacted fabric model. Based on the first flow velocity, the second flow velocity, and the aforementioned boundary node information, the resin permeability in the compacted fabric model is calculated. By discretizing the yarn into beam unit chains and compacting them, a two-region compacted fabric model is constructed, including the yarn gap region and the internal region of the filament bundle. The resin flow velocity in each region is calculated separately. Then, by combining the boundary node information of the outer contour of the compacted fabric model, the resin permeability in the refined compacted fabric model is calculated. This allows for a more accurate prediction of the resin flow behavior inside the fabric after compaction and deformation, providing high-precision permeability parameters for composite material molding processes.

[0016] In one embodiment of the present invention, the first flow velocity is calculated using the following formula: , , u This represents the velocity component of the first flow velocity in the warp direction. v This represents the velocity component of the first flow velocity in the weft direction. w This refers to the velocity component of the first flow velocity in the direction perpendicular to the plane containing the warp yarns. ρ The density of the resin, μ Resin viscosity, p 1 represents the pressure in the yarn gap area. f x This represents the component of the resin's unit mass force in the warp direction. f y This represents the component of the resin's unit mass force in the weft direction. f z This is the component of the resin's unit mass force in the direction perpendicular to the plane containing the warp yarn.

[0017] In this embodiment, the resin flow velocity in the yarn gap region is componentized in different directions. Pressure distribution and unit mass force in the yarn gap region are the main factors affecting the flow velocity. Resin viscosity and density are introduced as correction factors. The calculated velocity components of the resin in the warp direction, weft direction, and direction perpendicular to the warp plane are obtained. These velocity components in each direction will be used in the subsequent permeability calculation, providing accurate basic data for the calculation. By fully considering the combined effects of pressure distribution, unit mass force, resin viscosity, and density on the flow velocity, accurate calculation of multi-directional velocity components is achieved, effectively improving the calculation accuracy of resin flow velocity and providing more reliable basic data for subsequent permeability calculation. This also avoids errors caused by simplified calculations, making the simulation results more closely resemble the actual molding process.

[0018] In one embodiment of the present invention, the second flow velocity is calculated using the following formula: For the inner part of the yarn i The resin flow rate at the cross-section of each beam element chain. K t The internal permeability of the yarn. μ Resin viscosity, Pressure difference in the direction of flow l Length in the flow direction.

[0019] In this embodiment, the resin's physical properties (resin viscosity), the pore pressure inside the yarn bundle, and the internal permeability of the yarn are comprehensively considered to calculate the resin's flow velocity inside the yarn bundle. This ensures that the value of the second flow velocity accurately reflects the permeability characteristics of the yarn's internal pores, fully considers the coupling effect of the yarn's internal permeability, pressure difference, and resin viscosity, avoids calculation deviations caused by neglecting the flow inside the yarn bundle, and improves the completeness and accuracy of the overall flow velocity solution.

[0020] In one embodiment of the present invention, the resin permeability in the compacted fabric model is calculated using the following formula: Let be the cross-sectional area of ​​the resin at the outlet of the compacted fabric model. , This represents the z-axis coordinates of the highest node in the compacted fabric model coordinate system. The coordinates of the lowest point node on the z-axis. This represents the y-coordinate of the leftmost node. This represents the y-coordinate of the rightmost node. This is the area between yarns. This refers to the internal region of the yarn bundle. The first flow velocity, , This is the integral unit of the resin at the outlet section. For the second flow velocity, , μ Resin viscosity, The length of the resin flow direction in the compacted fabric model. This represents the pressure difference in the direction of resin flow in the compacted fabric model.

[0021] In this embodiment, based on the first flow velocity of the resin in the yarn gap region and the second flow velocity in the yarn bundle interior region, combined with the boundary contour information of the compacted fabric model, the equivalent permeability of the resin in the compacted yarn fabric is determined by a preset permeability calculation formula. The permeability calculation formula reflects the coupling relationship between the first flow velocity, the second flow velocity, the geometric characteristics of the compacted fabric, and the flow driving force and viscous resistance. Substituting the fluid properties of the resin, the pressure distribution of the flow region, and the boundary geometric parameters, the permeability parameters of the resin in the fabric are calculated using the above formula. Simultaneously considering the flow velocities in both the yarn gap region and the yarn bundle interior region better reflects actual permeation patterns, improving the accuracy and reliability of permeability calculation and providing a basis for flow simulation and process optimization in composite material molding processes.

[0022] The boundary node coordinates of the compacted fabric model are extracted. Based on these coordinates, the complete outline of the outlet section is reconstructed. The cross-sectional area at the outlet is calculated using the coordinates of the highest, lowest, leftmost, and rightmost nodes in the outline. This area represents the actual flow space of the resin at the fabric outlet. Specifically, the coordinate system of the compacted fabric model is established with the lower left corner of the compacted fabric model as the origin. The warp direction is the x-axis (located in the fabric plane and perpendicular to the main flow direction), the weft direction is the y-axis (the main flow direction of the resin in the yarn gap region), and the direction perpendicular to the fabric plane is the z-axis. The path of the beam element chain containing the topmost beam element node in the loose fabric model can be represented by a sine function z = a*sin(bx + c). After compaction, the sine function becomes z = a1*sin(b1x + c1). Based on this sine function, the coordinate value of the highest node on the z-axis is obtained. max1 In the loose fabric model, the path of the beam element chain containing the lowest beam element node can be represented by a sine function z = a² * sin(b²x + c²). After compaction, the sine function becomes z = a³ * sin(b³x + c³). Based on this sine function, the coordinates of the lowest node on the z-axis are obtained as z. min3 Similarly, obtain the y-coordinate of the leftmost node. and the y-coordinate of the rightmost node. .

[0023] In one embodiment of the present invention, the cross-sectional area S1 of the internal region of the yarn bundle is calculated by the following formula: in, n This refers to the total number of yarns contained within the internal region of the yarn bundle. i Indicates the first i Thread, m The number of beam unit chains contained in the cross-section of a single yarn. j Represents the first cross-section of a single yarn j Individual beam element chain, Let be the ordinate of the origin in the projection plane coordinate system. For the first i The first yarn in the root yarn j The coordinates of each beam unit chain in the projection plane coordinate system, the projection plane is a cross section perpendicular to the direction of the yarn, the centroids of each yarn cross section are connected in sequence to form a curve, and the unit tangential vector of the curve at the centroid of each yarn is the direction of the yarn. The cross-sectional area S2 of the yarn gap region is calculated using the following formula: .

[0024] In this embodiment, each yarn is discretized into several beam unit chains along its direction, with each beam unit chain corresponding to a cross-section. The contour nodes of this cross-section, a total of 50 points, are extracted and projected onto a local projection plane perpendicular to the yarn direction. Within this projection plane, the centroid of the cross-section is first calculated, and a local coordinate system is established using the centroid as a reference. The outer contour nodes are extracted to obtain the true boundary of the yarn cross-section. The leftmost and rightmost nodes of the contour are taken, and the line connecting them is used as a horizontal reference axis. All contour nodes are connected sequentially to form a closed polygonal line. The area enclosed by this closed polygonal line is divided horizontally into several vertical segments, each approximately a trapezoid. The area of ​​this closed region is calculated using the trapezoidal summation formula, which is the single-strand cross-sectional area of ​​the yarn at that beam unit chain. Repeating the above calculation for all beam unit chains yields the change in cross-sectional area at various positions along the entire yarn direction. The total cross-sectional area of ​​the solid region inside the yarn bundle is obtained by summing the values ​​for all yarns. The area of ​​the gap region between yarns is obtained by subtracting the total area of ​​the yarn solid from the total area of ​​the unit cell exit cross-section. Specifically, the projected plane coordinate system is established with the centroid of the yarn cross-section as the origin, the line connecting the leftmost and rightmost nodes of the cross-section as the x-axis, and the direction perpendicular to this line as the y-axis.

[0025] In one embodiment of the present invention, based on the geometric information of the yarn fabric, each yarn in the yarn fabric is discretized into multiple beam unit chains to obtain a loose fabric model, including: Based on the warp and weft density, layering structure, and warp yarn direction of the yarn fabric, the TexGen geometry engine is called to set the initial spline curve; Each bundle of yarn in the initial spline curve is discretized into multiple beam unit chains to obtain a loose fabric model.

[0026] In this embodiment, the TexGen geometry engine is invoked via a Python script for initial modeling. Based on the warp and weft density, layering structure, and warp yarn direction of the 2.5D fabric, initial spline curves are generated according to the fabric spline path. Each yarn bundle is discretized into multiple independent unit chains composed of first-order beam elements. An ideal ellipse is used as the initial cross-section, and the transverse shear stiffness is specified by custom beam element properties to correct the defect of excessive bending stiffness, giving the yarn the physical properties of being easy to bend and difficult to stretch, resulting in... Figure 4 The loose fabric model shown.

[0027] In one embodiment of the present invention, a loose fabric model is compacted to obtain a compacted fabric model, including: Vacuum pressure load is applied to a loose fabric model to simulate the compression, flattening and spatial twisting deformation of yarn during the compaction process, and the deformed fabric model is obtained. Based on the spatial coordinates of all beam element nodes in the deformed fabric model, the deformed contours of all yarn cross sections in the deformed fabric model are obtained. All deformed contours are spliced ​​and reconstructed along preset yarn paths to obtain a compacted fabric model.

[0028] In this embodiment, the compacted fabric model is as follows: Figure 5 As shown, the loose model was imported into finite element software for compaction simulation. Rigid shell planes were placed above and below the fabric to simulate the mold, and pressure loads were applied to recreate the vacuum bag compression process. Nonlinear geometry options were enabled, and an explicit dynamic solver was used for calculation. Universal contact was set between yarns until the model reached energy balance, outputting the three-dimensional coordinates of all element nodes after compaction. Geometric features were reconstructed based on the node coordinates, and the AlphaShapes algorithm was used to perform boundary identification on the nodes of each cross-section, obtaining the envelope that reflects the warp distortion and yarn nesting morphology. For example... Figure 6As shown, the reconstructed cross-section is scanned and stitched along the yarn path to generate a high-fidelity three-dimensional unit cell compacted fabric model. The reconstructed model can capture the thickness inhomogeneity and path buckling features found in CT scans, solving the problem that ideal models cannot simulate yarn interference. By restoring the deformed cross-section and irregular microscopic flow channels after yarn compression, the fluid resistance caused by localized narrowing and bending structures is realistically reproduced. Experimental verification shows that the in-plane permeability prediction accuracy is significantly improved from 56.26% of the traditional ideal model to 91.28%.

[0029] In one specific embodiment, the internal permeability tensor of the yarn is first calculated. Several 5cm × 5cm samples of 2.5D carbon fiber fabric are cut; the measurement is performed in two steps using Archimedes' buoyancy method. These samples are divided into a "standard group" and an "oil-immersion group." In the standard group, all samples are dried in a 105°C constant temperature drying oven for 2 hours to eliminate the interference of ambient humidity on the mass measurement. In the oil-immersion group, the samples are immersed in low-viscosity vegetable oil, utilizing capillary action to allow it to fully penetrate into the micropores inside the fiber bundle. After removal, a surface "draining-heat treatment-degreasing" cycle is performed to ensure that the pores inside the fiber bundle are completely physically sealed by the oil, and that there is no excess oil film on the fiber surface. The volume under the oil-immersion sealed state is measured again using the buoyancy method. The fiber volume fraction inside the 2.5D fabric is calculated. A rectangular representative cell is created in the modeling software. Based on the fiber volume fraction inside the fiber bundle, 100 carbon fiber monofilaments are placed within the cell using a random algorithm, ensuring that the monofilaments do not overlap. Apply periodic boundary conditions to ensure that the monofilaments automatically shear at the boundary and replenish from the opposite side, eliminating boundary effects. Set the fluid to an isothermal incompressible Newtonian fluid, set the dynamic viscosity, density and viscosity of the simulated fluid (such as resin or simulated oil), set non-slip boundary conditions on the monofilament surface, and calculate the yarn's internal permeability.

[0030] Refined Modeling of 2.5D Fabric Yarn Based on Virtual Fibers: Based on the warp and weft density, layering structure, and warp yarn direction of the 2.5D fabric, the TexGen geometry engine was invoked, and initial spline curves were set according to the spline paths of the 2.5D fabric. Each yarn bundle was discretized into 50 independent digital unit chains (composed of first-order beam elements), with the initial cross-section set as an ideal ellipse. To correct the problem of excessive bending stiffness of the beam elements, the transverse shear stiffness was specified by customizing the beam element properties, allowing the yarn to exhibit realistic physical characteristics of being easily bent and difficult to stretch in the simulation. In the finite element software, two rigid planes were set as molds. Two rigid shell planes were set above and below the fabric to simulate the vacuum bag compression process, applying pressure loads. Nonlinear geometry options were enabled in the simulation, the model was imported into the explicit dynamics solver, the contact between yarns was set as universal contact, and the calculation was performed until energy balance was reached, outputting the three-dimensional spatial coordinates of all digital unit nodes after compaction. The coordinates of all digital unit nodes after compaction equilibrium were extracted. The AlphaShapes algorithm is applied to identify the boundaries of nodes in each cross-section. The generated envelope accurately reflects the distortion caused by compression of the warp yarns and the nesting morphology between the yarns. The reconstructed cross-sections are then scanned and stitched along the yarn paths to generate a high-fidelity 3D unit cell model.

[0031] 2.5D Fabric Microscopic Dual-Scale Flow Coupled Permeability Calculation: The reconstructed unit cell was imported into CFD software. Fluid meshes were created for the wide pore regions between yarns and the interior of the yarns (porous media region), with consistent meshes for both regions. The fluid flow velocities in both regions were solved separately. The level set tracking function was enabled to track the evolution of the resin flow front in the complex extrusion channel in real time. The pressure difference between the inlet and outlet of the unit cell was set. The distribution of velocity and pressure in the flow field was solved. By analyzing the outlet flow rate and the location of the resin flow front, the stable mass flow rate at the outlet was derived after the flow field converged. The equivalent permeability prediction value of the reconstructed unit cell was calculated.

[0032] Finally, the permeability prediction results were experimentally verified using a vacuum-assisted resin infusion process platform. The bottom layer was a flat aluminum mold, covered with a 2.5D fabric sample. The top layer was covered with a highly transparent vacuum bag, and airtightness was ensured using sealing strips. The vacuum pump was activated to reduce the system pressure. The oil inlet valve was opened, and a camera recorded the entire propagation time of soybean oil on the leading edge grid. Figure 7 As shown, the square of the flow distance x 2 The vertical axis is plotted as y, and time t is plotted as x. The slope is obtained through linear regression analysis. The experimentally measured permeability is calculated and compared with the simulation results above.

[0033] like Figure 2 , Figure 3As shown, this invention provides a device for determining permeability based on refined yarn modeling. The device can be implemented via software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for determining permeability based on yarn fine modeling, provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0034] like Figure 3 As shown in the figure, this embodiment provides a permeability determination device based on yarn fine modeling. The device includes: The loose model construction module 300 discretizes each yarn in the yarn fabric into multiple beam element chains based on the geometric information of the yarn fabric, thus obtaining a loose fabric model. Each beam element chain includes multiple beam element nodes. The compaction model construction module 302 is connected to the loose model construction module to compact the loose fabric model and obtain a compacted fabric model. The compacted fabric model includes a yarn gap area and a yarn bundle internal area. The resin flow rate calculation module 304 is connected to the compaction model construction module to simulate the process of resin flowing in the compaction fabric model. Based on the resin density, resin viscosity and pressure distribution inside the compaction fabric model, it obtains the first flow rate of resin flowing in the yarn gap region and the second flow rate of resin flowing in the region inside the yarn bundle. The contour information extraction module 306 is connected to the resin flow rate calculation module. Based on the compacted fabric model, it obtains the highest point node information of the top beam unit chain, the lowest point node information of the bottom beam unit chain, the leftmost end node information of the leftmost beam unit chain, and the rightmost end node information of the rightmost beam unit chain. The permeability calculation module 308 is connected to the contour information extraction module. Based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information, it obtains the resin permeability in the compacted fabric model.

[0035] In this embodiment of the invention, the loose model construction module 300 can be used to execute step 100 in the above method embodiment, the compaction model construction module 302 can be used to execute step 102 in the above method embodiment, the resin flow rate calculation module 304 can be used to execute step 104 in the above method embodiment, the contour information extraction module 306 can be used to execute step 106 in the above method embodiment, and the permeability calculation module 308 can be used to execute step 108 in the above method embodiment.

[0036] In one embodiment of the present invention, the first flow velocity is calculated using the following formula: , , u This refers to the velocity component of the first flow velocity in the warp direction. v This refers to the velocity component of the first flow velocity in the weft direction. w This refers to the velocity component of the first flow velocity in the direction perpendicular to the plane containing the warp yarns. ρ The density of the resin, μ Resin viscosity, p 1 represents the pressure in the yarn gap area. f x This represents the component of the resin's unit mass force in the warp direction. f y This represents the component of the resin's unit mass force in the weft direction. f z This is the component of the resin's unit mass force in the direction perpendicular to the plane containing the warp yarn.

[0037] In one embodiment of the present invention, the second flow velocity is calculated using the following formula: For the inner part of the yarn i The resin flow rate at the cross-section of each beam element chain. K t The internal permeability of the yarn. μ Resin viscosity, Pressure difference in the direction of flow l Length in the flow direction.

[0038] In one embodiment of the present invention, the permeability of the resin in the compacted fabric model is calculated by the following formula: The cross-sectional area of ​​the resin at the outlet of the compacted fabric model is given. , This represents the z-axis coordinates of the highest node in the compacted fabric model coordinate system. The coordinates of the lowest point node on the z-axis. This represents the y-coordinate of the leftmost node. This represents the y-coordinate of the rightmost node. This is the area between yarns. This refers to the internal region of the yarn bundle. The first flow velocity, , This is the integral unit of the resin at the outlet section. For the second flow velocity, , μ Resin viscosity, The length of the resin flow direction in the compacted fabric model. This represents the pressure difference in the direction of resin flow in the compacted fabric model.

[0039] In one embodiment of the present invention, the cross-sectional area S1 of the internal region of the yarn bundle is calculated by the following formula: in, n This refers to the total number of yarns contained within the internal region of the yarn bundle. i Indicates the first i Thread, m The number of beam unit chains contained in the cross-section of a single yarn. j Represents the first cross-section of a single yarn j Individual beam element chain, Let be the ordinate of the origin in the projection plane coordinate system. For the first i The first yarn in the root yarn j The coordinates of each beam unit chain in the projection plane coordinate system, wherein the projection plane is a cross section perpendicular to the direction of the yarn, and the centroids of each yarn cross section are connected in sequence to form a curve, wherein the unit tangential vector of the curve at the centroid of each yarn is the direction of the yarn. The cross-sectional area S2 of the yarn gap region is calculated using the following formula: .

[0040] In one embodiment of the present invention, the loose model building module is configured to perform the following operations: Based on the warp and weft density, layering structure, and warp yarn direction of the yarn fabric, the TexGen geometry engine is called to set the initial spline curve; Each bundle of yarn in the initial spline curve is discretized into multiple beam unit chains to obtain a loose fabric model.

[0041] In one embodiment of the present invention, the compaction model construction module is configured to perform the following operations: A vacuum pressure load is applied to the loose fabric model to simulate the compression, flattening and spatial twisting deformation of the yarn during the compaction process, resulting in a deformed fabric model. Based on the spatial coordinates of all beam element nodes in the deformed fabric model, the deformed contours of all yarn cross sections in the deformed fabric model are obtained. All deformed contours are spliced ​​and reconstructed along a preset yarn path to obtain the compacted fabric model.

[0042] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a permeability determination device based on fine yarn modeling. In other embodiments of the present invention, a permeability determination device based on fine yarn modeling may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0043] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0044] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for determining permeability based on yarn fine modeling in any embodiment of this invention.

[0045] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a permeability determination method based on yarn fine modeling according to any embodiment of this invention.

[0046] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0047] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0048] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0049] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0050] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0052] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining permeability based on refined yarn modeling, characterized in that, include: Based on the geometric information of the yarn fabric, each yarn in the yarn fabric is discretized into multiple beam unit chains to obtain a loose fabric model. Each beam unit chain includes multiple beam unit nodes. The loose fabric model is compacted to obtain a compacted fabric model, which includes a yarn gap region and a yarn bundle interior region. The process of resin flowing in the compacted fabric model is simulated. Based on the resin density, resin viscosity and pressure distribution inside the compacted fabric model, the first flow velocity of the resin flowing in the yarn gap region and the second flow velocity of the resin flowing in the region inside the yarn bundle are obtained. Based on the compacted fabric model, the highest point node information of the topmost beam unit chain, the lowest point node information of the bottommost beam unit chain, the leftmost end node information of the leftmost beam unit chain, and the rightmost end node information of the rightmost beam unit chain are obtained. Based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information, the resin permeability in the compacted fabric model is obtained.

2. The method according to claim 1, characterized in that, The first flow velocity is calculated using the following formula: ， ， u This refers to the velocity component of the first flow velocity in the warp direction. v This refers to the velocity component of the first flow velocity in the weft direction. w This refers to the velocity component of the first flow velocity in the direction perpendicular to the plane containing the warp yarns. ρ The density of the resin, μ Resin viscosity, p 1 represents the pressure in the yarn gap area. f x This represents the component of the resin's unit mass force in the warp direction. f y This represents the component of the resin's unit mass force in the weft direction. f z This is the component of the resin's unit mass force in the direction perpendicular to the plane containing the warp yarn.

3. The method according to claim 2, characterized in that, The second flow velocity is calculated using the following formula: For the inner part of the yarn i The resin flow rate at the cross-section of each beam element chain. K t The internal permeability of the yarn. μ Resin viscosity, Pressure difference in the direction of flow l Length in the flow direction.

4. The method according to claim 3, characterized in that, The resin permeability in the compacted fabric model is calculated using the following formula: The cross-sectional area of ​​the resin at the outlet of the compacted fabric model is given. , This represents the coordinates of the highest node on the z-axis of the compacted fabric model coordinate system. The coordinates of the lowest point node on the z-axis. This represents the y-coordinate of the leftmost node. This represents the y-coordinate of the rightmost node. This is the area between yarns. This refers to the internal region of the yarn bundle. The first flow velocity, , This is the integral unit of the resin at the outlet section. For the second flow velocity, , μ Resin viscosity, The length of the resin flow direction in the compacted fabric model. This represents the pressure difference in the direction of resin flow in the compacted fabric model.

5. The method according to claim 4, characterized in that, The cross-sectional area S1 of the internal region of the yarn bundle is calculated using the following formula: in, n This refers to the total number of yarns contained within the internal region of the yarn bundle. i Indicates the first i Thread, m The number of beam unit chains contained in the cross-section of a single yarn. j Indicates the first cross-section of a single yarn j Individual beam element chain, Let be the ordinate of the origin in the projection plane coordinate system. For the first i The first yarn in the root yarn j The coordinates of each beam unit chain in the projection plane coordinate system, wherein the projection plane is a cross section perpendicular to the direction of the yarn, and the centroids of each yarn cross section are connected in sequence to form a curve, wherein the unit tangential vector of the curve at the centroid of each yarn is the direction of the yarn. The cross-sectional area S2 of the yarn gap region is calculated using the following formula: 。 6. The method according to claim 1, characterized in that, The geometric information of the yarn fabric is used to discretize each yarn in the fabric into multiple beam unit chains to obtain a loose fabric model, including: Based on the warp and weft density, layering structure, and warp yarn direction of the yarn fabric, the TexGen geometry engine is called to set the initial spline curve; Each bundle of yarn in the initial spline curve is discretized into multiple beam unit chains to obtain a loose fabric model.

7. The method according to claim 1, characterized in that, The process of compacting the loose fabric model to obtain a compacted fabric model includes: A vacuum pressure load is applied to the loose fabric model to simulate the compression, flattening and spatial twisting deformation of the yarn during the compaction process, resulting in a deformed fabric model. Based on the spatial coordinates of all beam element nodes in the deformed fabric model, the deformed contours of all yarn cross sections in the deformed fabric model are obtained. All deformed contours are spliced ​​and reconstructed along a preset yarn path to obtain the compacted fabric model.

8. A device for determining permeability based on refined yarn modeling, characterized in that, include: The loose model construction module, based on the geometric information of the yarn fabric, discretizes each yarn in the yarn fabric into multiple beam element chains to obtain a loose fabric model. Each beam element chain includes multiple beam element nodes. A compaction model construction module is connected to the loose model construction module to compact the loose fabric model and obtain a compacted fabric model. The compacted fabric model includes a yarn gap region and a yarn bundle internal region. The resin flow rate calculation module is connected to the compaction model construction module. It simulates the process of resin flowing in the compacted fabric model. Based on the resin density, resin viscosity and pressure distribution inside the compacted fabric model, it obtains the first flow rate of resin flowing in the yarn gap region and the second flow rate of resin flowing in the region inside the yarn bundle. The contour information extraction module is connected to the resin flow rate calculation module. Based on the compacted fabric model, it obtains the highest point node information of the top beam unit chain, the lowest point node information of the bottom beam unit chain, the leftmost end node information of the leftmost beam unit chain, and the rightmost end node information of the rightmost beam unit chain. The permeability calculation module, connected to the contour information extraction module, obtains the resin permeability in the compacted fabric model based on the first flow velocity, the second flow velocity, the highest point node information, the lowest point node information, the leftmost node information, and the rightmost node information.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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