Hybrid fiber model mechanical property simulation method and system and related equipment

Abstract

The embodiment of the invention provides a mixed fiber model mechanical property simulation method and system and related equipment. The method comprises the steps that corresponding target-order tensor characterization parameters are determined according to target fiber orientation dispersion of a target area, and the target-order tensor characterization parameters comprise second-order tensor characterization parameters and / or fourth-order tensor characterization parameters; generating a target fiber phase sub constitutive model according to the target order tensor characterization parameter; generating a target mixed fiber model according to the target fiber phase sub constitutive model; and generating a target mechanical property simulation result according to the target mixed fiber model. In this way, under the condition that the simulation precision of the mechanical property of the mixed fiber model is guaranteed, the simulation efficiency can be improved, the simulation precision and the simulation efficiency are both considered, and the balance of the simulation precision and the simulation efficiency is improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of simulation of mechanical properties of materials, and in particular, to a method and system for simulating the mechanical properties of a hybrid fiber model and related devices. Background Art

[0002] The simulation of the mechanical properties of a hybrid fiber model is mainly used to study the response of a composite material composed of two or more different types of fibers under different stress conditions.

[0003] In related technologies, there are problems such as the inability to balance simulation accuracy and simulation efficiency, and poor balance between simulation accuracy and simulation efficiency in the simulation of the mechanical properties of a hybrid fiber model. Summary of the Invention

[0004] According to the embodiments of the present application, a method and system for simulating the mechanical properties of a hybrid fiber model and related devices are provided, which can improve the simulation efficiency while ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, so as to balance simulation accuracy and simulation efficiency and improve the balance between simulation accuracy and simulation efficiency.

[0005] In the first aspect of the present application, a method for simulating the mechanical properties of a hybrid fiber model is provided, including: Determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target region, where the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters; Generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; Generating a target hybrid fiber model according to the target fiber phase sub-constitutive model; Generating a target mechanical property simulation result according to the target hybrid fiber model.

[0006] In some feasible embodiments, the determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target region includes: Determining target orientation distribution information according to the target discrete fiber orientation data, where the target orientation distribution information includes: target orientation distribution information entropy, and / or, target orientation distribution gradient; Determining the target fiber orientation dispersion degree according to the target orientation distribution information.

[0007] In some feasible embodiments, the determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target region further includes: When the target fiber orientation dispersion degree is less than or equal to a preset threshold, determining that the target order tensor characterization parameter corresponds to the second-order tensor characterization parameter; When the target fiber orientation dispersion is greater than a preset threshold, determine that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter.

[0008] In some feasible embodiments, the above-mentioned generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameter includes: Generating a target fiber phase sub-constitutive model according to the target phase fiber distribution function, where the target phase fiber distribution function includes: a target aspect ratio distribution function, and / or, a target orientation distribution function.

[0009] In some feasible embodiments, the above-mentioned generating a target hybrid fiber model according to the target fiber phase sub-constitutive model includes: Generating a target hybrid fiber model according to the volume fractions corresponding to multiple target fiber phase sub-constitutive models.

[0010] In some feasible embodiments, the above-mentioned generating a target mechanical property simulation result according to the target hybrid fiber model includes: Generating a target mechanical property simulation result according to the target hybrid fiber model and target parameters, where the target parameters include: a target length parameter, and / or, a target ratio parameter.

[0011] In some feasible embodiments, the above-mentioned target mechanical property simulation result includes: Target fiber orientation tensor analysis result.

[0012] In a second aspect of the present application, there is provided a system for simulating the mechanical properties of a hybrid fiber model, including: A determination module, configured to determine a corresponding target order tensor characterization parameter according to the target fiber orientation dispersion of a target region, where the target order tensor characterization parameter includes: a second-order tensor characterization parameter, and / or, a fourth-order tensor characterization parameter; A first generation module, configured to generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameter; A second generation module, configured to generate a target hybrid fiber model according to the target fiber phase sub-constitutive model; A third generation module, configured to generate a target mechanical property simulation result according to the target hybrid fiber model.

[0013] In a third aspect of the present application, there is provided an electronic device, including a memory and a processor, where a computer program is stored on the memory, and when the processor executes the computer program, the method described above is implemented.

[0014] In a fourth aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described above is implemented.

[0015] An embodiment of the present application provides a method, system and related devices for simulating the mechanical properties of a hybrid fiber model. The method includes: determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of a target region, where the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters; generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; generating a target hybrid fiber model according to the target fiber phase sub-constitutive model; and generating a target mechanical property simulation result according to the target hybrid fiber model. The present application can improve the simulation efficiency while ensuring the simulation accuracy of the hybrid fiber model, so as to balance the simulation accuracy and simulation efficiency and improve the balance between the simulation accuracy and simulation efficiency.

[0016] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages and aspects of the embodiments of the present application will become more obvious. In the drawings, the same or similar reference numerals denote the same or similar elements, where: Figure 1 is a flowchart of a method for simulating the mechanical properties of a hybrid fiber model according to an embodiment of the present application; Figure 2 is the analysis result of the target fiber orientation tensor corresponding to a glass fiber injection molded part of an automotive front end module according to an embodiment of the present application; Figure 3 is the analysis result of the mechanical properties of a glass fiber injection molded part of an automotive front end module based on the corresponding fiber orientation tensor according to an embodiment of the present application; Figure 4 is the analysis result of the target fiber orientation tensor corresponding to a glass fiber injection molded part of an automotive accelerator pedal according to an embodiment of the present application; Figure 5 is another analysis result of the target fiber orientation tensor corresponding to a glass fiber injection molded part of an automotive accelerator pedal according to an embodiment of the present application; Figure 6 is another analysis result of the target fiber orientation tensor corresponding to a glass fiber injection molded part of an automotive accelerator pedal according to an embodiment of the present application; Figure 7 is the analysis result of the mechanical properties of a glass fiber injection molded part of an automotive accelerator pedal based on the corresponding fiber orientation tensor according to an embodiment of the present application; Figure 8It is a structural schematic diagram of a mechanical property simulation system for a hybrid fiber model provided according to an embodiment of the present application; Figure 9 It is a structural schematic diagram of an electronic device suitable for implementing the embodiments of the present application. Detailed implementation manners

[0018] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0019] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0020] Currently, injection-molded short fiber-reinforced composites are widely used in fields such as automobiles and electronics due to their advantages such as lightweight and high strength. However, the mechanical property simulation of injection-molded short fiber-reinforced composites still faces huge challenges.

[0021] The performance of injection-molded short fiber-reinforced composites highly depends on mesoscopic characteristics such as fiber distribution, orientation, and interfacial bonding. The complex non-uniform structure caused by the injection molding process, such as "skin-core" orientation stratification, local fiber aggregation, etc., makes the traditional homogenization hypothesis fail.

[0022] The mechanical property simulation of the hybrid fiber model is mainly used to study the response of composites composed of two or more different types of fibers under different stress conditions. The related technologies mainly include two categories: macroscopic models and mesoscopic homogenization methods.

[0023] Among them, macroscopic models, such as anisotropic elastoplastic constitutive models, are mainly based on the framework of continuum mechanics. By introducing parameters such as orientation tensors to approximately characterize the fiber reinforcement effect, the composite material is regarded as a homogeneous anisotropic material. Its constitutive equation is usually expressed in the tensor form of the stress-strain relationship, which has the advantage of high computational efficiency. However, it cannot reveal mesoscopic mechanisms such as fiber-matrix interface debonding and local fiber aggregation, and the prediction error in the nonlinear stage is relatively high, resulting in poor simulation accuracy.

[0024] Among them, the microscopic homogenization method mainly realizes cross-scale performance prediction by establishing a representative volume element (RVE) containing the real fiber distribution, such as (Representative Volume Element, RVE), and adopting numerical homogenization or analytical homogenization. The characteristic displacement field is solved through the microscopic scale periodic boundary conditions to achieve the equivalent macroscopic performance. Although the above methods can accurately describe microscopic features such as fiber orientation gradient and interface equality, the above methods rely on the periodicity assumption, which is essentially contradictory to the non-periodic random distribution caused by the injection molding process, and the calculation cost is high, showing an exponential growth. The time-consuming for a single multi-scale nonlinear analysis with millions of degrees of freedom is long, resulting in low simulation efficiency.

[0025] Embodiments of the present application provide a method, a system and related devices for simulating the mechanical properties of a hybrid fiber model, which can improve the simulation efficiency while ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, so as to balance the simulation accuracy and simulation efficiency and improve the balance of the simulation accuracy and simulation efficiency.

[0026] In the first aspect of the present application, a method for simulating the mechanical properties of a hybrid fiber model is provided. Figure 1 It is a schematic flowchart of a method 100 for simulating the mechanical properties of a hybrid fiber model provided by an embodiment of the present application, as Figure 1 shown. The method 100 includes: Step S1; determining the corresponding target order tensor characterization parameter according to the target fiber orientation dispersion degree of the target region, where the target order tensor characterization parameter includes: a second-order tensor characterization parameter, and / or, a fourth-order tensor characterization parameter.

[0027] Exemplarily, the above target region includes: a target local region.

[0028] Exemplarily, the above target order tensor characterization parameter is used to characterize the distribution of the target fiber in the matrix based on the microscopic scale. Among them, the above target fiber may include: short fibers, and / or, long fibers.

[0029] Specifically, the corresponding target order tensor characterization parameter can be determined according to the target fiber orientation dispersion degree of the target local region, where the target order tensor characterization parameter includes: a second-order tensor characterization parameter, and / or, a fourth-order tensor characterization parameter.

[0030] In some feasible implementation manners, the above step S1; determining the corresponding target order tensor characterization parameter according to the target fiber orientation dispersion degree of the target region includes: Step S11; determining the target orientation distribution information according to the target discrete fiber orientation data, where the target orientation distribution information includes: the target orientation distribution information entropy, and / or, the target orientation distribution gradient.

[0031] Exemplarily, based on the target discrete fiber orientation data corresponding to the target region, the target orientation distribution information entropy and / or the change of the target orientation distribution gradient can be calculated in real time.

[0032] Step S12; Determine the target fiber orientation dispersion according to the target orientation distribution information.

[0033] Exemplarily, based on the above-mentioned target orientation distribution information entropy and / or the change of the target orientation distribution gradient, the target fiber orientation dispersion can be determined in real time to achieve the dynamic switching between the second-order tensor characterization parameter and the fourth-order tensor characterization parameter.

[0034] Thus, the above method can accurately determine the target orientation distribution information entropy and / or the target orientation distribution gradient according to the target discrete fiber orientation data corresponding to the target region, so as to improve the determination accuracy of the target fiber orientation dispersion, and thus accurately select the target order tensor characterization parameter corresponding to the target region, so as to avoid using a single tensor characterization parameter for multiple target regions, resulting in the inability to balance the simulation accuracy and simulation efficiency of the mechanical property simulation of the hybrid fiber model, and the poor balance between the simulation accuracy and simulation efficiency. Therefore, the inherent contradiction between the simulation accuracy and simulation efficiency is overcome. While ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, the simulation efficiency is improved, the adaptive orientation tensor distribution function is reconstructed, the mesoscopic mechanical response is equivalent to the macroscopic equivalent performance, and the cross-scale prediction from the randomly distributed microscopic structure to the macroscopic mechanical properties is realized, so as to balance the simulation accuracy and simulation efficiency and improve the balance between the simulation accuracy and simulation efficiency.

[0035] In some feasible embodiments, the above step S1; Determine the corresponding target order tensor characterization parameter according to the target fiber orientation dispersion of the target region, and further includes: Step S13; When the target fiber orientation dispersion is less than or equal to the preset threshold, determine that the target order tensor characterization parameter corresponds to the second-order tensor characterization parameter.

[0036] Exemplarily, when the target fiber orientation dispersion is less than or equal to the preset threshold, that is, the target region belongs to the target orientation distribution flat region, it is determined that the target order tensor characterization parameter corresponds to the second-order tensor characterization parameter.

[0037] Step S14; When the target fiber orientation dispersion is greater than the preset threshold, determine that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter.

[0038] Exemplarily, when the target fiber orientation dispersion is greater than the preset threshold, that is, the target region belongs to the target complex region with significant anisotropy, it is determined that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter.

[0039] Thus, the above method can accurately determine that the target order tensor characterization parameter corresponding to the target region corresponds to the second-order tensor characterization parameter when the target fiber orientation dispersion is less than or equal to the preset threshold; and accurately determine that the target order tensor characterization parameter corresponding to the target region corresponds to the fourth-order tensor characterization parameter when the target fiber orientation dispersion is greater than the preset threshold, so as to realize the adaptive switching of the target order tensor characterization parameters corresponding to multiple target regions.

[0040] Step S2; Generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameter.

[0041] Exemplarily, when the target fiber orientation dispersion is less than or equal to the preset threshold, that is, the target region belongs to the target orientation distribution smooth region, it is determined that the target order tensor characterization parameter corresponds to the second-order tensor characterization parameter, and then a target fiber phase sub-constitutive model corresponding to the target region is generated according to the second-order tensor characterization parameter.

[0042] Thus, the above method can quickly generate a target fiber phase sub-constitutive model corresponding to the target region when the target fiber orientation dispersion is less than or equal to the preset threshold, that is, the target region belongs to the target orientation distribution smooth region, so as to improve the simulation efficiency of the mechanical properties of the hybrid fiber model.

[0043] Exemplarily, when the target fiber orientation dispersion is greater than the preset threshold, that is, the target region belongs to the target complex region with significant anisotropy, it is determined that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter, and then a target fiber phase sub-constitutive model corresponding to the target region is generated according to the fourth-order tensor characterization parameter. Among them, the above target region may include: a thin-walled member region, and / or a region with strong gradient characteristics such as a fiber aggregation region.

[0044] Thus, the above method can perform a refined generation operation on the target fiber phase sub-constitutive model corresponding to the target region when the target fiber orientation dispersion is greater than the preset threshold, that is, the target region belongs to the target complex region with significant anisotropy, so as to accurately capture the fiber orientation space gradient characteristics caused by the injection molding flow corresponding to the target region, and can fully consider the microscopic characteristics such as the orientation gradient and interface phase of the fiber distribution, reduce the model generation error of the target region, and improve the simulation accuracy of the mechanical properties of the hybrid fiber model.

[0045] In some feasible implementation manners, the above step S2; Generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameter, including: Step S21; Generate a target fiber phase sub-constitutive model according to the target phase fiber distribution function, where the target phase fiber distribution function includes: a target aspect ratio distribution function, and / or a target orientation distribution function.

[0046] Exemplarily, a target fiber phase sub-constitutive model corresponding to a target region can be constructed respectively according to a target aspect ratio distribution function and / or a target orientation distribution function.

[0047] Thus, the above method can accurately generate target fiber phase sub-constitutive models corresponding to multiple target regions according to the target aspect ratio distribution function and / or the target orientation distribution function, so as to realize the generation process of a segmented multi-phase doped fiber model, thereby improving the applicability to complex situations in the generation process of the multi-phase doped fiber model.

[0048] Step S3: Generate a target hybrid fiber model according to the target fiber phase sub-constitutive model.

[0049] Exemplarily, the above target hybrid fiber model may include: a model retaining process performance-related features.

[0050] In some feasible embodiments, the above step S3: Generate a target hybrid fiber model according to the target fiber phase sub-constitutive model, includes: Step S31: Generate a target hybrid fiber model according to the volume fractions corresponding to multiple target fiber phase sub-constitutive models.

[0051] Exemplarily, based on the target model, the above target hybrid fiber model, i.e., the macroscopic equivalent material constitutive model, is generated according to the consistency weighted average result of the volume fractions corresponding to multiple target fiber phase sub-constitutive models, wherein the above target model may include: a Voigt model.

[0052] Specifically, the above target hybrid fiber model, i.e., the macroscopic equivalent material constitutive model, can be generated based on the Voigt model according to the consistency weighted average result of the volume fractions corresponding to multiple target fiber phase sub-constitutive models.

[0053] Thus, the above method can accurately construct a target hybrid fiber model according to the volume fractions corresponding to multiple target fiber phase sub-constitutive models, thereby improving the generation accuracy of the target hybrid fiber model, i.e., the macroscopic equivalent material constitutive model.

[0054] Step S4: Generate a target mechanical property simulation result according to the target hybrid fiber model.

[0055] In some feasible embodiments, the above step S4: Generate a target mechanical property simulation result according to the target hybrid fiber model, includes: Step S41: Generate a target mechanical property simulation result according to the target hybrid fiber model and target parameters, wherein the target parameters include: a target length parameter and / or a target ratio parameter.

[0056] Exemplarily, the mechanical property simulation results corresponding to each target fiber can be generated according to the target hybrid fiber model and the target length parameter, and the above-mentioned target mechanical property simulation results can be generated according to the mechanical property simulation results corresponding to each target fiber and the target proportion parameter.

[0057] Thus, the above method can accurately and efficiently generate the target mechanical property simulation results according to the target hybrid fiber model and the target parameters.

[0058] In some feasible embodiments, the above-mentioned target mechanical property simulation results include: the analysis result of the target fiber orientation tensor.

[0059] Exemplarily, the analysis results of the target fiber orientation tensor corresponding to each target fiber can be generated according to the target hybrid fiber model.

[0060] Specifically, as Figures 2 to 7 shown, the analysis results of the target fiber orientation tensor corresponding to each target fiber in the automotive component can be generated according to the target hybrid fiber model corresponding to the automotive component, where the above-mentioned automotive component can include: the glass fiber injection molded part of the automotive front end module, the glass fiber injection molded part of the automotive accelerator pedal, etc.

[0061] Thus, the above method can accurately generate the analysis results of the target fiber orientation tensor according to the target hybrid fiber model, thereby improving the generation accuracy and generation efficiency of the analysis results of the target fiber orientation tensor.

[0062] Based on this, the embodiment of the present application provides a method for simulating the mechanical properties of a hybrid fiber model. By determining the corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target region, where the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters; generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; generating a target hybrid fiber model according to the target fiber phase sub-constitutive model; generating a target mechanical property simulation result according to the target hybrid fiber model, it is possible to realize multi-scale finite element modeling, represent the material microscopic constitutive characteristics by using the analytical homogenization method, characterize the distribution of fibers in the matrix by using second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters for the fibers at the microscopic scale, and improve the simulation efficiency while ensuring the simulation accuracy of the hybrid fiber model mechanical properties, so as to balance the simulation accuracy and simulation efficiency and improve the balance of the simulation accuracy and simulation efficiency.

[0063] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0064] The above is the introduction of the method embodiments. The following further describes the solution of this application through system embodiments.

[0065] Figure 8 Fig. shows a structural schematic diagram of a hybrid fiber model mechanical property simulation system 200 proposed by an embodiment of this application, as Figure 8 shown, the system 200 includes: a determination module 210, a first generation module 220, a second generation module 230, and a third generation module 240.

[0066] The determination module 210 is configured to determine a corresponding target order tensor characterization parameter according to the target fiber orientation dispersion degree of the target region, where the target order tensor characterization parameter includes: a second-order tensor characterization parameter, and / or, a fourth-order tensor characterization parameter; The first generation module 220 is configured to generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameter; The second generation module 230 is configured to generate a target hybrid fiber model according to the target fiber phase sub-constitutive model; The third generation module 240 is configured to generate a target mechanical property simulation result according to the target hybrid fiber model.

[0067] It should be noted that since the core solver of the current Computer Aided Engineering (CAE) platform adopts a closed architecture, for example: ABAQUS, ANSYS, its material constitutive module only pre-sets classical macroscopic models (such as Hill plasticity, Tsai-Wu criterion), and regards the analytical homogenization method as a "user-defined function". It is necessary to run the macroscopic finite element solver and the constitutive calculation program simultaneously to enable real-time exchange of stress and strain data through inter-process communication. The above cross-process interaction increases the calculation delay of a single increment step.

[0068] In some feasible embodiments, the mechanical property simulation system of the hybrid fiber model provided by the present application can perform low-level reconstruction of the kernel algorithm through C++, and natively integrate improved analytical homogenization methods such as the Mori-Tanaka method into the core kernel of the implicit structural solver, so as to eliminate the time consumption of cross-process data interaction, realize seamless coupling calculation from the fiber orientation at the microscale to the macroscopic mechanical response, improve the calculation efficiency, and further improve the simulation efficiency.

[0069] In some feasible embodiments, the mechanical property simulation system of the hybrid fiber model provided by the present application is provided with a target interface, including: an injection molding structure co-simulation data interface to achieve seamless docking between process software such as Moldflow and the structural analysis platform, and ensure the accurate transmission of key process parameters such as fiber orientation and volume fraction.

[0070] Therefore, the mechanical property simulation system of the hybrid fiber model provided by the present application can reconstruct the architecture design of traditional CAE software, embed the core algorithm of multi-scale homogenization calculation that originally required cross-process communication into the implicit iterative solution process. The above-mentioned tightly coupled architecture can eliminate the time-consuming inter-process data exchange in traditional multi-scale simulations, improve the applicability to the simulation of complex engineering structures, and improve the simulation efficiency.

[0071] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.

[0072] In the third aspect of the present application, an electronic device is provided, including a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program, the method described above is implemented.

[0073] Figure 9 The structural schematic diagram of the electronic device suitable for implementing the embodiments of the present application is shown.

[0074] As Figure 9 shown, the electronic device includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0075] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0076] Specifically, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, the above functions defined in the system of the present application are executed.

[0077] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described above is implemented.

[0078] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the aforementioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] The units or modules involved in the embodiments described in this application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0081] As another aspect, this application also provides a computer-readable storage medium. The computer-readable storage medium can be included in the electronic device described in the foregoing embodiments; or it can exist independently without being assembled into the electronic device. The foregoing computer-readable storage medium stores one or more programs, and when the foregoing programs are executed by one or more processors, they implement the methods described in this application.

[0082] The above description is only a preferred embodiment of this application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the application involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing inventive concept. For example, the technical solutions formed by mutually replacing the above features with technical features having similar functions (but not limited to) described in this application.

Claims

1. A simulation method for the mechanical properties of a hybrid fiber model, characterized in that, Including: Determine corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target area, wherein the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters; Generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; Generate a target hybrid fiber model according to the target fiber phase sub-constitutive model; Generate a target mechanical property simulation result according to the target hybrid fiber model.

2. The mechanical property simulation method of the hybrid fiber model according to claim 1, wherein The determining the corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target area includes: Determine target orientation distribution information according to target discrete fiber orientation data, wherein the target orientation distribution information includes: target orientation distribution information entropy, and / or, target orientation distribution gradient; Determine the target fiber orientation dispersion degree according to the target orientation distribution information.

3. The mechanical property simulation method of the hybrid fiber model according to claim 2, wherein Also including: In the case where the target fiber orientation dispersion degree is less than or equal to a preset threshold, determine that the target order tensor characterization parameters correspond to the second-order tensor characterization parameters; In the case where the target fiber orientation dispersion degree is greater than the preset threshold, determine that the target order tensor characterization parameters correspond to the fourth-order tensor characterization parameters.

4. The mechanical property simulation method of the hybrid fiber model according to claim 1, characterized in that The generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameters includes: Generate the target fiber phase sub-constitutive model according to a target phase fiber distribution function, wherein the target phase fiber distribution function includes: a target aspect ratio distribution function, and / or, a target orientation distribution function.

5. The mechanical property simulation method of the hybrid fiber model according to claim 1, characterized in that, The generating a target hybrid fiber model according to the target fiber phase sub-constitutive model includes: Generate the target hybrid fiber model according to the volume fractions corresponding to a plurality of the target fiber phase sub-constitutive models.

6. The mechanical property simulation method of the hybrid fiber model according to claim 1, wherein The generating a target mechanical property simulation result according to the target hybrid fiber model includes: Generate the target mechanical property simulation result according to the target hybrid fiber model and target parameters, wherein the target parameters include: target length parameters, and / or, target ratio parameters.

7. The mechanical property simulation method of the hybrid fiber model according to any one of claims 1 to 6, characterized in that, The target mechanical property simulation result includes: Target fiber orientation tensor analysis result.

8. A mechanical property simulation system for a hybrid fiber model, characterized in that, Including: A determining module, configured to determine corresponding target order tensor characterization parameters according to the target fiber orientation dispersion degree of the target area, wherein the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or, fourth-order tensor characterization parameters; A first generating module, configured to generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; A second generating module, configured to generate a target hybrid fiber model according to the target fiber phase sub-constitutive model; A third generating module, configured to generate a target mechanical property simulation result according to the target hybrid fiber model.

9. An electronic device, comprising a memory and a processor, wherein a computer program is stored on the memory, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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