Hybrid fiber model mechanical property simulation method, system and related device

Abstract

Embodiments of the present application provide a hybrid fiber model mechanical property simulation method, system and related equipment. The above method comprises: determining a corresponding target order tensor characterization parameter according to a target fiber orientation dispersion of a target region, wherein the target order tensor characterization parameter comprises a second order tensor characterization parameter and / or a fourth order tensor characterization parameter; generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameter; 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. In this way, the simulation efficiency can be improved while ensuring the simulation accuracy of the hybrid fiber model, so as to balance the simulation accuracy and efficiency and improve the balance of simulation accuracy and efficiency.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of material mechanical property simulation, and in particular to a hybrid fiber model mechanical property simulation method, system, and related equipment. Background Art

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

[0003] In the related art, the simulation of mechanical properties of hybrid fiber models has problems such as the inability to balance simulation accuracy and simulation efficiency, and the poor balance between simulation accuracy and simulation efficiency. Summary of the Invention

[0004] According to the embodiments of the present application, a method, system and related equipment for simulating the mechanical properties of a hybrid fiber model 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 take into account both simulation accuracy and simulation efficiency and improve the balance between simulation accuracy and simulation efficiency.

[0005] In a first aspect of the present application, a method for simulating the mechanical properties of a hybrid fiber model is provided, comprising:

[0006] Determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion of the target area, wherein the target order tensor characterization parameters include: second order tensor characterization parameters and / or fourth order tensor characterization parameters;

[0007] Generate the target fiber phase sub-constitutive model according to the target order tensor characterization parameters;

[0008] Generate a target hybrid fiber model based on the target fiber phase sub-constitutive model;

[0009] Based on the target hybrid fiber model, the target mechanical properties simulation results are generated.

[0010] In some feasible implementations, the above-mentioned determination of the corresponding target order tensor characterization parameter based on the target fiber orientation dispersion of the target region includes:

[0011] Determining target orientation distribution information according to the target discrete fiber orientation data, wherein the target orientation distribution information includes: target orientation distribution information entropy and / or target orientation distribution gradient;

[0012] The target fiber orientation dispersion is determined based on the target orientation distribution information.

[0013] In some feasible implementations, the above-mentioned determination of the corresponding target order tensor characterization parameter based on the target fiber orientation dispersion of the target region further includes:

[0014] When the target fiber orientation dispersion is less than or equal to a preset threshold, determining that the target order tensor characterization parameter corresponds to a second order tensor characterization parameter;

[0015] When the target fiber orientation dispersion is greater than a preset threshold, it is determined that the target order tensor characterization parameter corresponds to a fourth order tensor characterization parameter.

[0016] In some feasible implementations, the target fiber phase sub-constitutive model is generated according to the target order tensor characterization parameters, including:

[0017] A target fiber phase sub-constitutive model is generated according to the target phase fiber distribution function, wherein the target phase fiber distribution function includes: a target major diameter distribution function and / or a target orientation distribution function.

[0018] In some feasible implementations, generating a target hybrid fiber model based on the target fiber phase sub-constitutive model includes:

[0019] A target hybrid fiber model is generated according to the volume fractions corresponding to multiple target fiber phase sub-constitutive models.

[0020] In some feasible implementations, generating target mechanical property simulation results based on the target hybrid fiber model includes:

[0021] A target mechanical property simulation result is generated 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.

[0022] In some feasible implementations, the above-mentioned target mechanical property simulation results include:

[0023] Target fiber orientation tensor analysis results.

[0024] In a second aspect of the present application, a hybrid fiber model mechanical properties simulation system is provided, comprising:

[0025] a determination module, configured to determine corresponding target order tensor characterization parameters according to the target fiber orientation dispersion of the target area, wherein the target order tensor characterization parameters include: second order tensor characterization parameters and / or fourth order tensor characterization parameters;

[0026] The first generation module is used to generate a target fiber phase sub-constitutive model according to target order tensor characterization parameters;

[0027] The second generation module is used to generate a target hybrid fiber model based on the target fiber phase sub-constitutive model;

[0028] The third generation module is used to generate target mechanical property simulation results based on the target hybrid fiber model.

[0029] In a third aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described above when executing the computer program.

[0030] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0031] The embodiments of the present application provide a method, system, and related equipment for simulating the mechanical properties of a hybrid fiber model, wherein the method includes: determining corresponding target-order tensor characterization parameters based on the target fiber orientation dispersion in the target area, wherein 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 based on the target fiber phase sub-constitutive model; and generating target mechanical property simulation results based on the target hybrid fiber model. The present application can improve simulation efficiency while ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, thereby taking into account both simulation accuracy and simulation efficiency, and improving the balance between simulation accuracy and simulation efficiency.

[0032] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0034] Figure 1 A schematic diagram of the process of simulating the mechanical properties of a hybrid fiber model provided in accordance with an embodiment of the present application;

[0035] Figure 2 The target fiber orientation tensor analysis result corresponding to a glass fiber injection molded part of an automobile front-end module provided according to an embodiment of the present application;

[0036] Figure 3 The mechanical properties analysis results of a glass fiber injection molded part of an automobile front-end module based on the corresponding fiber orientation tensor provided in an embodiment of the present application are as follows;

[0037] Figure 4The target fiber orientation tensor analysis result corresponding to a glass fiber injection molded part of an automobile accelerator pedal provided in an embodiment of the present application;

[0038] Figure 5 Target fiber orientation tensor analysis results corresponding to another glass fiber injection molded part of an automobile accelerator pedal provided according to an embodiment of the present application;

[0039] Figure 6 Target fiber orientation tensor analysis results corresponding to another glass fiber injection molded part of an automobile accelerator pedal provided according to an embodiment of the present application;

[0040] Figure 7 The mechanical properties analysis results of a glass fiber injection molded part of an automobile accelerator pedal based on the corresponding fiber orientation tensor provided in an embodiment of the present application are as follows;

[0041] Figure 8 Schematic diagram of the structure of a hybrid fiber model mechanical properties simulation system provided according to an embodiment of the present application;

[0042] Figure 9 Schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0044] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0045] Currently, injection molded short fiber reinforced composites are widely used in the automotive, electronics and other fields due to their advantages such as light weight and high strength. However, the simulation of the mechanical properties of injection molded short fiber reinforced composites still faces huge challenges.

[0046] The performance of injection-molded short-fiber-reinforced composites is highly dependent on microscopic characteristics such as fiber distribution, orientation, and interfacial bonding. However, the complex non-uniform structure caused by the injection molding process, such as "skin-core" orientation stratification and localized fiber aggregation, renders the traditional homogenization assumption invalid.

[0047] Hybrid fiber model mechanical property simulation is primarily used to study the response of composite materials composed of two or more different fiber types under different stress conditions. Related technologies primarily fall into two categories: macroscopic models and microscopic homogenization methods.

[0048] Among them, macroscopic models, such as the anisotropic elastoplastic constitutive model, are mainly based on the framework of continuum mechanics. By introducing parameters such as the orientation tensor 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. It has the advantage of high computational efficiency, but cannot reveal microscopic mechanisms such as debonding at the fiber-matrix interface and local fiber aggregation. The prediction error in the nonlinear stage is high, resulting in poor simulation accuracy.

[0049] Among them, the mesoscopic homogenization method mainly establishes a representative volume unit containing the actual fiber distribution, such as: (Representative Volume Element, RVE), uses numerical homogenization or analytical homogenization to achieve cross-scale performance prediction, and solves the characteristic displacement field through mesoscale periodic boundary conditions to achieve equivalent macroscopic performance. Although the above method can accurately characterize microscopic features such as fiber orientation gradient and interface phase, it relies on the periodic assumption and is essentially inconsistent with the non-periodic random distribution caused by the injection molding process. The computational cost is high and grows exponentially. The single multi-scale nonlinear analysis of millions of degrees of freedom takes a long time, resulting in low simulation efficiency.

[0050] The embodiments of the present application provide a method, system and related equipment 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 take into account both simulation accuracy and simulation efficiency and improve the balance between simulation accuracy and simulation efficiency.

[0051] In a first aspect of the present application, a method for simulating the mechanical properties of a hybrid fiber model is provided. Figure 1 A schematic diagram of a hybrid fiber model mechanical properties simulation method 100 provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method 100 includes:

[0052] Step S1: Determine corresponding target order tensor characterization parameters according to the target fiber orientation discreteness of the target area, wherein the target order tensor characterization parameters include: second order tensor characterization parameters, and / or fourth order tensor characterization parameters.

[0053] Exemplarily, the above-mentioned target area includes: a target local area.

[0054] Exemplarily, the target order tensor characterization parameters are used to characterize the distribution of target fibers in the matrix based on a microscopic scale. The target fibers may include short fibers and / or long fibers.

[0055] Specifically, the corresponding target order tensor characterization parameters can be determined according to the target fiber orientation discreteness of the target local area, wherein the target order tensor characterization parameters include: second-order tensor characterization parameters, and / or fourth-order tensor characterization parameters.

[0056] In some feasible implementations, the above step S1: determining the corresponding target order tensor characterization parameter according to the target fiber orientation dispersion of the target area includes:

[0057] Step S11: 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.

[0058] For example, the target orientation distribution information entropy and / or the change in the target orientation distribution gradient may be determined by real-time calculation based on the target discrete fiber orientation data corresponding to the target area.

[0059] Step S12: Determine the target fiber orientation dispersion according to the target orientation distribution information.

[0060] Exemplarily, the target fiber orientation discreteness can be determined in real time based on the above-mentioned target orientation distribution information entropy and / or the change of the target orientation distribution gradient to achieve dynamic switching between second-order tensor characterization parameters and fourth-order tensor characterization parameters.

[0061] Therefore, the above method can accurately determine the target orientation distribution information entropy and / or the target orientation distribution gradient based on the target discrete fiber orientation data corresponding to the target area, so as to improve the determination accuracy of the target fiber orientation discreteness, thereby accurately selecting the target order tensor characterization parameters corresponding to the target area, so as to avoid the use of a single tensor characterization parameter for multiple target areas, resulting in the inability to take into account both the simulation accuracy and the simulation efficiency of the mechanical properties simulation of the hybrid fiber model, and the poor balance between the simulation accuracy and the simulation efficiency, thereby overcoming the inherent contradiction between the simulation accuracy and the simulation efficiency, and ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, improving the simulation efficiency, realizing adaptive orientation tensor distribution function reconstruction, and equating the microscopic mechanical response to the macroscopic equivalent performance, realizing cross-scale prediction from randomly distributed microstructures to macroscopic mechanical properties, so as to take into account both the simulation accuracy and the simulation efficiency, so as to improve the balance between the simulation accuracy and the simulation efficiency.

[0062] In some feasible implementations, the above step S1: determining the corresponding target order tensor characterization parameter according to the target fiber orientation dispersion of the target region, further includes:

[0063] Step S13: When the target fiber orientation dispersion 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.

[0064] Exemplarily, when the target fiber orientation dispersion is less than or equal to a preset threshold, that is, the target region belongs to a region where the target orientation distribution is flat, it is determined that the target-order tensor characterization parameter corresponds to the second-order tensor characterization parameter.

[0065] Step S14: When the target fiber orientation dispersion is greater than a preset threshold, determining that the target order tensor characterization parameter corresponds to a fourth-order tensor characterization parameter.

[0066] Exemplarily, when the target fiber orientation dispersion is greater than a preset threshold, that is, when the target region belongs to a target complex region with significant anisotropy, it is determined that the target order tensor characterization parameter corresponds to a fourth-order tensor characterization parameter.

[0067] Therefore, the above method can achieve the situation where the target fiber orientation discreteness is less than or equal to the preset threshold value, accurately determining that the target order tensor characterization parameters corresponding to the target area correspond to the second-order tensor characterization parameters; when the target fiber orientation discreteness is greater than the preset threshold value, accurately determining that the target order tensor characterization parameters corresponding to the target area correspond to the fourth-order tensor characterization parameters, so as to achieve adaptive switching of the target order tensor characterization parameters corresponding to multiple target areas.

[0068] Step S2: Generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameters.

[0069] Exemplarily, when the target fiber orientation discreteness is less than or equal to a preset threshold, that is, the target area belongs to the area with a flat target orientation distribution, it is determined that the target-order tensor characterization parameter corresponds to the second-order tensor characterization parameter, and then the target fiber phase sub-constitutive model corresponding to the target area is generated based on the second-order tensor characterization parameter.

[0070] Therefore, the above method can quickly generate the target fiber phase sub-constitutive model corresponding to the target area when the target fiber orientation discreteness is less than or equal to the preset threshold, that is, the target area belongs to the area with a flat target orientation distribution, so as to improve the simulation efficiency of the mechanical properties of the hybrid fiber model.

[0071] Exemplarily, when the target fiber orientation discreteness is greater than a preset threshold, that is, when the target area belongs to a target complex area with significant anisotropy, it is determined that the target-order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter, and then the target fiber phase sub-constitutive model corresponding to the target area is generated based on the fourth-order tensor characterization parameter, wherein the above-mentioned target area may include: thin-walled component area, and / or, fiber aggregation area and other areas with strong gradient characteristics.

[0072] Therefore, the above method can perform a refined generation operation on the target fiber phase sub-constitutive model corresponding to the target area when the target fiber orientation discreteness is greater than the preset threshold, that is, the target area belongs to a target complex area with significant anisotropy, so as to accurately capture the fiber orientation spatial gradient characteristics caused by the injection molding flow corresponding to the target area, and fully consider the orientation gradient, interface phase and other microscopic characteristics of the fiber distribution, reduce the model generation error of the target area, and improve the simulation accuracy of the mechanical properties of the hybrid fiber model.

[0073] In some feasible implementations, the above step S2: generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameters, includes:

[0074] Step S21: Generate a target fiber phase sub-constitutive model according to the target phase fiber distribution function, wherein the target phase fiber distribution function includes: a target major diameter distribution function and / or a target orientation distribution function.

[0075] For example, the target fiber phase sub-constitutive model corresponding to the target region can be constructed according to the target major diameter distribution function and / or the target orientation distribution function.

[0076] Therefore, the above method can accurately generate target fiber phase sub-constitutive models corresponding to multiple target regions based on the target length distribution function and / or the target orientation distribution function, thereby realizing the generation process of the segmented multi-phase doped fiber model to improve the applicability to complex situations in the multi-phase doped fiber model generation process.

[0077] Step S3: Generate a target hybrid fiber model based on the target fiber phase sub-constitutive model.

[0078] Illustratively, the target hybrid fiber model may include a model that retains process performance-related features.

[0079] In some feasible implementations, the above step S3: generating a target hybrid fiber model according to the target fiber phase sub-constitutive model, includes:

[0080] Step S31: Generate a target hybrid fiber model according to the volume fractions corresponding to the multiple target fiber phase sub-constitutive models.

[0081] Exemplarily, according to the consistent weighted average result of the volume fractions corresponding to multiple target fiber phase sub-constitutive models, the above-mentioned target hybrid fiber model, that is, the macro-equivalent material constitutive model, is generated based on the target model, wherein the above-mentioned target model may include: Voigt model.

[0082] Specifically, the above-mentioned target hybrid fiber model, i.e., the macro-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.

[0083] Therefore, the above method can accurately construct the 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, that is, the macroscopic equivalent material constitutive model.

[0084] Step S4: Generate target mechanical property simulation results based on the target hybrid fiber model.

[0085] In some feasible implementations, the above step S4: generating target mechanical property simulation results according to the target hybrid fiber model, includes:

[0086] Step S41: Generate target mechanical property simulation results according to the target hybrid fiber model and target parameters, wherein the target parameters include: target length parameters and / or target ratio parameters.

[0087] 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.

[0088] Therefore, the above method can accurately and efficiently generate target mechanical properties simulation results based on the target hybrid fiber model and target parameters.

[0089] In some feasible implementations, the above-mentioned target mechanical property simulation results include: target fiber orientation tensor analysis results.

[0090] For example, a target fiber orientation tensor analysis result corresponding to each target fiber may be generated according to the target hybrid fiber model.

[0091] Specifically, if Figures 2 to 7 As shown, the target fiber orientation tensor analysis results corresponding to each target fiber in the automobile component can be generated based on the target hybrid fiber model corresponding to the automobile component, wherein the above-mentioned automobile component may include: automobile front-end module glass fiber injection molding parts, automobile accelerator pedal glass fiber injection molding parts, etc.

[0092] Therefore, the above method can accurately generate the target fiber orientation tensor analysis results according to the target hybrid fiber model, thereby improving the generation accuracy and efficiency of the target fiber orientation tensor analysis results.

[0093] Based on this, an 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 discreteness of the target area, wherein 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 fiber phase sub-constitutive model; generating a target hybrid fiber model according to the target hybrid fiber model, and generating a target mechanical property simulation result. This method can be implemented based on multi-scale finite element modeling, using an analytical homogenization method to represent the microscopic constitutive properties of the material, and using second-order tensor characterization parameters and / or fourth-order tensor characterization parameters for micro-scale fibers to characterize the distribution of fibers in the matrix. While ensuring the simulation accuracy of the mechanical properties of the hybrid fiber model, the simulation efficiency is improved, so as to take into account both simulation accuracy and simulation efficiency and improve the balance between simulation accuracy and simulation efficiency.

[0094] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0095] The above is an introduction to the method embodiment. The following is a system embodiment to further illustrate the solution described in this application.

[0096] Figure 8 FIG. 2 shows a structural diagram of a hybrid fiber model mechanical property simulation system 200 proposed in an embodiment of the present application, as shown in FIG. Figure 8 As 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 .

[0097] A determination module 210 is configured to determine corresponding target order tensor characterization parameters according to the target fiber orientation dispersion of the target area, wherein the target order tensor characterization parameters include: second order tensor characterization parameters and / or fourth order tensor characterization parameters;

[0098] A first generation module 220 is used to generate a target fiber phase sub-constitutive model according to target order tensor characterization parameters;

[0099] The second generation module 230 is used to generate a target hybrid fiber model based on the target fiber phase sub-constitutive model;

[0100] The third generation module 240 is used to generate target mechanical property simulation results according to the target hybrid fiber model.

[0101] It should be noted that since the core solvers of current computer-aided engineering (CAE) platforms adopt a closed architecture, such as ABAQUS and ANSYS, their material constitutive modules only pre-set classic macro models (such as Hill plasticity and Tsai-Wu criterion), and regard the analytical homogenization method as a "user-defined function", the macro finite element solver and the constitutive calculation program must be run simultaneously to enable inter-process communication to realize real-time exchange of stress and strain data. The above cross-process interaction leads to an increase in the calculation delay of a single incremental step.

[0102] In some feasible embodiments, the hybrid fiber model mechanical properties simulation system provided in this application can perform underlying reconstruction of the core algorithm through C++, and natively integrate improved Mori-Tanaka and other analytical homogenization methods into the core kernel of the implicit structural solver to eliminate the time consumption of cross-process data interaction, and realize seamless coupling calculation from micro-scale fiber orientation to macro-mechanical response, so as to improve computing efficiency and thus improve simulation efficiency.

[0103] In some feasible embodiments, the hybrid fiber model mechanical property simulation system provided in this application is provided with a target interface, including: an injection molding structure joint simulation data interface to achieve seamless docking of process software such as Moldflow with the structural analysis platform, ensuring the accurate transmission of key process parameters such as fiber orientation and volume fraction.

[0104] Therefore, the hybrid fiber model mechanical properties simulation system provided in this application can reconstruct the architectural design of traditional CAE software, and embed the multi-scale homogenization calculation core algorithm 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 simulation, improve the applicability of complex engineering structure simulation, and improve simulation efficiency.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0106] In a third aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described above when executing the computer program.

[0107] Figure 9 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present application is shown.

[0108] like Figure 9 As 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. Various programs and data required for the operation of the electronic device are also stored in the RAM 303. The CPU 301, ROM 302 and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0109] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. 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. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0110] In particular, 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 code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-mentioned functions defined in the system of the present application are executed.

[0111] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0112] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media 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 that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0114] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0115] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the method described in the present application.

[0116] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A method for simulating the mechanical properties of a hybrid fiber model, characterized in that: include: Determining corresponding target order tensor characterization parameters according to the target fiber orientation dispersion of the target area, wherein the target order tensor characterization parameters include: second-order tensor characterization parameters and / or fourth-order tensor characterization parameters; Specifically, target orientation distribution information is determined according to the target discrete fiber orientation data, wherein the target orientation distribution information includes: target orientation distribution information entropy, and / or target orientation distribution gradient; determining the target fiber orientation dispersion according to the target orientation distribution information; Furthermore, it also includes: When the target fiber orientation dispersion is less than or equal to a preset threshold, determining that the target first-order tensor characterization parameter corresponds to the second-order tensor characterization parameter; In a case where the target fiber orientation dispersion is greater than the preset threshold, determining that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter; 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; According to the target hybrid fiber model, target mechanical property simulation results are generated.

2. The hybrid fiber model mechanical properties simulation method according to claim 1, characterized in that: Generating a target fiber phase sub-constitutive model according to the target order tensor characterization parameter includes: The target fiber phase sub-constitutive model is generated according to the target phase fiber distribution function, wherein the target phase fiber distribution function includes: a target major diameter distribution function and / or a target orientation distribution function.

3. The hybrid fiber model mechanical properties simulation method according to claim 1, characterized in that: Generating a target hybrid fiber model according to the target fiber phase sub-constitutive model includes: The target hybrid fiber model is generated according to the volume fractions corresponding to the plurality of target fiber phase sub-constitutive models.

4. The hybrid fiber model mechanical properties simulation method according to claim 1, characterized in that: Generating target mechanical property simulation results according to the target hybrid fiber model includes: The target mechanical property simulation result is generated 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.

5. The hybrid fiber model mechanical properties simulation method according to any one of claims 1 to 4, characterized in that: The target mechanical performance simulation results include: Target fiber orientation tensor analysis results.

6. A hybrid fiber model mechanical properties simulation system, characterized in that: include: a determination module, configured to determine corresponding target order tensor characterization parameters according to the target fiber orientation dispersion of the target area, wherein the target order tensor characterization parameters include: second-order tensor characterization parameters and / or fourth-order tensor characterization parameters; Specifically, target orientation distribution information is determined according to the target discrete fiber orientation data, wherein the target orientation distribution information includes: target orientation distribution information entropy, and / or target orientation distribution gradient; determining the target fiber orientation dispersion according to the target orientation distribution information; Furthermore, it also includes: When the target fiber orientation dispersion is less than or equal to a preset threshold, determining that the target first-order tensor characterization parameter corresponds to the second-order tensor characterization parameter; In a case where the target fiber orientation dispersion is greater than the preset threshold, determining that the target order tensor characterization parameter corresponds to the fourth-order tensor characterization parameter; A first generating module is used to generate a target fiber phase sub-constitutive model according to the target order tensor characterization parameters; A second generation module is used to generate a target hybrid fiber model based on the target fiber phase sub-constitutive model; The third generation module is used to generate target mechanical property simulation results according to the target hybrid fiber model.

7. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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