Method, system and storage medium for generating a microscopically representative volume element of a composite material

Abstract

This invention belongs to the field of composite material microstructure modeling technology, and provides a method, system, and storage medium for generating representative microstructure volume elements of composite materials. The method first defines key parameters such as the target fiber volume fraction, target pore volume fraction, and fiber radius. Second, an iterative random move algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints. Next, based on this set and according to a given distance threshold, closed polygonal regions composed of at least three fiber center points are identified as potential pores, forming a set of pore regions. Finally, using the target pore volume fraction as a constraint, geometric modeling is used to generate a representative microstructure volume element model of the composite material with controllable pore volume fraction, random pore shape and location, and containing the three phases of fibers, matrix, and pores. The method of this invention effectively balances the realism of pore distribution with the precise controllability of pore content, improving the fidelity and engineering applicability of microstructure modeling.

Description

Technical Field

[0001] This invention belongs to the field of composite material microstructure modeling technology, and relates to a method, system and storage medium for generating representative volume elements (RVEs) of composite materials. Background Technology

[0002] In the study of the mechanical properties of composite materials, the Relative Velocity Spectrometry (RVE) model is the foundation for finite element analysis. Traditional RVE modeling typically employs assumptions of regularly arranged fibers and idealized pores, which makes it difficult to accurately reflect the random distribution characteristics of pores in actual materials.

[0003] Existing pore modeling methods have the following limitations:

[0004] Random placement method: By repeatedly trying to place pores in the matrix and detecting interference to avoid overlap, this method can obtain a relatively ideal pore distribution, but it relies on a lot of trial and error, resulting in low efficiency in porosity control and making it difficult to meet the needs of high precision or large-scale modeling.

[0005] Element selection method: First, the matrix is ​​meshed, and then some elements are randomly selected as pores. This method is simple to implement, but requires additional mesh computing resources and is limited by element size. It is only suitable for generating small, diffuse pores and cannot effectively simulate large-scale or non-uniform pore structures.

[0006] Polygonal Boolean operation method: This method generates pores by performing Boolean operations on predefined geometric shapes (such as triangles, quadrilaterals, etc.) with fibers. However, the pore morphology generated by this method is too regular and lacks the geometric randomness and topological complexity of pores in real materials, resulting in a significant difference from the actual pore distribution.

[0007] Micro-CT reconstruction method: Although this method can restore the three-dimensional structure of pores in real composite materials with high fidelity, the resulting model is in a non-parametric form, which is difficult to support systematic parameter research; at the same time, the current construction of RVE from CT images still relies on a lot of computing resources and manual intervention, and has poor engineering scalability.

[0008] In summary, achieving precise control of pore volume fraction while ensuring complete randomness in pore shape and location remains a critical technological bottleneck that urgently needs to be overcome. Therefore, a new method is urgently needed to automatically generate microscopic RVE models of composite materials with controllable pores and truly random morphology and distribution. Summary of the Invention

[0009] To address the technical problems in existing technologies, such as idealized pore shape, unrealistic pore location distribution, and difficulty in effectively controlling pore volume fraction, this invention discloses a method and system for generating representative microscopic volumetric elements of composite materials with controllable pore volume fraction and randomized pore shape and location. This invention achieves high-fidelity, customizable automatic generation of RVE models through parameterized input, iterative random algorithms, and closed-loop feedback control. The generated models can be directly applied to mainstream modeling software and finite element analysis software.

[0010] Specifically, the technical solution for implementing the present invention is as follows: In a first aspect, the present invention provides a method for generating representative microscopic volumetric units of composite materials, the method comprising the following steps: S1. Define the key parameters required to establish a representative volume unit, wherein the key parameters include at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius; S2. Based on the target fiber volume fraction and the fiber radius, an iterative random movement algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints; S3. Based on the set of fiber center points, identify all closed polygonal regions consisting of at least three fiber center points and where the distance between any two points is less than the given distance threshold, and take each of the closed polygonal regions as a potential pore region to form a set of potential pore regions. S4. Using the target pore volume fraction as a constraint, based on the set of fiber center points and the set of potential pore regions, generate a representative volume element model of the composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores through geometric modeling methods.

[0011] In one embodiment of step S2 above, based on the target fiber volume fraction and the fiber radius, an iterative random movement algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints, including: S21. Calculate the total number of fibers required based on the target fiber volume fraction and the preset representative volume unit size; S22. Arrange all the calculated fibers closely within the initial rectangular area according to the hexagonal close packing method to obtain the initial set of fiber center points; S23. Generate a random moving direction and moving distance for each fiber independently, and calculate its candidate new position coordinates. Check whether the candidate new position meets the following two conditions: (1) the fiber does not exceed the boundary of the representative volume unit; (2) the center distance between the fiber and any other fiber is not less than twice the fiber radius. S24. If the above two conditions are met, the candidate new position coordinates are taken as the new position of the fiber; if not, its original position is kept unchanged or a new direction and distance of movement are generated for correction until a candidate new position coordinate that meets the conditions is obtained. S25. Repeat the process of generating random movement directions and distances, calculating candidate new position coordinates, condition judgment and position update for all fibers multiple times to obtain a set of fiber center point coordinates that are non-overlapping and satisfy boundary constraints.

[0012] In one embodiment of step S3 above, the distance threshold is an adjustable geometric parameter whose value is greater than the diameter of the fiber.

[0013] In one embodiment of step S3 above, forming a set of potential porosity regions includes: S31. Taking each fiber center point in the set of fiber center points as a node, if the Euclidean distance between any two nodes is less than a given distance threshold, then establish a connecting edge between them. S32. Starting from any connecting edge, traverse and search for all the smallest closed paths formed by connecting edges, treating each closed path as a closed polygon region; S33. Eliminate the closed polygonal region formed by the center points of completely repeating fibers to form a set of potential pore regions.

[0014] In one embodiment of step S4 above, a representative volumetric element model of the composite material with controllable pore volume fraction, random pore shape and location, and containing three phases of fibers, matrix, and pores is generated using a geometric modeling method, including: S41. Each potential pore region in the set of potential pore regions is stretched into a three-dimensional cylinder along the thickness direction to form an initial set of pore geometries; S42. Model each fiber in the set of fiber center points as a cylinder to form a set of fiber geometries; S43. Using a cuboid with the same size as the representative volume unit as the initial matrix entity, perform a Boolean subtraction operation on the initial matrix entity, first subtracting all entities in the fiber geometry set, and then subtracting all entities in the initial pore geometry set, to obtain the final matrix region; S44. Perform a Boolean subtraction operation on the initial pore geometry set by subtracting all entities in the fiber geometry set to obtain the final pore region; S45. Merge the fiber geometry set, the final pore region and the final matrix region to form a complete representative volume element model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pore.

[0015] In one embodiment, the generated representative volumetric element model of the composite material is used for predicting the equivalent elastic modulus or simulating the damage evolution of the composite material under multiaxial stress.

[0016] In one embodiment of step S4 above, using the target pore volume fraction as a constraint specifically includes: Calculate the actual volume fraction of the final porous region in the currently generated representative volume element model. ,like ,in The target pore volume fraction, To preset an error threshold, the distance threshold is adjusted according to the deviation direction, and the final set of pore regions and a representative volumetric element model of the composite material are re-formed based on the adjusted distance threshold until the desired error is met. .

[0017] Furthermore, if the actual volume fraction is greater than the target pore volume fraction, the distance threshold is decreased; if the actual volume fraction is less than the target pore volume fraction, the distance threshold is increased.

[0018] Secondly, the present invention provides a system for generating representative microscopic volumetric units of composite materials, including a parameter configuration module, a fiber layout generation module, a pore region identification module, and a geometric model construction module.

[0019] The parameter configuration module is used to define the key parameters required to establish a representative volume unit. The key parameters include at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius. The fiber layout generation module is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints based on the target fiber volume fraction and the fiber radius using an iterative random movement algorithm. The pore region identification module is used to identify, based on the set of fiber center points, all closed polygonal regions consisting of at least three fiber center points, and where the distance between any two points is less than the distance threshold, and to take each of the closed polygonal regions as potential pore regions to form a set of potential pore regions. The geometric model building module is used to generate a representative volume element model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores, based on the fiber center point set and the potential pore region set, using the target pore volume fraction as a constraint.

[0020] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the above-mentioned method for generating microscopic representative volume units of composite materials, thereby solving the technical problems in the prior art such as idealized pore shape, unrealistic positional distribution, and difficulty in effectively controlling pore volume fraction.

[0021] Compared with the prior art, the present invention has the following outstanding advantages and positive effects: 1. The pore distribution is highly realistic with high geometric fidelity. This invention cleverly resolves the contradiction between the realism and controllability of pores through a physically driven algorithm. Specifically, this invention abandons idealized or completely random pore generation methods, instead simulating the actual formation mechanism of pores in composite materials—that is, the gaps formed by the mutual compression of randomly arranged fibers. By defining pores as closed polygonal regions enclosed by three or more fiber center points, their position, shape, and size are naturally "induced" by the actual random arrangement of fibers. The pore network generated by this method has an irregular morphology and non-uniform spatial distribution, and can spontaneously form a clustering effect that conforms to physical laws, thereby greatly improving the geometric fidelity of the microscopic RVE model, enabling it to more realistically reflect the complex characteristics of defects in actual composite materials.

[0022] 2. Pore content is precisely controllable, with significant engineering application value. While ensuring the accuracy of pore distribution, this invention establishes a direct, predictable, and monotonic quantitative relationship between pore volume fraction and model input by introducing a key adjustable parameter: the "fiber center point distance threshold L." The physical meaning of this threshold L is clear: increasing the value of L connects more fibers, forming larger and more potential pore regions, thus increasing the total pore volume; conversely, decreasing it reduces it. Based on this clear control mechanism, this invention, through an efficient closed-loop feedback control process (i.e., calculation-comparison-adjustment), can quickly and accurately converge the pore volume fraction to a preset target value. This characteristic completely overcomes the inherent defects of traditional random placement methods that rely on inefficient "trial and error." The dual capability of "random shape, controllable quantity" achieved by this invention provides a reliable tool for systematically studying the influence of porosity on the mechanical properties of composite materials (such as equivalent elastic modulus, damage evolution, etc.), and lays a solid foundation for precise material design for specific performance requirements, demonstrating excellent engineering application prospects. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method for generating representative microscopic volumetric units of composite materials according to the present invention; Figure 2 This is a schematic diagram of a representative volume element geometric model provided in the embodiment; Figure 3 The examples provide RVE models with the same random fiber distribution but different distance thresholds. Figure 4 The examples provide RVE models with different random fiber distributions and the same distance threshold. Figure 5 This is an architectural diagram of the composite material micro-representative volumetric unit generation system of the present invention; Among them, 1. Fiber region; 2. Matrix region; 3. Porous region; 4. Final RVE model; 5. First RVE model; 6. Second RVE model; 7. Third RVE model; 8. Fourth RVE model; 9. Fifth RVE model; 10. Sixth RVE model; 11. Seventh RVE model; 12. Eighth RVE model. Detailed Implementation

[0025] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0026] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] like Figure 1 As shown, this embodiment of the invention provides a method for generating representative microscopic volumetric units of composite materials, the method comprising the following steps: S1. Define the key parameters required to establish a representative volumetric unit, including at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius.

[0028] This invention can be implemented through secondary development using script files in Abaqus software. These script files are written in Python, and the Python scripts implement parameter input via a graphical user interface, allowing the acquisition of key parameters required for RVE modeling. These parameters include fiber radius. and its fluctuation range ( , R min and R max These are the minimum and maximum fiber radii, respectively, and the target fiber volume fraction. (Typically 40%-60%) and target pore volume fraction (Usually 10%~15%) etc.

[0029] By inputting the fiber radius and target fiber volume fraction It can calculate the basic dimensions of the RVE model, including the RVE model itself. Length of direction ,Model Length of direction Dimensions in the thickness direction of the model It is necessary to ensure that the model contains a sufficient number of fibers, and that the fiber volume fraction matches the target volume fraction.

[0030] S2. Based on the target fiber volume fraction and the fiber radius, an iterative random movement algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints.

[0031] In one embodiment, the process of generating the fiber center point set specifically includes the following steps: S21. Calculate the total number of fibers required based on the target fiber volume fraction and the preset representative volume unit size.

[0032] S22. Arrange all the calculated fibers closely within the initial rectangular area according to the hexagonal close packing method to obtain the initial set of fiber center points.

[0033] S23. Generate a random direction and distance of movement for each fiber independently, and calculate the coordinates of its candidate new position. Check whether the candidate new position meets the following two conditions: (1) the fiber does not exceed the boundary of the representative volume unit; (2) the center distance between the fiber and any other fiber is not less than twice the fiber radius.

[0034] S24. If the above two conditions are met, the candidate new position coordinates are taken as the new position of the fiber; if not, its original position is kept unchanged or a new direction and distance of movement are generated for correction until a candidate new position coordinate that meets the conditions is obtained.

[0035] S25. Repeat the process of generating random movement directions and distances, calculating candidate new position coordinates, condition judgment and position update for all fibers multiple times to obtain a set of fiber center point coordinates that are non-overlapping and satisfy boundary constraints.

[0036] In practice, the iterative random shift algorithm is used to generate a random distribution of fibers, and the process of obtaining the set of fiber center points is as follows: First, initially, all fibers are arranged in a hexagonal close-packed arrangement. Within the rectangular region, a random fiber radius is generated for each fiber. In this example, randomness is achieved by importing the Python random function Random.

[0037] Secondly, for the center point of the i-th fiber Generate random movement direction and distance traveled At the same time, it is necessary to avoid fiber boundary interference and update the center point of the i-th fiber. The coordinates of the new position after the move.

[0038] Finally, the iterative process continues, typically requiring 10 to 1000 iterations, until the set of fiber center points with fiber center coordinates and actual radius is obtained. .

[0039] S3. Based on the set of fiber center points, identify all closed polygonal regions consisting of at least three fiber center points, where the distance between any two points is less than the given distance threshold, and use each of the closed polygonal regions as a potential pore region to form a set of potential pore regions.

[0040] In one embodiment, forming a set of potential porous regions specifically includes the following steps: S31. Taking each fiber center point in the set of fiber center points as a node, if the Euclidean distance between any two nodes is less than a given distance threshold, then establish a connecting edge between them. S32. Starting from any connecting edge, traverse and search for all the smallest closed paths formed by connecting edges, treating each closed path as a closed polygon region; S33. Eliminate the closed polygonal region formed by the center points of completely repeating fibers to form a set of potential pore regions.

[0041] In practical implementation, the following algorithm can be used to identify the pore regions among randomly distributed fibers and obtain the set of fiber center point coordinates, specifically including: First, set a distance threshold. L is an adjustable geometric parameter whose value is greater than the fiber diameter. In this embodiment, the initial value of L can be set to 2.4 times the average fiber radius. (Subsequently adjusted automatically through closed-loop feedback) when the distance between the center points of the two fibers... If so, a virtual connection edge is established between them, and all connection edges are found and defined as a set. .

[0042] Subsequently, a surface search algorithm was used to identify closed pore regions from the set. Any connecting edge (The connecting edge formed by connecting the i-th node and the j-th node) starts the traversal, always selecting the first edge in the clockwise / counterclockwise direction that is connected to the current connecting edge, until the minimum closed path is formed, and each closed path is treated as a closed polygon region.

[0043] Finally, all identified closed loops are verified, and invalid closed loops consisting of completely repetitive fiber center points are removed to ensure the uniqueness of each pore region. By traversing the loops, a set of potential pore regions for all closed loops is established. Each closed loop is a closed polygonal region, corresponding to a potential pore region surrounded by three or more fibers.

[0044] S4. Using the target pore volume fraction as a constraint, based on the set of fiber center points and the set of potential pore regions, generate a representative volume element model of the composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores through geometric modeling methods.

[0045] In one embodiment, a representative volumetric element model of a composite material with controllable pore volume fraction, random pore shape and location, and comprising three phases—fiber, matrix, and pores—is generated using a geometric modeling method, including: S41. Each potential pore region in the set of potential pore regions is stretched into a three-dimensional cylinder along the thickness direction to form an initial set of pore geometries; S42. Model each fiber in the set of fiber center points as a cylinder to form a set of fiber geometries; S43. Using a cuboid with the same size as the representative volume unit as the initial matrix entity, perform a Boolean subtraction operation on the initial matrix entity, first subtracting all entities in the fiber geometry set, and then subtracting all entities in the initial pore geometry set, to obtain the final matrix region; S44. Perform a Boolean subtraction operation on the initial pore geometry set by subtracting all entities in the fiber geometry set to obtain the final pore region; S45. Merge the fiber geometry set, the final pore region and the final matrix region to form a complete representative volume element model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pore.

[0046] In practical implementation, the generation of representative volume element models of composite materials is based on sets. ,gather With the relevant set parameters, construct a complete RVE geometric model in Abaqus, specifically including the following steps: Step 1: Create an initial 3D solid representing the complete base, with dimensions of [size missing]. A rectangular prism basic framework; Step 2, based on the set Create a 3D cylinder representing the fiber; fiber region 1 is now modeled. To facilitate subsequent operations, create a collection containing all fiber regions. Fiber region 1 is shown in Figure 2; Step 3: Set All closed polygonal regions are stretched into three-dimensional cylinders; Step 4: Perform a Boolean subtraction operation between the initial 3D solid and the 3D cylinder and 3D column, thereby forming a cavity region in the complete matrix corresponding to the closed polygon region and fiber region. The modeling of matrix region 2 is completed, and the set of matrix regions is established. The matrix region 2 is shown in Figure 2; Step 5: Perform a Boolean subtraction operation between the three-dimensional cylinder and the three-dimensional cylindrical shape to remove the fiber-corresponding region from the initial porous region. The modeling of porous region 3 is now complete, and a set of porous regions is established. Porous region 3 is shown in Figure 2; Step 6: Merge the processed fiber region, matrix region and pore region through Boolean addition operation to form the final RVE model 4, which includes the fiber, matrix and pores. The final RVE model 4 is shown in Figure 2.

[0047] Furthermore, when generating a representative volume element model of the composite material, the target pore volume fraction can be used as a constraint to modify the generated model, thereby obtaining a model with controllable pore volume fraction and random pore shape and location. Specifically, using the target pore volume fraction as a constraint includes: calculating the actual volume fraction of the final pore region in the currently generated representative volume element model. ,like ,in The target pore volume fraction, To preset an error threshold, the distance threshold is adjusted according to the deviation direction, and the final set of pore regions and a representative volumetric element model of the composite material are re-formed based on the adjusted distance threshold until the desired error is met. Furthermore, if the actual volume fraction is greater than the target pore volume fraction, the distance threshold is decreased; if the actual volume fraction is less than the target pore volume fraction, the distance threshold is increased.

[0048] More specifically, the controllability of pore volume fraction is achieved by adjusting the distance threshold. To achieve the target pore volume fraction With distance threshold There exists a strict monotonic relationship between them, and their values ​​change with... It increases with size. The specific control method is as follows: First, set the allowed preset error threshold. ; Secondly, read and calculate the current actual volume fraction. ; Next, make a judgment if... Then reduce the distance threshold. ,like Then increase ; Finally, repeat the process, iterating through loops to achieve the desired result. .

[0049] After the above operations, a representative microscopic volumetric unit of the composite material with controllable pore content and random shape and position can be established. For example, Figure 3 As shown, the first RVE model 5, the second RVE model 6, the third RVE model 7, and the fourth RVE model 8 respectively illustrate four models with the same random fiber distribution but different distance thresholds. Different RVE models were used, and the four RVE models had different porosity. Figure 4 shows four RVE models (model 9, model 10, model 11, and model 12) with the same distance threshold. However, the four RVE models with different random fiber distributions also have different porosity contents. This is mainly because the porosity content is determined by the fiber distribution and the distance threshold. This is due to the combined effect.

[0050] In one embodiment, the generated representative volumetric element model of the composite material is used for predicting the equivalent elastic modulus or simulating the damage evolution of the composite material under multiaxial stress.

[0051] Based on the same inventive concept, this invention also provides a composite material micro-representative volumetric unit generation system, as described in the following embodiments. Since the principle underlying the problem-solving of the composite material micro-representative volumetric unit generation system is similar to the composite material micro-representative volumetric unit generation method disclosed in the above embodiments, the implementation of the composite material micro-representative volumetric unit generation system can refer to the implementation of the composite material micro-representative volumetric unit generation method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0052] Figure 5 This is a structural block diagram of a composite material micro-representative volumetric unit generation system disclosed in an embodiment of the present invention, such as... Figure 5 As shown, the system includes a parameter configuration module 501, a fiber layout generation module 502, a pore region identification module 503, and a geometric model construction module 504. The structure is described below.

[0053] The parameter configuration module 501 is used to define the key parameters required to establish a representative volume unit. The key parameters include at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius. The fiber layout generation module 502 is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints based on the target fiber volume fraction and the fiber radius using an iterative random movement algorithm. The pore region identification module 503 is used to identify, based on the set of fiber center points, all closed polygon regions consisting of at least three fiber center points and where the distance between any two points is less than the distance threshold, and to take each of the closed polygon regions as potential pore regions to form a set of potential pore regions. The geometric model construction module 504 is used to generate a representative volume element model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores, based on the fiber center point set and the potential pore region set, using the target pore volume fraction as a constraint.

[0054] The embodiments of the present invention achieve the following technical effects: 1. The pore distribution is highly realistic with high geometric fidelity. This invention cleverly resolves the contradiction between the realism and controllability of pores through a physically driven algorithm. Specifically, this invention abandons idealized or completely random pore generation methods, instead simulating the actual formation mechanism of pores in composite materials—that is, the gaps formed by the mutual compression of randomly arranged fibers. By defining pores as closed polygonal regions enclosed by three or more fiber center points, their position, shape, and size are naturally "induced" by the actual random arrangement of fibers. The pore network generated by this method has an irregular morphology and non-uniform spatial distribution, and can spontaneously form a clustering effect that conforms to physical laws, thereby greatly improving the geometric fidelity of the microscopic RVE model, enabling it to more realistically reflect the complex characteristics of defects in actual composite materials.

[0055] 2. Pore content is precisely controllable, with significant engineering application value. While ensuring the accuracy of pore distribution, this invention establishes a direct, predictable, and monotonic quantitative relationship between pore volume fraction and model input by introducing a key adjustable parameter: the "fiber center point distance threshold L." The physical meaning of this threshold L is clear: increasing the value of L connects more fibers, forming larger and more potential pore regions, thus increasing the total pore volume; conversely, decreasing it reduces it. Based on this clear control mechanism, this invention, through an efficient closed-loop feedback control process (i.e., calculation-comparison-adjustment), can quickly and accurately converge the pore volume fraction to a preset target value. This characteristic completely overcomes the inherent defects of traditional random placement methods that rely on inefficient "trial and error." The dual capability of "random shape, controllable quantity" achieved by this invention provides a reliable tool for systematically studying the influence of porosity on the mechanical properties of composite materials (such as equivalent elastic modulus, damage evolution, etc.), and lays a solid foundation for precise material design for specific performance requirements, demonstrating excellent engineering application prospects.

[0056] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described methods for generating microscopic representative volumetric units of composite materials.

[0057] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0058] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method for generating microscopic representative volumetric units of composite materials.

[0059] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0060] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of generating a micro representative volume element of a composite material, characterized in that, include: Define the key parameters required to establish a representative volume unit, including at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius; Based on the target fiber volume fraction and the fiber radius, an iterative random movement algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints; Based on the set of fiber center points, all closed polygonal regions consisting of at least three fiber center points are identified according to a given distance threshold, and the distance between any two points is less than the distance threshold. Each of the closed polygonal regions is taken as a potential pore region to form a set of potential pore regions. Using the target pore volume fraction as a constraint, and based on the set of fiber center points and the set of potential pore regions, a representative volume element model of the composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores is generated by geometric modeling method.

2. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, Based on the target fiber volume fraction and the fiber radius, an iterative random move algorithm is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints, including: Calculate the total number of fibers required based on the target fiber volume fraction and the preset representative volume unit size; According to the hexagonal close packing method, all the calculated fibers are closely arranged within the initial rectangular area to obtain the initial set of fiber center points; For each fiber, a random direction and distance of movement are generated independently, and the coordinates of its candidate new position are calculated. The candidate new position is checked to see if it meets the following two conditions: (1) the fiber does not exceed the boundary of the representative volume unit; (2) the center distance between the fiber and any other fiber is not less than twice the fiber radius. If the above two conditions are met, the candidate new position coordinates are taken as the new position of the fiber; if not, its original position is kept unchanged or a new direction and distance of movement are generated for correction until a candidate new position coordinate that meets the conditions is obtained. The process of generating random movement directions and distances, calculating candidate new position coordinates, condition judgment, and position update is repeated multiple times for all fibers to obtain a set of fiber center point coordinates that are non-overlapping and satisfy boundary constraints.

3. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, The distance threshold is an adjustable geometric parameter whose value is greater than the diameter of the fiber.

4. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, Forming a set of potential porous regions, including: Using each fiber center point in the set of fiber center points as a node, if the Euclidean distance between any two nodes is less than a given distance threshold, then a connecting edge is established between them. Starting from any connecting edge, traverse and search for all the smallest closed paths formed by connecting edges, treating each closed path as a closed polygon region; The closed polygonal regions formed by the center points of completely repeating fibers are removed to form a set of potential porous regions.

5. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, Representative volumetric element models of composite materials with controllable pore volume fraction, random pore shape and location, and containing three phases of fibers, matrix, and pores are generated using geometric modeling methods. These models include: Each potential pore region in the set of potential pore regions is stretched into a three-dimensional cylinder along the thickness direction to form an initial set of pore geometries. Each fiber in the set of fiber center points is modeled as a cylinder, forming a set of fiber geometries; Using a cuboid with the same size as the representative volume unit as the initial matrix entity, a Boolean subtraction operation is performed on the initial matrix entity, first subtracting all entities in the fiber geometry set, and then subtracting all entities in the initial pore geometry set, to obtain the final matrix region; Perform a Boolean subtraction operation on the initial pore geometry set by subtracting all entities in the fiber geometry set to obtain the final pore region; The fiber geometry set, the final pore region, and the final matrix region are merged to form a complete representative volumetric unit model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix, and pores.

6. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, The generated representative volume element model of the composite material is used for the prediction of the equivalent elastic modulus or the simulation of damage evolution of the composite material under multiaxial stress.

7. The method for generating representative microscopic volumetric units of composite materials according to claim 1, characterized in that, Using the target pore volume fraction as a constraint, specifically including: Calculate the actual volume fraction of the final porous region in the currently generated representative volume element model. ,like ,in The target pore volume fraction, To preset an error threshold, the distance threshold is adjusted according to the deviation direction, and the final pore region and representative volumetric element model of the composite material are re-formed based on the adjusted distance threshold until the desired error is met. .

8. The method for generating representative microscopic volumetric units of composite materials according to claim 7, characterized in that, If the actual volume fraction is greater than the target pore volume fraction, the distance threshold is decreased; if the actual volume fraction is less than the target pore volume fraction, the distance threshold is increased.

9. A system for generating representative microscopic volumetric units of composite materials, characterized in that, include: The parameter configuration module is used to define the key parameters required to establish a representative volume cell. The key parameters include at least the target fiber volume fraction, the target pore volume fraction, and the fiber radius. The fiber layout generation module is used to generate a set of fiber center points that satisfy non-overlapping and boundary constraints based on the target fiber volume fraction and the fiber radius using an iterative random movement algorithm. The pore region identification module is used to identify, based on the set of fiber center points, all closed polygonal regions consisting of at least three fiber center points, and where the distance between any two points is less than the distance threshold, and to regard each of the closed polygonal regions as potential pore regions, thereby forming a set of potential pore regions. The geometric model construction module is used to generate a representative volume element model of composite material with controllable pore volume fraction, random pore shape and position, and containing the three phases of fiber, matrix and pores, based on the fiber center point set and the potential pore region set, using the target pore volume fraction as a constraint.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for generating micro-representative volumetric units of composite materials as described in any one of claims 1 to 8.

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