Numerical prediction method and storage medium for flow field and performance of melt filter

Abstract

This application discloses a numerical prediction method and storage medium for the flow field and performance of a melt filter, relating to the field of computer-aided design technology. It solves the problem of poor product quality caused by localized melt retention and deterioration in existing filters. The method includes: establishing a melt medium fluid model; determining the flow state of the melt within the filter; establishing a pressure drop model of the melt medium flowing through the filter element based on the flow state; establishing a melt medium filter fluid domain model based on the melt medium fluid model and pressure drop model, and performing mesh generation; setting software parameters based on the melt medium filter fluid domain model; verifying the reliability of the numerical prediction method for the melt medium filter flow field; and performing numerical prediction of the melt filter based on the melt medium filter fluid domain model. This achieves the purpose of reliable prediction and three-dimensional visualization of the flow characteristics and pressure loss characteristics of high-viscosity melt media within the filter.

Description

Technical Field

[0001] This application relates to the field of computer-aided design technology, and in particular to a method and storage medium for numerical prediction of flow field and performance of melt filter. Background Technology

[0002] Polyester melt filters are crucial equipment in the production processes of PET raw materials, high-speed spinning, plastic film, and blow molding. They are primarily used to remove impurities and unmelted particles from the melt, achieving seamless filtration and ensuring product quality. The working environment and media characteristics of polyester melt filters can be summarized as "four highs": high pressure, high temperature, high humidity, and high molecular weight polymers. Due to the harsh working environment, even with measures such as one operating and one standby filter and regular filter element cleaning, localized melt retention and deterioration can still occur within the filter, posing a serious threat to the quality of PET chips and downstream products.

[0003] Analysis suggests that, due to the high-temperature, high-pressure, and enclosed working environment, designers find it difficult to clearly grasp the filtration and flow state of high-viscosity polyester melt containing impurities within the enclosed space of the filter. This leads to problems such as localized poor flow of the polyester melt medium in the filter. Summary of the Invention

[0004] This application provides a method and storage medium for predicting the flow field and performance of a melt filter, which solves the problem of poor product quality caused by local melt retention and deterioration in the filter in the prior art. It achieves the purpose of reliable prediction and three-dimensional visualization of the flow characteristics and pressure loss characteristics of high-viscosity melt media in the filter.

[0005] In a first aspect, embodiments of the present invention provide a method for numerical prediction of the flow field and performance of a melt filter, the method comprising:

[0006] Establish a fluid model for the melt medium;

[0007] Determine the flow state of the melt within the filter;

[0008] Based on the flow state, establish a pressure drop model for the melt medium flowing through the filter element;

[0009] Based on the melt medium fluid model and the pressure drop model, a melt medium filter fluid domain model is established, and mesh generation is performed.

[0010] Set the software parameters according to the fluid domain model of the melt medium filter;

[0011] Verify the reliability of the numerical prediction method for the flow field of the melt medium filter;

[0012] Numerical predictions of the flow field and performance of the melt filter are made based on the fluid domain model of the melt medium filter.

[0013] In conjunction with the first aspect, in one possible implementation, establishing the melt medium fluid model includes:

[0014] Acquire the physical properties of the melt medium under service conditions, the viscosity variation with shear rate within a specific shear rate range, the non-Newtonian index, and the consistency coefficient;

[0015] A fluid model for the melt medium is established based on the above parameters.

[0016] In conjunction with the first aspect, in one possible implementation, determining the flow state of the melt within the filter includes:

[0017] Obtain the design parameters of the filter, and calculate the flow velocity and Reynolds number at different parts of the filter;

[0018] The flow state of the melt inside the filter is determined based on the parameters involved, the flow rate, and the Reynolds number.

[0019] In conjunction with the first aspect, in one possible implementation, the pressure drop model of the filter element includes:

[0020] The filter element is determined to be a metal fiber filter element; the filter element is determined to be a porous media domain; the resistance model of the filter is determined; the viscous resistance coefficient value under different precision filter elements is determined; and the porosity of the porous media domain is determined.

[0021] In conjunction with the first aspect, in one possible implementation, the mesh generation includes:

[0022] The fluid domain model of the melt medium filter is partially simplified, retaining the part that generates flow resistance;

[0023] The simplified fluid domain model of the melt medium filter is divided into regions, and each region is discretized into a mesh.

[0024] In conjunction with the first aspect, in one possible implementation, the software parameters include: the physical property parameters of the melt medium, the radial viscous resistance coefficient of the filter element in the filter, the inner wall surface characteristics of the filter, and the convection pattern parameters.

[0025] In conjunction with the first aspect, in one possible implementation, verifying the reliability of the numerical prediction method for the melt medium filter flow field includes:

[0026] Determine whether the fluid velocity in different fluid domains of the filter conforms to the principles of fluid mechanics;

[0027] Determine whether the pressure distribution and velocity distribution of the melt medium correspond to those of different parts of the flow.

[0028] Calculate the total pressure loss from the filter inlet to the outlet and compare it with the actual operating pressure loss.

[0029] In conjunction with the first aspect, in one possible implementation, the step of performing numerical prediction of the melt filter flow field and performance based on the melt medium filter fluid domain model includes:

[0030] The parameters of the melt filter were changed and tested.

[0031] The distribution of filter elements in the filter was changed for testing.

[0032] Secondly, embodiments of the present invention provide a numerical prediction server for the flow field and performance of a melt filter, the server including a memory and a processor;

[0033] The memory is used to store computer-executable instructions;

[0034] The processor is configured to execute the computer-executable instructions to implement the method described in the first aspect and any one of the first aspects.

[0035] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing executable instructions, wherein a computer executing the executable instructions is capable of implementing the method described in the first aspect and any one of the first aspects.

[0036] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0037] This invention employs a method and storage medium for numerical prediction of the flow field and performance of a melt filter. The method includes: establishing a melt medium fluid model; determining the flow state of the melt within the filter; establishing a pressure drop model of the melt medium flowing through the filter element based on the flow state; establishing a melt medium filter fluid domain model based on the melt medium fluid model and the pressure drop model, and performing mesh generation; setting software parameters based on the melt medium filter fluid domain model; verifying the reliability of the numerical prediction method for the melt medium filter flow field; and performing numerical prediction of the melt filter flow field and performance based on the melt medium filter fluid domain model. In the method provided in this application, a melt medium filter fluid domain model and a porous media resistance model that can reasonably describe the flow and filtration behavior of high-viscosity melt media are established, achieving reliable prediction of the flow field within the high-pressure melt filter. Furthermore, by changing parameters such as melt medium fluid and pressure, filter chamber structure, and filter element distribution, the filter's effluent flow performance and pressure drop performance are optimized. By changing the filter element resistance coefficient, the filter flow field and performance are predicted under different usage conditions. The reliability of the prediction results significantly shortens the R&D cycle of high-viscosity melt filter products, improves the quality and operational reliability of high-viscosity melt filters, promotes the localization of high-end filters, and has good social and economic benefits. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments of the present invention or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the steps of the numerical prediction method for the flow field and performance of a melt filter provided in this application embodiment;

[0040] Figure 2 A flowchart illustrating the steps for establishing a melt medium fluid model is provided in this application embodiment;

[0041] Figure 3 A flowchart illustrating the specific steps for establishing a porous media resistance model for a filter element, as provided in this application embodiment;

[0042] Figure 4 A flowchart illustrating the filter fluid domain mesh generation steps provided in this application embodiment;

[0043] Figure 5 A schematic diagram of a melt filter flow field and performance numerical prediction server provided in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] Polyester melt filters are crucial equipment in the production processes of PET raw materials, high-speed spinning, plastic film, and blow molding. They are primarily used to remove impurities and unmelted particles from the melt to achieve continuous filtration and ensure product quality. The working environment and media characteristics of polyester melt filters can be summarized as "four highs": high temperature, high pressure, high viscosity, and high molecular weight polymers. Due to the harsh working environment, even with measures such as one operating and one standby filter and regular filter element cleaning, localized melt retention and deterioration can still occur in the filter. This poses a serious threat to the quality of polyester resin (PET) chips and downstream products.

[0046] Analysis suggests that, constrained by the high-temperature, high-pressure, and confined working environment, designers struggle to clearly grasp the filtration and flow state of high-viscosity polyester melt containing impurities within the filter's enclosed space. This leads to problems such as localized flow obstruction of the polyester melt medium within the filter. Research indicates that there is currently no literature specifically addressing flow field analysis and performance prediction for high-pressure melt filters. Only a few studies, such as those by Qi Liming et al., Liu Hesheng et al., Li Youbing et al., and Ma Xiangjun et al., have conducted preliminary explorations into the rheological behavior of polymer extrusion die melts. Furthermore, Liu Peng et al., Ba Peng et al., and Wu Shixian et al., respectively, used porous media models to simulate the resistance loss of fluid working mediums in applications such as hydraulic oil filters and air filter elements.

[0047] Based on the literature review, no numerical studies specifically addressing the flow characteristics and performance of high-viscosity melts within high-pressure melt filters have been found. To advance the localization of high-pressure melt filters, shorten the R&D cycle for high-viscosity melt filter products by domestic companies, and improve filter quality and operational reliability, there is an urgent need to establish numerical prediction methods for the flow and filtration characteristics of high-viscosity melt media within high-pressure melt filters.

[0048] To address the aforementioned problems, embodiments of the present invention provide a method for numerically predicting the flow field and performance of a melt filter, such as... Figure 1 The method shown includes steps S101 to S107.

[0049] S101, Establish the melt medium fluid model. Establish the melt medium fluid model, such as... Figure 2 The steps shown are S201 to S202.

[0050] S201 acquires the physical properties of the melt medium under service conditions, including viscosity as a function of shear rate within a specific shear rate range, non-Newtonian exponent, and consistency coefficient. The viscosity of the melt medium is tested within a specific shear rate range of 10⁻³ to 10², based on the non-Newtonian fluid power law model τ = k·γ. n By fitting experimental data on the viscosity and shear rate of the melt medium, the non-Newtonian index n and consistency coefficient k of the melt medium are obtained.

[0051] S202, Establish a melt medium fluid model based on the above parameters.

[0052] S102, Determine the flow state of the melt within the filter. This includes: obtaining the filter's design parameters and calculating the flow velocity and Reynolds number at different parts of the filter; determining the flow state of the melt within the filter based on the relevant parameters, flow velocity, and Reynolds number. When determining the flow state of the melt within the filter based on the fluid velocity and Reynolds number of each component, it is necessary to examine the melt flow state at each key characteristic section of the filter (inlet and outlet sections, filter housing section, and filter element section). Based on the geometric dimensions of each key section (such as inlet and outlet pipe diameters, filter housing diameter, and the unfolded area of ​​the filter element), the design flow rate, and experimentally measured melt density, melt viscosity, and other physical properties, calculate the flow velocity and Reynolds number of the fluid at different parts of the filter. Utilize the relationship between the flow rate of incompressible fluid and the flow area. Where Q represents the incompressible fluid flow rate, A represents the flow area, and ρ represents the melt density, the melt velocity at different cross-sections can be calculated. Using the formula... Where ρ represents the melt density and μ represents the viscosity coefficient, the Reynolds number of the melt flow is calculated at different locations. If the Reynolds number is much less than 2300, it can be determined that the melt is in a laminar flow state throughout the filter.

[0053] S103, establish a pressure drop model for the molten medium flowing through the filter element based on the flow state. The filter element pressure drop model includes: determining that the filter element is a metal fiber filter element, determining that the filter element is a single layer of porous media, determining the resistance model of the filter, determining the viscous resistance coefficient value under different precision filter elements, and determining the porosity of the porous media domain. When establishing the pressure drop model for the molten medium flowing through the metal fiber filter element, since the actual metal fiber filter element is composed of multiple complex structures, this application proposes the idea of ​​using a single layer of composite porous media fluid domain to simulate the overall pressure drop of this special structure. The total thickness of the porous media domain is the total radial thickness of the filter element, and the porosity of the porous media is adopted as the porosity of the metal fiber felt, which plays a decisive role in the overall filter element structure, with a specific value between 0.74 and 0.8. Establish the pressure drop model for the molten medium flowing through the filter element, as follows: Figure 3 The process specifically includes the following steps S301 to S305.

[0054] S301, the actual structure of the metal fiber filter element is determined to include: an outer protective tube, inner and outer two layers of metal wire mesh, multiple layers of 304SS metal fiber felt, and an inner perforated ring support structure.

[0055] S302, Determine the application method of the porous media model. To avoid the problem of significantly increasing simulation errors caused by repeated use of the porous media model, a composite porous media model is used for this special multilayer filter structure, that is, the multilayer overall structure of the filter is simplified into a single porous media domain;

[0056] S303, Determine the expression for the porous media resistance model. Since the melt flow within the filter is presumably laminar, the inertial loss coefficient in the porous media model is ignored, and only the viscous drag coefficient is calculated. At this point, the resistance model can be simplified to Darcy's formula. in, This represents the value of the viscous drag coefficient. This represents the velocity vector.

[0057] S304, Determine the viscous resistance coefficient values ​​of the porous media resistance model for filter elements of different precision. By experimentally measuring the flow rate and pressure difference of the melt medium under filter elements of different precision, and by fitting the experimental data, the viscous resistance coefficient of the porous medium model of filter elements of different precision was obtained.

[0058] S305, determine the porosity of the composite porous media flow domain. The total thickness of the porous media domain is the total radial thickness of the filter element, and the porosity of the porous media is the porosity of the metal fiber felt, which plays a decisive role in the filtration of the entire filter element structure, with a specific value in the range of 0.74 to 0.8.

[0059] S104, based on the melt medium fluid model and pressure drop model, establish the melt medium filter fluid domain model and perform mesh generation. For example... Figure 4 The mesh division shown includes the following steps S401 to S402.

[0060] S401 partially simplifies the fluid domain model of the melt media filter, retaining the components that generate flow resistance. During the simplification of the fluid domain model, components such as the filter element positioning plate structure are retained.

[0061] S402 involves dividing the simplified fluid domain model of the melt media filter into regions and discretizing each region using a mesh. The fluid domain model is divided into six regions: the inlet pipe region, the cylinder pipe region, the porous media region of the filter element, the internal flow channel region of the filter element, the mounting plate and top cover region, and the outlet pipe region. Each region is ensured to have a defined geometric space, and adjacent regions have clearly defined physical interfaces. Mesh discretization is then performed on each of these six regions. Due to its complex structure, the cylinder region can be discretized using tetrahedral meshes to improve the mesh quality of the complex structure. Other regions can be discretized using hexahedral structured meshes, which reduces the number of meshes in the model while maintaining mesh quality. The final total number of meshes in the calculated model needs to be determined based on the mesh independence assessment results.

[0062] S105 sets the software parameters based on the melt medium filter fluid domain model. These parameters include: melt medium physical property parameters, the radial viscous resistance coefficient of the filter element, the filter's inner wall characteristics, and convection pattern parameters. In CFX-Pre preprocessing, the melt medium fluid model, flow state model, and filter element resistance model are first defined. Specifically, parameters such as the melt medium's density, specific heat, and thermal conductivity need to be input.

[0063] By defining the expression function, the relationship between the shear rate and viscosity of the melt medium is input, namely the power-law model τ=k·γ. n .

[0064] A laminar flow model is used to describe the melt flow state. A porous media model is used to simulate the resistance of the media passing through the filter element, and the radial viscous resistance coefficient of the filter element at the corresponding filter element accuracy level is input.

[0065] Set calculation boundary conditions: Set the mass flow rate (or pressure) conditions at the inlet of the inlet pipe to be consistent with the actual operation of the melt filter, and set the melt pressure (or mass flow rate) conditions at the outlet of the outlet pipe.

[0066] Considering that the inner walls of all components of the filter have a very high degree of smoothness and the outer wall has good thermal insulation, all solid walls are designed with smooth surfaces to ensure thermal insulation and slippage-free flow conditions.

[0067] Set the flow interface: the inlet pipe area and the cylinder area are set as fluid-fluid interface, the cylinder and the filter element porous media area are set as fluid-porous media interface, the filter element porous media area and the filter element inner flow channel area are set as porous media-fluid interface, the filter element inner flow channel area and the mounting plate upper cover area are set as fluid-fluid interface, and the mounting plate upper cover area and the outlet pipe are set as fluid-fluid interface.

[0068] Solution settings: Select high precision for the convection scheme, select automatic time step, set an appropriate number of solution steps, and select an RSM value of 10⁻⁶ to 10⁻⁸ for the residual convergence criterion.

[0069] S106 verifies the reliability of the numerical prediction method for the flow field of the melt medium filter. This includes: determining whether the fluid velocity in different fluid domains of the filter conforms to the principles of fluid mechanics; determining whether the pressure distribution of the melt medium flowing through different parts corresponds to the velocity distribution; calculating the total pressure loss from the filter inlet to the outlet and comparing it with the actual operating pressure loss. First, the velocity vector information in different fluid domains within the filter is viewed in CFX-Post. It is confirmed that the fluid flow direction is as follows: the melt flows into the lower end cover from the inlet pipe, flows upward through the filter element positioning plate and then radially through the porous media domain of the filter element into the inner flow channel of the filter element. Afterward, the melt flows along the inner flow channel of the filter element through the mounting plate channel and enters the upper end cover area, finally flowing out from the outlet pipe. Simultaneously, determine whether the velocity of the fluid at different locations conforms to the principles of fluid mechanics; secondly, after satisfying the above fluid velocity vector distribution, check whether the pressure distribution of the melt flowing through different locations corresponds to the velocity distribution; finally, calculate the total pressure loss from the filter inlet to the outlet, and compare it with the actual operating pressure loss of the melt filter under the same filtration accuracy level to verify the predicted results of the filter pressure loss performance.

[0070] S107, Numerical prediction of the melt filter based on the melt medium filter fluid domain model. This includes: testing by changing various parameters in the melt filter; and testing by changing the distribution of filter elements in the filter.

[0071] By varying the incoming melt flow rate, the impact of flow rate on filter pressure drop performance is explored, enabling prediction of filter flow field and performance under different melt mass flow rates. With a fixed filtration area, by changing factors such as filter element placement and filter chamber structure, the flow field and performance of filters with different filter element arrangements and filter chamber structures can be predicted. Simultaneously, by comparing system pressure drop and melt shear rates at key locations, the filter effluent performance and pressure drop performance can be optimized. With fixed operating parameters and filter structure, by changing the filter element resistance coefficient, the process of filtration performance degradation after filter element contamination by impurity particles can be simulated, thereby enabling prediction of the filter's flow field and performance under different operating conditions.

[0072] The numerical prediction method for the flow field and performance of high-viscosity melt filters provided in this application considers the physical properties and rheological characteristics of high-viscosity melt media under actual service conditions. It establishes a fluid medium model and a porous medium resistance model that can reasonably describe the flow and filtration behavior of high-viscosity melt media, achieving reliable prediction of the flow field within the high-pressure melt filter. Based on this, by changing parameters such as melt flow rate and pressure, filter chamber structure, and filter element distribution, the filter's effluent flow performance and pressure drop performance are optimized. By changing the filter element resistance coefficient, the flow field and performance of the filter under different operating conditions are predicted. This invention clearly demonstrates the three-dimensional flow of the melt medium within the confined space of a high-temperature, high-pressure melt filter, while significantly shortening the R&D cycle for high-viscosity melt filter products, improving the quality and operational reliability of high-viscosity melt filters, and promoting the localization of high-end filters, thus possessing significant socio-economic benefits.

[0073] This invention provides a numerical prediction server 500 for the flow field and performance of a melt filter, such as... Figure 5 The server shown includes a memory 501 and a processor 502; the memory 501 is used to store computer-executable instructions; the processor 502 is used to execute the computer-executable instructions to implement the melt filter flow field and performance numerical prediction method and any one thereof.

[0074] This invention provides a computer-readable storage medium storing executable instructions, which, when executed by a computer, enable the implementation of a method for predicting melt filter values ​​and any one thereof.

[0075] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0076] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0077] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0078] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0079] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for numerical prediction of melt filter flow field and performance, characterized in that, The method comprises the following steps: establishing a melt medium fluid model; judging the flow state of the melt in the filter; According to the flow state, a pressure drop model of the melt medium flowing through the filter element is established, including: determining the actual structure of the metal fiber filter element, including an outer protective tube, inner and outer wire meshes, a plurality of layers of 304SS metal fiber felt, and a circular ring support structure with holes on the inner side; determining the use form of the porous medium resistance model as a composite porous medium model, that is, simplifying the multi-layer whole structure of the filter element into a porous medium domain; determining the expression of the porous medium resistance model: wherein, viscosity resistance coefficient value, velocity vector; determining the viscosity resistance coefficient value of the porous medium resistance model under different precision filter elements ; measuring the flow rate and pressure difference data of the melt medium under different precision filter elements through experiments, fitting the experimental data to obtain the viscosity resistance coefficient of the porous medium resistance model of different precision filter elements; determining that the porosity of the flow domain of the porous medium resistance model is in the range of 0.74-0.8; establishing a melt medium filter fluid domain model according to the melt medium fluid model and the filter element pressure drop model, and performing mesh division, which comprises: dividing the fluid domain model, including dividing into an inlet pipe domain, a cylinder pipe domain, a filter element porous medium flow domain, a filter element inner flow channel flow domain, an installation plate and upper end cover flow domain, and an outlet pipe flow domain, each flow domain has a clear geometric space, and there is a clear physical interface between two adjacent flow domains; performing mesh discretization on the above six regions respectively; the cylinder region is discretized by using tetrahedron mesh to improve the mesh quality of the complex structure; other regions are discretized by using hexahedron structured mesh to reduce the number of meshes of the model while ensuring the mesh quality; setting software parameters according to the melt medium filter fluid domain model; verifying the reliability of the melt medium filter flow field numerical prediction method; performing melt filter flow field and performance numerical prediction according to the melt medium filter fluid domain model.

2. The method of claim 1, wherein, The method for establishing a melt medium fluid model comprises the following steps: obtaining the physical property data of the melt medium under the service condition, the viscosity change of the melt medium with the shear rate in a specific shear rate range, the non-Newtonian index, and the consistency coefficient; establishing a melt medium fluid model according to the above parameters.

3. The method of claim 1, wherein, The method for judging the flow state of the melt in the filter comprises the following steps: obtaining the design parameters of the filter and calculating the flow velocity and Reynolds number of different parts of the filter; judging the flow state of the melt in the filter according to the design parameters, the flow velocity, and the Reynolds number.

4. The method of claim 1, wherein, The filter element pressure drop model comprises the following steps: determining that the filter element of the filter is a metal fiber filter element, determining that the filter element is a porous medium domain, determining the resistance model of the filter, determining the viscous resistance coefficient value under different precision filter elements, and determining the porosity of the porous medium domain.

5. The method of claim 1, wherein, The method for performing mesh division comprises the following steps: simplifying the melt medium filter fluid domain model, and retaining the part that produces flow resistance; dividing the simplified melt medium filter fluid domain model into regions, and performing mesh discretization on each region.

6. The method of claim 1, wherein, The software parameters comprise the physical property parameters of the melt medium, the viscous resistance coefficient of the filter element along the radial direction, the inner wall surface characteristics of the filter, and the convection format parameters.

7. The method of claim 1, wherein, The method for verifying the reliability of the melt medium filter flow field numerical prediction method comprises the following steps: judging whether the fluid velocity in different fluid domains of the filter conforms to the principle of fluid mechanics; determining whether the pressure distribution of the melt flowing through different parts corresponds to the velocity distribution; calculating the total pressure loss from the inlet to the outlet of the filter, and comparing it with the actual operating pressure loss.

8. The method of claim 1, wherein, The method for performing melt filter flow field and performance numerical prediction according to the melt medium filter fluid domain model comprises the following steps: changing each parameter in the melt filter for testing; changing the distribution of the filter element in the filter for testing.

9. A melt filter flow field and performance numerical prediction server, characterized by, The device comprises a memory and a processor; the memory is used for storing computer executable instructions; The processor is configured to execute the computer-executable instructions to implement the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable instructions, and the computer executes the executable instructions to implement the method of any one of claims 1-8.

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