A method for simulating the fluidity of composite materials filled with multi-size fillers

Abstract

The present invention discloses a method for simulating the fluidity of a composite material filled with multi-particle diameter fillers. The method comprises: setting filler geometry information, and dividing unit grids according to the maximum filler particle size; determining filler physical property parameters and filler-filler, filler-wall contact models according to discrete element simulation, and defining a particle generator and model boundary conditions; using a particle diameter growth method to generate a random distribution structure of particles, and setting the initial velocity of the linear distribution of particles; determining a matrix material density and viscosity model, setting boundary conditions, and simulating the three-dimensional Couette flow of the matrix material at a given shear rate; using discrete element software to generate a dynamic link library file, and importing a coupling file; using computational fluid dynamics software to set coupling parameters, and importing a coupling file; combining computational fluid dynamics software with discrete element software for coupling calculation, and calculating the viscosity of the composite material. The present invention can more accurately and comprehensively analyze the viscosity variation law of composite materials filled with fillers of various shapes.

Description

Technical Field

[0001] The invention relates to the technical field of polymer composite material design, and more specifically to a method for simulating the fluidity of a composite material filled with multi-particle size fillers. Background Art

[0002] Composite materials filled with multi-particle size fillers are widely used in the field of electronic packaging, such as underfill and thermal interface material (TIM). As the filler filling density increases, fillers tend to agglomerate, the friction effect between fillers increases, and the interface area between fillers and the matrix increases, causing the fluidity of the composite material to deteriorate, which is not conducive to material processing and molding. The microscopic factors that affect the macroscopic viscosity of composite materials include: filler filling density, filler particle size, particle size distribution, filler and matrix material properties, etc. It is difficult to analyze the specific effects of these microscopic factors using macroscopic rheological experiments, and the experimental cost is high and the cycle is long.

[0003] At present, the methods of using numerical simulation methods to study the fluidity of composite materials include discrete element method (DEM) and computational fluid dynamics (CFD)-discrete element (DEM) coupling method. The discrete element method can take into account the friction effect and contact force between particles, but it cannot accurately describe the particle-matrix interaction. The CFD-DEM coupling method can calculate the interaction between the fluid phase and the solid phase. The grid size of the fully analytical method is equivalent to the size of the smallest eddy in turbulence, and an accurate solution to the flow field can be obtained. However, this method has high computational costs and the number of particles included in the model is limited, especially for cases where the filler particle size varies greatly. For semi-analytical or non-analytical methods, the CFD grid size is larger than the particle diameter, so a larger system can be described.

[0004] After analysis, it is found that existing simulation methods are generally aimed at single-particle filling systems or single-particle filling systems considering particle size distribution, and few studies are aimed at three-particle filling systems commonly used in engineering. In addition, considering the diversity of electronic packaging material matrix and filler types, an efficient calculation scheme that can accurately describe the fluidity of multi-particle filling systems with different types of matrices has not yet been developed. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a method for simulating the fluidity of a composite material filled with multi-size fillers, the method comprising the following steps:

[0006] Set the packing shape, packing density, particle size, particle size distribution, select representative units, and divide the unit grid according to the maximum particle size;

[0007] Import the mesh model into the discrete element software, determine the filler physical properties and filler-filler and filler-wall contact models based on discrete element simulation, and define the particle generator and model boundary conditions;

[0008] The particle diameter growth method is used to generate a random distribution structure of particles under a set filling density, and the initial velocity of the particle linear distribution is set according to the particle position and shear rate;

[0009] Determine the matrix material density and viscosity model, set boundary conditions, simulate the three-dimensional Couette flow of the matrix material at a given shear rate, and obtain a stable flow field;

[0010] The discrete element software generates a dynamic link library file and imports a coupling file in which coupling parameters are set;

[0011] Use computational fluid dynamics software to set coupling parameters and import coupling files;

[0012] The computational fluid dynamics software is coupled with the discrete element software to extract particle position, velocity, and force information, and flow field velocity distribution information and model boundary force information to calculate the viscosity of the composite material.

[0013] Compared with the prior art, the advantage of the present invention is that, taking CFD software Fluent and discrete element simulation software DEMSlab as examples, a composite material viscosity simulation method is provided, which can simulate the viscosity of the composite material under different filling densities, particle sizes, particle size distributions, and filler-matrix interactions according to the composite material formula, analyze the key factors affecting the viscosity of the material, and provide a theoretical basis for improving the fluidity of the composite material.

[0014] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0016] Figure 1 is a flow chart of a method for simulating fluidity of a composite material filled with multi-size fillers according to one embodiment of the present invention;

[0017] Figure 2 is a schematic diagram of a process for simulating the fluidity of a composite material filled with multi-particle-size fillers according to an embodiment of the present invention;

[0018] Figure 3 is a flow chart of particle diameter growth and initial velocity setting according to one embodiment of the present invention;

[0019] Figure 4 It is a schematic diagram of viscosity values ​​of a composite material model filled with three particle sizes at different shear rates according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0021] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0022] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0023] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0024] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0025] Combination Figure 1 and Figure 2 As shown, a method for simulating the fluidity of a composite material filled with multi-size fillers is provided, which includes the following steps.

[0026] Step S110, determine the filler shape, packing density, particle size, and particle size distribution, select representative volume units, and divide the unit grid according to the maximum particle size.

[0027] In order to reduce costs in industrial production, the shape of the filler is generally spherical, and the filler size is generally 3. In one embodiment, three filler particle sizes of 1mm, 3mm, and 5mm are taken as the research object, and the filler volume fraction is 0.3. The model viscosity is calculated by taking volume units of different sizes to ensure that the volume unit is representative.

[0028] After determining the volume unit size, ICEM software was used to establish a cubic simulation box as the computational domain of the particle-matrix composite system. The computational domain was unstructured meshed, and the mesh size was about twice the diameter of the large particle. For example, the side length of the cubic volume unit was 75 mm, and the mesh size was 15 mm.

[0029] Step S120, importing the grid model into the discrete element simulation software, determining the filler physical property parameters and the filler-filler and filler-wall contact models, and defining the particle generator and model boundary conditions.

[0030] Specifically, the mesh file was imported into the DEMSlab discrete element simulation software, and the particle material type was set to Al2O3, where the material density was 3750 kg / m 3 , the elastic modulus of the material is 360GPa, the Poisson's ratio of the material is 0.24; the wall material is glass, and the material density is 2600kg / m 3 The elastic modulus of the material is 55 GPa, and the Poisson's ratio of the material is 0.25. The particle contact model and the particle-wall contact model both use linear models, with a hardness coefficient of 7, a recovery coefficient of 0.9, a sliding friction coefficient of 0.3, and a rolling friction coefficient of 0.

[0031] Corresponding to the CFD grid size, a cube with a side length of 75 mm is defined as the particle feeder, the two horizontal directions are periodic boundary conditions, and the upper and lower surfaces in the vertical direction are set as wall boundary conditions.

[0032] Step S130, using the particle diameter growth method to generate a random distribution structure of particles under a given filling density, and assigning an initial velocity of the linear distribution of the particles according to the particle position and the shear rate.

[0033] At the initial moment, a particle static generation method is used to generate a sparsely packed structure with a packing density of Φ0 = 0.05 in a cubic particle feeder. The corresponding particle diameters are 0.55 mm, 2.8 mm, and 3.9 mm, and the relative volume contents of small particles, medium particles, and large particles are 0.05, 0.4, and 0.55, respectively. Through the particle diameter growth function (such as Figure 3 As shown in the figure, the particle diameter is linearly increased to 1 mm, 3 mm, and 5 mm within 10,000 time steps, forming a randomly distributed particle structure with a filling density of Φ0 = 0.30.

[0034] In order to shorten the coupling calculation time, before the CFD-DEM coupling calculation, the CFD model has obtained a stable flow field under a specified shear rate. When the flow field is coupled with the stationary particles, the relative velocity between the particles and the flow field is too large, resulting in excessive drag. The coupling calculation takes a long time to reach equilibrium again. Therefore, before the CFD-DEM coupling calculation, the initial velocity of the particle filling system is given according to the FLUENT flow field velocity distribution to shorten the coupling calculation time. Specifically, if the particle shear direction is the X direction, the vertical shear direction is the Z direction, and the shear rate is The position coordinates of particle i are x i ,y i , z i , then the velocity of particle i along the X direction is (like Figure 3 shown).

[0035] Step S140, determining the density and viscosity model of the matrix material, setting boundary conditions, simulating the three-dimensional Couette flow of the matrix material at a given shear rate, and obtaining a stable flow field.

[0036] In this step, the matrix material is simulated at a given shear rate The Couette flow under the condition of γ is stable.

[0037] Specifically, import the mesh file into the Fluent solver, set it to transient calculation, and set the gravity acceleration in the vertical direction to -9.81m / s 2 The flow process does not consider mass transfer or heat transfer. According to the characteristics of the polymer matrix material, the fluid material density is set to 950kg / m 3 , the viscosity adopts the Carreau model and is expressed as:

[0038]

[0039] In the formula, μ ∞ is the infinite shear rate viscosity, μ0 is the zero shear rate viscosity, λ is the unit time parameter, n is a dimensionless parameter, is the shear rate. In one embodiment, the parameter is set to: ∞ =0, μ0=166Pa·s, λ=0.0173s, n=0.538.

[0040] According to the Couette flow characteristics, the boundary conditions are set as follows: periodic boundary conditions in the horizontal direction and wall boundary conditions in the vertical direction. The lower wall is a stationary wall with a no-slip shear boundary condition. The upper wall is a moving wall with a moving speed of No-slip shear boundary conditions are used. The pressure gradient of the periodic boundary along the shear direction is -12Pa / m.

[0041] In one embodiment, the Fluent simulation uses the SIMPLE algorithm, and each sub-item uses the following discrete format: the gradient uses the Green-Gaussian unit, the pressure uses the second-order format, and the momentum uses the second-order upwind format. The residual value is set to 0.0001, that is, the calculation is considered to have reached convergence when the residual of the result calculated by the SIMPLE algorithm is lower than the residual value. Initialize the model, set the time step and calculation time. Specifically, the time step is 1s, the number of calculation steps is 200 steps, and the calculation begins.

[0042] After the calculation is completed, Fluent is used for post-processing to obtain a stable three-dimensional Couette flow field, and the fluid velocity in the shear direction is linearly distributed.

[0043] Step S150, the discrete element software generates a dynamic link library file, sets coupling parameters, such as coupling type, time step ratio, CFD grid type, drag model, etc., and imports the coupling file.

[0044] DEMSlab software generates a dynamic link library file, i.e., a DLL file. The coupling type is set to bidirectional coupling, that is, the effect of the fluid on the particles and the effect of the particles on the fluid are considered at the same time. The time step ratio of the discrete element and CFD is 1, and the CFD grid type is static. The force from the fluid to the particle in fluid-solid coupling is divided into four parts: drag, lift caused by fluid shear, lift caused by fluid and particle rotation, and buoyancy. In one embodiment, only the drag effect is considered, for example, the drag model is set to the commonly used Di Felice / Holze&Sommerfeld model, expressed as:

[0045]

[0046] in, F d0 Mainly determined by the fluid velocity v f With particle velocity v p The relative value of C D is the drag coefficient, and its value is related to the Reynolds number Re p,a Related. p,a When ≤1, When Re p,a >1 hour,

[0047]

[0048] When the particle diameter growth method is used to generate a particle random filling model with a specified filling density, the contact model is set to a hard sphere linear contact model in order to increase the calculation speed and reduce the overlap between particles; the particle velocity increases during the fluid-solid coupling calculation. In order to prevent excessive particle-particle and particle-device overlap during the coupling calculation, the contact model is changed to a nonlinear model that is more consistent with the actual material contact, that is, when the contact overlap increases, the contact spring modulus also increases. In one embodiment, the Hertz contact model is used, the rebound coefficient is set to 0.2, the sliding friction coefficient is set to 0.3, and the rolling friction coefficient is set to 0.

[0049] Step S160, the CFD software sets coupling parameters, such as source terms, solid-liquid interactions, etc., and imports the coupling file.

[0050] Import the DLL file generated by the discrete element software DEMSlab into Fluent and set it to the Euler two-phase flow model, where the first phase is the liquid phase and the second phase is the solid phase. Open the source item for the first phase liquid, set the corresponding udf in the three directions of x, y, and z respectively, and set the velocity of the second phase along the three directions of x, y, and z to a constant of 0. The solid-liquid interaction adopts the settings in DEMSlab. Set the signal transmission between Fluent and DEMSlab, set the time step and calculation time step of Fluent according to the ratio of the time step set by DEMSlab and the DEM / CFD time step, set the residual value to 0.0001, and start the calculation analysis.

[0051] Step S170, CFD software and discrete element software are coupled to calculate, extract particle position, velocity, and force information, extract flow field velocity distribution information and model boundary force information, and calculate the viscosity of the composite material.

[0052] After the calculation is completed, Fluent is used for post-processing to obtain the dynamic shear process of the filler-matrix composite system under three-dimensional Couette flow and the force conditions of the moving wall when the flow field is finally stable.

[0053] For example, the viscosity is calculated based on the shear stress and shear rate on the wall. The formula is:

[0054]

[0055] Where τ is the shear stress on the moving wall, is the shear rate.

[0056] It should be understood that although the above-mentioned granular material type takes Al2O3 as an example, and the filler particle size takes 1mm, 3mm, and 5mm as an example, by setting relevant parameters, the present invention is applicable to various types of fillers and fillers of various particle sizes, and the specific parameters can be set according to actual needs.

[0057] In order to further verify the effect of the present invention, experimental verification was carried out. In the experiment, shear simulation was carried out on three particle size filled composite materials with small particles, medium particles and large particles with diameters of 0.001m, 0.005m and 0.007m respectively, and the relative volume contents of small particles, medium particles and large particles were 0.05, 0.4 and 0.55 respectively. The matrix viscosity model adopts the Carreau model. The representative volume element side length is 0.075m, and the grid size is 0.015m. The viscosity values ​​of the pure fluid model and the composite material model with a particle filling volume fraction of 0.3 at different shear rates were calculated respectively, and the shear stress distribution and velocity distribution of the flow field were characterized. The experimental results are shown in the figure. Figure 4 As shown, Figure 4(a) is the viscosity value of the three-particle-size filled composite material model at different shear rates. The filler particle sizes are 0.001m, 0.005m, and 0.007m, and the relative volume contents of the fillers are 0.05, 0.4, and 0.55. Figure 4 (b) with Figure 4 The marked point model in (a) corresponds to the shear stress distribution and velocity distribution of the flow field. It can be seen that for the pure fluid model, the viscosity calculation scheme of the present invention can give a viscosity calculation value consistent with the theoretical model. Moreover, for the composite material model containing fillers, the viscosity value calculated by the present invention is significantly higher than that of the pure fluid without fillers, and the viscosity decreases with the increase of shear rate, reflecting the rheological properties of shear-thinning materials consistent with the matrix material.

[0058] In summary, compared with the prior art, the present invention has at least the following technical advantages:

[0059] 1) Adopt CFD-DEM coupling calculation method to realize efficient and accurate calculation of viscosity of multi-size filler filling system with certain particle size distribution. Different non-Newtonian fluid viscosity calculation models are selected according to the flow characteristics of the matrix material, and the random distribution model of particles with specified filling density is constructed through the particle diameter growth algorithm. The particle size and particle size distribution are set according to the actual product requirements; in addition, the coupling calculation results are independent of the CFD grid, and multi-core parallel calculation is set to improve the efficiency and accuracy of the coupling calculation.

[0060] 2) After generating a model with a specified filling density through particle diameter growth, set the initial particle velocity consistent with the three-dimensional Couette flow field to avoid excessive drag due to excessive relative velocity between particles and fluid; in addition, setting the initial particle velocity according to the Couette flow velocity field greatly reduces the time it takes for the coupled calculation to reach stability, thereby reducing the computational cost.

[0061] 3) When simulating particle diameter growth, the particle-particle and particle-wall contact models adopt the hard sphere linear contact model, and the contact model adopts the nonlinear contact model during fluid-solid coupling. When generating a high-density filling model, in order to reduce the overlap between particles and particles and between particles and walls and increase the calculation speed, the contact model is set to the hard sphere linear contact model; during fluid-solid coupling, in order to accurately describe the movement and interaction of particles in the flow field, a nonlinear contact model that is more consistent with the actual material is used, avoiding the problem of excessive overlap between particles and particles and between particles and equipment caused by the linear contact model under high-speed particle motion.

[0062] 4) The semi-analytical method is used for fluid-solid coupling calculation. The calculation results are independent of the CFD grid size. Compared with the full analytical method, the representative volume element contains more particles, which can reach 10 5 The magnitude of the calculation model is improved, which improves the representativeness of the calculation model and the accuracy of the results.

[0063] 5) Compared with existing discrete element software, DEMSlab software is easier to build non-spherical particle models, can accurately describe any real particle shape, and can more accurately and comprehensively analyze the viscosity change law of composite materials filled with various shapes of fillers in industrial applications.

[0064] 6) Using general fluid calculation software, it has a wealth of non-Newtonian fluid viscosity calculation models and user-defined function interfaces, which can accurately describe the fluidity of different types of substrates, lowering the threshold for technical personnel to use.

[0065] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0066] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: 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), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0067] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0068] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, Python, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0069] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0070] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0071] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0072] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0073] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for simulating the fluidity of a composite material filled with multi-size fillers, comprising the following steps: Step S1, setting the filler shape, filling density, particle size, and particle size distribution, and selecting representative units to divide the unit grid according to the maximum particle size, wherein the filler is a filled composite material with multiple particle sizes; Step S2, importing the grid model into the discrete element software, determining the filler physical property parameters and the filler-filler and filler-wall contact models according to the discrete element simulation, and defining the particle generator and the model boundary conditions; Step S3, using a particle diameter growth method to generate a random distribution structure of particles under a set filling density, and setting an initial velocity of the linear distribution of particles according to the particle position and shear rate; Step S4, determining the density and viscosity model of the matrix material, setting boundary conditions, simulating the three-dimensional Couette flow of the matrix material at a given shear rate, and obtaining a stable flow field; Step S5, the discrete element software generates a dynamic link library file and imports a coupling file in which coupling parameters are set; Step S6, using computational fluid dynamics software to set coupling parameters and importing coupling files; Step S7, combining the computational fluid dynamics software with the discrete element software for coupling calculation, extracting particle position, velocity, and force information, and extracting flow field velocity distribution information and model boundary force information, and calculating the viscosity of the composite material.

2. The method according to claim 1, characterized in that Step S1 includes the following sub-steps: The particle size was set to 1 mm, 3 mm, and 5 mm, and volume units of different sizes were selected as representative units to calculate the model viscosity respectively; After determining the volume unit size, a cubic simulation box is established as the calculation domain of the particle-matrix composite system; The computational domain is divided into unstructured grids, and the grid size is determined according to the diameter of the large particles.

3. The method according to claim 1, characterized in that Step S2 includes the following sub-steps: Set the particle material type and the corresponding material density, material elastic modulus, and material Poisson's ratio; Set the wall material type and the corresponding material density, material elastic modulus, and material Poisson's ratio; The particle contact model and the particle-wall contact model are set as linear models, and the corresponding hardness coefficient, restitution coefficient, sliding friction coefficient and rolling friction coefficient are set; A cube is defined as a particle feeder, with periodic boundary conditions in the two horizontal directions and wall boundary conditions in the upper and lower surfaces in the vertical direction.

4. The method according to claim 2, characterized in that: Step S3 includes the following sub-steps: At the initial moment, a particle static generation method is used to generate a sparsely packed structure of particles with a predetermined packing density in a cubic particle feeder. The corresponding particle diameters are 0.55 mm for large particles, 2.8 mm for small particles, and 3.9 mm for medium particles. The relative volume contents of each particle are 0.05, 0.4, and 0.55, respectively. The particle diameter is linearly increased to 1 mm, 3 mm, and 5 mm within a set number of time steps through particle diameter growth, forming a random distribution structure of particles with a set filling density.

5. The method according to claim 4, characterized in that Also includes: According to the velocity distribution of the flow field, the initial velocity of the particle filling system is set. If the particle shear direction is the X direction and the vertical shear direction is the Z direction, the shear rate is The position coordinates of particle i are x i ,y i , z i , then the velocity of particle i along the X direction is expressed as 6. The method according to claim 1, characterized in that Step S4 includes the following sub-steps: Import the grid file into the Fluent solver, set it to transient calculation, set the gravity acceleration along the vertical direction, and set the fluid material density according to the polymer matrix material properties. The viscosity adopts the Carreau model. According to the Couette flow characteristics, the boundary conditions are set as follows: periodic boundary conditions in the horizontal direction and wall boundary conditions in the upper and lower boundaries in the vertical direction, where the lower wall is a stationary wall and adopts a no-slip shear boundary condition; the upper wall is a moving wall and adopts a no-slip shear boundary condition; the pressure gradient of the periodic boundary along the shear direction is -12Pa / m; The SIMPLE algorithm is used for calculation to obtain the three-dimensional Couette flow field. The fluid velocity in the shear direction is linearly distributed. The sub-items of the SIMPLE algorithm use the following discrete formats: the gradient uses the Green-Gauss unit, the pressure uses the second-order format, the momentum uses the second-order upwind format, and the residual value is set to 0.0001.

7. The method according to claim 1, characterized in that In step S5, the coupling parameters include coupling type, time step ratio, computational fluid dynamics grid type, and drag model, wherein the coupling type is set to bidirectional coupling to simultaneously consider the effect of the fluid on the particles and the effect of the particles on the fluid; the fluid dynamics grid type is set to static.

8. The method according to claim 1, characterized in that: In step S6, the coupling parameters include source terms and solid-liquid interaction, and are set as an Euler two-phase flow model, where the first phase is the liquid phase and the second phase is the solid phase.

9. The method according to claim 1, characterized in that: In step S7, the viscosity of the composite material is calculated according to the following formula: Where τ is the shear stress on the moving wall, is the shear rate.

10. A computer-readable storage medium having a computer program stored thereon, wherein: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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