Optimization method and device of lithium battery slurry mixing equipment based on fluid mechanics simulation, computer equipment and storage medium

Abstract

The invention provides an optimization method and device of lithium battery slurry mixing equipment based on fluid mechanics simulation, computer equipment and a storage medium. The optimization method of the lithium battery slurry mixing equipment based on fluid mechanics simulation comprises the following steps: establishing a plurality of geometric models of the lithium battery slurry mixing equipment; performing model simplification processing on each geometric model, and extracting a fluid region needing to be calculated; dividing finite element grids for each geometric model, and encrypting a local area of a stirring paddle of each geometric model to obtain a grid file of each geometric model; determining a basic control equation of a calculation flow field for lithium battery slurry mixing, and performing volume integration processing on the basic control equation; obtaining material attributes corresponding to the lithium battery slurry added into the geometric model, and determining a medium flow model according to a Reynolds coefficient calculation equation; according to the optimization method, a large amount of research and development time and research and development cost can be saved, and meanwhile the test effect can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of simulation design of lithium battery slurry mixing equipment, and in particular to an optimization method, device, computer equipment and storage medium for lithium battery slurry mixing equipment based on fluid mechanics simulation. Background Art

[0002] With the rapid development of the global new energy electric vehicle market, lithium batteries, as one of its core power sources, have achieved great success and wide application in the field of new energy. Among them, in the production and manufacturing process of lithium batteries, lithium battery slurry stirring is one of the most critical process links. Lithium battery slurry is usually a mixture of active materials (such as positive electrode materials, negative electrode materials), conductive agents, binders and solvents. These raw materials are fully mixed by stirring to ensure the performance and stability of the battery. Therefore, the research and development of lithium battery slurry stirring and mixing equipment is particularly important.

[0003] Previous research relied heavily on test results and had many drawbacks. Technical personnel were unable to fully understand the specific conditions of the flow characteristics of the flow field in the mixing system, making it difficult to provide a reliable reference for engineering practice, making the design process of lithium battery slurry stirring and mixing equipment more complicated and requiring repeated tests. As a result, the research and development of lithium battery slurry stirring and mixing equipment required a large amount of research and development costs and a long research and development time, and the test results of the material stirring and mixing equipment were poor.

[0004] Based on this, this application proposes a simulation optimization method for lithium battery slurry mixing equipment based on computational fluid dynamics. Summary of the invention

[0005] The purpose of this application is to overcome the shortcomings of the prior art and provide an optimization method, device, computer equipment and storage medium for lithium battery slurry mixing equipment based on fluid mechanics simulation with low R&D cost, short R&D time and good test results.

[0006] The purpose of this application is achieved through the following technical solutions:

[0007] A method for optimizing a lithium battery slurry mixing device based on fluid mechanics simulation, comprising:

[0008] Establish geometric models of several lithium battery slurry mixing equipment;

[0009] Simplifying each of the geometric models and extracting the fluid region that needs to be calculated;

[0010] Dividing each of the geometric models into finite element grids, and performing encryption processing on a local area of ​​the stirring blade of each of the geometric models to obtain a grid file of each of the geometric models;

[0011] Determine the basic control equation of the flow field of the lithium battery slurry mixing, and perform volume integration processing on the basic control equation;

[0012] Obtaining material properties corresponding to the lithium battery slurry added to the geometric model, and determining a medium flow model according to a calculation equation for the Reynolds coefficient; wherein the material properties include density and viscosity;

[0013] Obtaining physical parameters and boundary conditions of the initial conditions, and performing steady-state flow field solving on the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results, wherein the physical parameters include a preset rotation speed of a stirring paddle in the stirring tank of the geometric model;

[0014] Performing residual judgment on the solution results corresponding to each of the geometric models respectively; if the residual converges, saving the solution results;

[0015] Establishing a component transport equation, adding a tracer, and then performing transient solutions to the computational fluid model in the stirred tank of each of the geometric models to obtain fluid parameters of the stirred tanks of the multiple geometric models, and using the results of the steady-state solutions of each geometric model as the initial values ​​of the component transport equation;

[0016] Post-processing the fluid parameters of the stirring tank of each geometric model to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model changing with time and the flow field velocity cloud map data in the stirring tank;

[0017] The stirring and mixing data of the stirring tanks of the plurality of geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment.

[0018] In one embodiment, the basic control equations include a mass conservation equation and a momentum conservation equation; determining the basic control equations for the calculation flow field of the lithium battery slurry mixing, and performing volume integration processing on the basic control equations includes:

[0019] Determine the basic governing equations for the computational flow field of lithium battery slurry mixing;

[0020] Volume integration processing is performed on the mass conservation equation and the momentum conservation equation respectively.

[0021] In one embodiment, the mass conservation equation is: The momentum conservation equation is:

[0022] Where ρ is the fluid density, t is the time, the local velocity components of the velocity V at any point in the fluid field are u, v, and w respectively; the x, y, and z directions are the spatial directions and positions respectively; p is the pressure in the fluid microelement; τ ij is the viscous stress acting on the surface of the microelement; ρg i is the gravitational body force in the i direction, F i is the external body force in the i direction.

[0023] In one embodiment, the step of extracting the fluid region to be calculated is specifically:

[0024] The fluid area that needs to be calculated is extracted through volume extraction and Boolean operations.

[0025] In one embodiment, the step of dividing each geometric model into a finite element mesh is specifically as follows: dividing each geometric model into a finite element mesh using an unstructured tetrahedral mesh method, and performing encryption processing on the region of the stirring paddle corresponding to each geometric model.

[0026] In one embodiment, the viscosity variation of the lithium battery slurry conforms to that of a pseudoplastic fluid;

[0027] A power-law fluid model is used to simulate non-Newtonian fluids, where the power-law fluid satisfies τ = Kγ. n , where τ is the shear stress, K is the consistency coefficient, γ is the shear strain rate, n is the power law exponent, and n<1.

[0028] In one embodiment, the calculation equation of the Reynolds coefficient of the non-Newtonian fluid is:

[0029] Where: Re is a dimensionless number; V is the average flow velocity, m / s; D is the diameter of the impeller, mm; ρ is the fluid density, kg / m 3 .

[0030] In one embodiment, the component transport equation is: Where ρ is the density of the fluid, μ is the velocity of the fluid, is the mass fraction of the tracer, Γ is the diffusion coefficient of the tracer in the fluid, in is the diffusion rate of the tracer, is the source term. The numerical simulation process of lithium battery slurry mixing does not involve chemical reactions. The value of is 0.

[0031] An optimization device for lithium battery slurry mixing equipment based on fluid mechanics simulation, comprising:

[0032] The geometric model building module is used to build geometric models of several lithium battery slurry mixing equipment;

[0033] A model simplification and extraction module, used to perform model simplification processing on each of the geometric models and extract the fluid area that needs to be calculated;

[0034] A partitioning module, used to partition each of the geometric models into finite element meshes, and to perform encryption processing on a local area of ​​the stirring paddle of each of the geometric models to obtain a mesh file of each of the geometric models;

[0035] An integral processing module, used to determine the basic control equation of the flow field of the lithium battery slurry mixing, and perform volume integral processing on the basic control equation;

[0036] Determine the flow pattern module, which is used to obtain the material properties corresponding to the lithium battery slurry added to the geometric model, and determine the medium flow model according to the calculation equation of the Reynolds coefficient;

[0037] A flow field solving module, used to obtain physical parameters and boundary conditions of the initial conditions, and to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results; wherein the physical parameters include a preset rotation speed of a stirring paddle in the stirring tank of the geometric model;

[0038] A residual judgment module is used to perform residual judgment on the solution results corresponding to each geometric model; if the residual converges, the solution result is saved;

[0039] An equation building module is used to build a component transport equation, add a tracer, and then perform transient solutions to the computational fluid model in the stirred tank of each geometric model to obtain fluid parameters of the stirred tanks of multiple geometric models, and use the results of the steady-state solution of each geometric model as the initial value of the component transport equation;

[0040] A parameter processing module, used for post-processing the fluid parameters of the stirring tank of each geometric model, to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model varying with time and the flow field velocity cloud map data in the stirring tank;

[0041] The comparison output module is used to compare the stirring and mixing data of the stirring tanks of the multiple geometric models, and use the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment.

[0042] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method described in any one of the above embodiments when executing the computer program.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0044] Compared with the prior art, this application has at least the following advantages:

[0045] 1. The above-mentioned optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation first establishes several geometric models of lithium battery slurry mixing equipment; then simplifies each geometric model and extracts the fluid area that needs to be calculated, that is, extracts the slurry area of ​​the stirring tank of the geometric model; then divides each geometric model into finite element grids to obtain a grid file for each geometric model; then determines the basic control equations of the computational flow field of lithium battery slurry mixing, and performs volume integration on the basic control equations; then determines the basic control equations of the computational flow field of lithium battery slurry mixing, and performs volume integration on the basic control equations; then obtains the material properties corresponding to the lithium battery slurry added to the geometric model, and determines the medium flow model according to the calculation equation of the Reynolds coefficient; then obtains the physical parameters and boundary conditions of the initial conditions, and uses the solver to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model to obtain the corresponding solution results; then Then, the residual judgment is performed on the solution results corresponding to each geometric model respectively; if the residual converges, the solution result is saved; then the component transport equation is established, and the tracer is added, and then the computational fluid model in the stirring tank of each geometric model is transiently solved respectively to obtain the fluid parameters of the stirring tanks of multiple geometric models, and the results of the steady-state solution of each geometric model are used as the initial value of the component transport equation; then the fluid parameters of the stirring tank of each geometric model are post-processed to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model that changes with time and the flow field velocity cloud map data in the stirring tank; finally, the stirring and mixing data of the stirring tanks of multiple geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment, so that the engineering problem of structural optimization of the lithium battery mixing equipment is realized through computational fluid dynamics analysis, making the design method of the lithium battery slurry mixing equipment simple and convenient;

[0046] 2. The above-mentioned optimization method solves the problem that traditional optimization design needs to spend a lot of time and money to judge the stirring effect of lithium battery mixing equipment through experiments, so that the research and development cost of the optimization method is relatively low. The flow field simulation analysis in the stirring tank not only greatly shortens the time required for structural optimization of lithium battery mixing equipment, that is, shortens the research and development time of lithium battery mixing equipment, but also solves the problem of poor test effect of traditional lithium battery mixing equipment. The lithium battery mixing equipment after optimization better meets the actual production needs of battery mixing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 This is a diagram of the mixing equipment for raw slurry type lithium battery;

[0049] Figure 2 It is a flow chart of an optimization method of a lithium battery slurry mixing device based on fluid mechanics simulation according to an embodiment;

[0050] Figure 3 This is a schematic diagram of the mesh in the fluid calculation area of ​​the original propeller geometry model;

[0051] Figure 4 The flow field velocity cloud map and trace map of the original propeller type;

[0052] Figure 5 It is the flow field velocity cloud map and trajectory map of the optimal propeller type;

[0053] Figure 6 A flow chart of a method for optimizing a lithium battery slurry mixing device based on fluid mechanics simulation according to another embodiment;

[0054] Figure 7 This is a schematic diagram of the tracer feeding location and monitoring points;

[0055] Figure 8a This is the curve of the concentration variation of the monitoring point over time for the original paddle type;

[0056] Figure 8b This is the curve of the concentration variation of the monitoring point with time for the optimal paddle type;

[0057] Fig. 9 This is a diagram of the mixing equipment for lithium battery slurry of the optimal slurry type;

[0058] Fig.10 An internal structural diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present application are given in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thoroughly and comprehensively understood.

[0060] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0062] The present application provides an optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation, including some or all of the following steps: establishing geometric models of several lithium battery slurry mixing equipment; simplifying each of the geometric models and extracting the fluid area that needs to be calculated; dividing each of the geometric models into finite element grids, and encrypting the local area of ​​the stirring paddle of each of the geometric models to obtain a grid file for each of the geometric models; determining the basic control equations of the computational flow field of lithium battery slurry mixing, and performing volume integration processing on the basic control equations; obtaining the material properties corresponding to the lithium battery slurry added to the geometric model, and determining the medium flow model according to the calculation equation of the Reynolds coefficient; wherein the material properties include density and viscosity; obtaining the physical parameters and boundary conditions of the initial conditions, and solving the steady-state flow field of the computational fluid model of the stirring tank of each of the geometric models through a solver to obtain the corresponding The solution result is obtained; wherein the physical parameter includes the preset rotation speed of the stirring paddle in the stirring tank of the geometric model; the residual judgment is performed on the solution result corresponding to each of the geometric models; if the residual converges, the solution result is saved; a component transport equation is established, and a tracer is added, and then the computational fluid model in the stirring tank of each of the geometric models is transiently solved to obtain the fluid parameters of the stirring tanks of multiple geometric models, and the result of the steady-state solution of each geometric model is used as the initial value of the component transport equation; the fluid parameters of the stirring tank of each geometric model are post-processed to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model that changes with time and the flow field velocity cloud map data in the stirring tank; the stirring and mixing data of the stirring tanks of multiple geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment.

[0063] The above-mentioned optimization method of lithium battery slurry mixing equipment based on fluid mechanics simulation first establishes several geometric models of lithium battery slurry mixing equipment; then simplifies each geometric model and extracts the fluid area to be calculated, that is, extracts the slurry area of ​​the stirring tank of the geometric model; then divides each geometric model into finite element grids to obtain a grid file of each geometric model; then determines the basic control equation of the computational flow field of lithium battery slurry mixing, and performs volume integration on the basic control equation; then obtains the material properties corresponding to the lithium battery slurry added to the geometric model, and determines the medium flow model according to the calculation equation of the Reynolds coefficient; then obtains the physical parameters and boundary conditions of the initial conditions, and uses the solver to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model to obtain the corresponding solution results; then performs residual judgment on the solution results corresponding to each geometric model; if the residual converges, the solution result is saved; then establishes the component transport equation, adds tracers, and then performs transient solution on the computational fluid model in the stirring tank of each geometric model to obtain the fluid parameters of the stirring tanks of the multiple geometric models, and each The result of the steady-state solution of a geometric model is used as the initial value of the component transport equation; then the fluid parameters of the stirring tank of each geometric model are post-processed to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model changing with time and the flow field velocity cloud map data in the stirring tank; finally, the stirring and mixing data of the stirring tanks of multiple geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment, so that the engineering problem of structural optimization of the lithium battery mixing equipment is realized through computational fluid dynamics analysis, making the design method of the lithium battery slurry mixing equipment simple and convenient; the above-mentioned optimization method solves the problem that the traditional optimization design needs to spend a lot of time and money to judge the stirring effect of the lithium battery mixing equipment through experiments, so that the research and development cost of the optimization method is relatively low, and the flow field simulation analysis in the stirring tank not only greatly shortens the time required for the structural optimization of the lithium battery mixing equipment, that is, shortens the research and development time of the lithium battery mixing equipment, but also solves the problem of poor test results of the traditional lithium battery mixing equipment, so that the optimized lithium battery mixing equipment better meets the actual production needs of the battery mixing equipment.

[0064] In one embodiment, the basic control equations include a mass conservation equation and a momentum conservation equation; determining the basic control equations for the calculation flow field of the lithium battery slurry mixing, and performing volume integration processing on the basic control equations includes:

[0065] Determine the basic governing equations for the computational flow field of lithium battery slurry mixing;

[0066] Volume integration processing is performed on the mass conservation equation and the momentum conservation equation respectively.

[0067] In one embodiment, the mass conservation equation is: The momentum conservation equation is:

[0068] Where ρ is the fluid density, t is the time, the local velocity components of the velocity V at any point in the fluid field are u, v, and w respectively; the x, y, and z directions are the spatial directions and positions respectively; p is the pressure in the fluid microelement; τ ij is the viscous stress acting on the surface of the microelement; ρg i is the gravitational body force in the i direction, F i is the external volume force in the i direction. In one embodiment, the step of extracting the fluid region to be calculated is specifically:

[0069] The fluid area that needs to be calculated is extracted through volume extraction and Boolean operations.

[0070] In one embodiment, the step of dividing each geometric model into a finite element mesh is specifically as follows: dividing each geometric model into a finite element mesh using an unstructured tetrahedral mesh method, and performing encryption processing on the region of the stirring paddle corresponding to each geometric model.

[0071] In one embodiment, the viscosity variation of the lithium battery slurry conforms to that of a pseudoplastic fluid;

[0072] A power-law fluid model is used to simulate non-Newtonian fluids, where the power-law fluid satisfies τ = Kγ. n , where τ is the shear stress, K is the consistency coefficient, γ is the shear strain rate, n is the power law exponent, and n<1.

[0073] In one embodiment, the calculation equation of the Reynolds coefficient of the non-Newtonian fluid is:

[0074] Where: Re is a dimensionless number; V is the average flow velocity, m / s; D is the diameter of the impeller, mm; ρ is the fluid density, kg / m 3 .

[0075] In one embodiment, the component transport equation is: Where ρ is the density of the fluid, μ is the velocity of the fluid, is the mass fraction of the tracer, Γ is the diffusion coefficient of the tracer in the fluid, in is the diffusion rate of the tracer, is the source term. The numerical simulation process of lithium battery slurry mixing does not involve chemical reactions. The value of is 0.

[0076] The present application also provides an optimization device for a lithium battery slurry mixing device based on fluid mechanics simulation, comprising:

[0077] The geometric model building module is used to build geometric models of several lithium battery slurry mixing equipment;

[0078] A model simplification and extraction module, used to perform model simplification processing on each of the geometric models and extract the fluid area that needs to be calculated;

[0079] A partitioning module, used to partition each of the geometric models into finite element meshes, and to perform encryption processing on a local area of ​​the stirring paddle of each of the geometric models to obtain a mesh file of each of the geometric models;

[0080] An integral processing module, used to determine the basic control equation of the flow field of the lithium battery slurry mixing, and perform volume integral processing on the basic control equation;

[0081] A flow field solving module, used to obtain physical parameters and boundary conditions of the initial conditions, and to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results; wherein the physical parameters include a preset rotation speed of a stirring paddle in the stirring tank of the geometric model;

[0082] A residual judgment module is used to perform residual judgment on the solution results corresponding to each geometric model; if the residual converges, the solution result is saved;

[0083] An equation building module is used to build a component transport equation, add a tracer, and then perform transient solutions to the computational fluid model in the stirred tank of each geometric model to obtain fluid parameters of the stirred tanks of multiple geometric models, and use the results of the steady-state solution of each geometric model as the initial value of the component transport equation;

[0084] A parameter processing module, used for post-processing the fluid parameters of the stirring tank of each geometric model, to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model varying with time and the flow field velocity cloud map data in the stirring tank;

[0085] The comparison output module is used to compare the stirring and mixing data of the stirring tanks of the multiple geometric models, and use the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment.

[0086] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method described in any of the above embodiments when executing the computer program.

[0087] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0088] Taking the structural optimization of a type of lithium battery slurry mixing equipment as an example, the original slurry type lithium battery slurry mixing equipment is shown in the figure below. Figure 1 To better understand the technical solution and beneficial effects of the present application, the present application is further described in detail below in conjunction with specific embodiments:

[0089] like Figure 2 As shown, an optimization method for a lithium battery slurry mixing device based on fluid mechanics simulation in one embodiment includes part or all of the following steps:

[0090] S101, establishing geometric models of several lithium battery slurry mixing devices.

[0091] In this embodiment, geometric models of several lithium battery slurry mixing devices are established, that is, geometric models of at least two lithium battery slurry mixing devices are established. It should be noted that the limitation of "at least" mentioned in the present disclosure includes the number itself, and the same applies below. For example, "at least two" includes the case where the number is two.

[0092] S103, performing model simplification processing on each of the geometric models, and extracting the fluid region that needs to be calculated.

[0093] In this embodiment, each of the geometric models is simplified and the fluid region to be calculated is extracted. Specifically, each of the geometric models is imported into the SpaceClaim function module of ANSYS (computer-aided engineering software, also known as CAE software) for model simplification and the fluid region to be calculated is extracted. For example, if the number of geometric models is two, then the number of corresponding fluid regions to be calculated in sequence is two.

[0094] S105, dividing each of the geometric models into finite element grids, and performing encryption processing on the local area of ​​the stirring blade of each of the geometric models to obtain a grid file of each of the geometric models.

[0095] In one of the embodiments, the step of dividing each geometric model into a finite element mesh is specifically as follows: using an unstructured tetrahedron mesh method to divide each geometric model into a finite element mesh, and using an unstructured tetrahedron mesh method to perform encryption processing on the region of the stirring paddle corresponding to each geometric model. One of the geometric models corresponds to a puree-type geometric model. In this embodiment, the finite element mesh is divided into each geometric model by the unstructured tetrahedron method of the Mesh module of ANSYS, and the local region of the stirring paddle of each geometric model is encrypted to obtain a mesh file corresponding to each geometric model, while ensuring the mesh quality and calculation accuracy.

[0096] Furthermore, the local area of ​​the impeller of each geometric model is encrypted to obtain n computational domain grids with different numbers of grids, and the grid independence verification is performed to select the computational domain grid with the optimal number of grids to better ensure the grid quality and computing time cost. Where n is an integer. For example, if n is 4 and the number of grids is 762536, then the computational domain grid with the number of grids being 762536 is selected. The fluid computational area grid in the geometric model of the original impeller is as follows: Figure 3 shown.

[0097] S107, determining a basic control equation for calculating a flow field of lithium battery slurry mixing, and performing volume integration processing on the basic control equation.

[0098] In this embodiment, the basic control equations of the flow field for the calculation of the lithium battery slurry mixing are determined, and the basic control equations are volume-integrated. In one embodiment, the basic control equations include the mass conservation equation and the momentum conservation equation; the steps of determining the basic control equations of the flow field for the calculation of the lithium battery slurry mixing and volume-integrating the basic control equations include: first determining the basic control equations of the flow field for the calculation of the lithium battery slurry mixing; and secondly volume-integrating the mass conservation equation and the momentum conservation equation, respectively.

[0099] In one embodiment, the mass conservation equation is: The momentum conservation equation is:

[0100] Where ρ is the fluid density, t is the time, the local velocity components of the velocity V at any point in the fluid field are u, v, and w respectively; the x, y, and z directions are the spatial directions and positions respectively; p is the pressure in the fluid microelement; τ ij is the viscous stress acting on the surface of the microelement; ρg i is the gravitational body force in the i direction, F i is the external body force in the i direction.

[0101] S109, obtaining material properties corresponding to the lithium battery slurry added to the geometric model, and determining a medium flow model according to a calculation equation of the Reynolds coefficient.

[0102] In this embodiment, the material properties corresponding to the lithium battery slurry added to the geometric model are obtained, and the medium flow model is determined according to the calculation equation of the Reynolds coefficient, wherein the material properties include density and viscosity; specifically, the material properties corresponding to the lithium battery slurry can be manually pre-set according to actual conditions to obtain the material properties corresponding to the lithium battery slurry added to the geometric model. For example, the density of the lithium battery slurry is set to 1600kg / m 3 , the viscosity is set to a non-Newtonian fluid. Furthermore, when setting the data, the minimum viscosity is set to 10000 cP, the maximum viscosity is set to 100000 cP, and the viscosity of the lithium battery varies between the maximum viscosity and the minimum viscosity.

[0103] Further, according to the formula τ=Kγ n Calculation is performed, where τ is shear stress, K is consistency coefficient, γ is shear strain rate, and n is power law index; wherein two sets of τ and γ data are obtained by measurement, that is, two sets of data (τ1, γ1) and (τ2, γ2) are obtained by measurement, and then data K and n are calculated by formula. In this embodiment, for lithium battery slurry, the calculated consistency coefficient K is 5 and the power law index n is 0.5.

[0104] In this embodiment, the consistency coefficient K is 5, and the power law index n is 0.5. When n<1, it indicates a pseudoplastic fluid with shear thinning; when n=1, it indicates a Newtonian fluid; when n>1, it indicates an expansive fluid with shear thickening.

[0105] In one embodiment, the viscosity change of the lithium battery slurry conforms to the pseudoplastic fluid; a power law fluid model is used to simulate the non-Newtonian fluid, and the power law fluid satisfies τ=Kγ n , where τ is the shear stress, K is the consistency coefficient, γ is the shear strain rate, n is the power law exponent, and n<1.

[0106] In one embodiment, the calculation equation of the Reynolds coefficient of the non-Newtonian fluid is:

[0107] Where: Re is a dimensionless number; if Re≤2000, it is a laminar flow; if Re>2000, it is a turbulent flow; V is the average flow velocity, m / s; D is the diameter of the impeller, mm; ρ is the fluid density, kg / m 3 In this embodiment, D = 0.69m; ρ = 1600kg / m 3; V = 0.73m / s; K is 5; n is 0.5; Substituting into the formula, the Re is calculated to be 419.42, that is, Re is less than 2000. Since Re is less than 2000, the viscosity model is set to a laminar flow model.

[0108] S111, obtaining physical parameters and boundary conditions of the initial conditions, and solving the steady-state flow field of the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results.

[0109] In this embodiment, the physical parameters and boundary conditions of the initial conditions are obtained, and the computational fluid model of the stirring tank of each geometric model is solved by the solver to solve the steady-state flow field, and the corresponding solution results are obtained; wherein, the physical parameters include the preset speed of the stirring paddle in the stirring tank of the geometric model. In one embodiment, the preset speed of the stirring paddle is 35rpm~50rpm. In this embodiment, the preset speed of the stirring paddle is 40rpm. The stirring paddle rotation area is set as a multiple reference system, that is, the stirring paddle rotation area is processed by the multiple reference system method. It should be noted that the boundary conditions include setting the blade area as a dynamic area, other areas as static areas, and the data exchange of the dynamic and static areas is completed by the exchange surface interface, the wall of the stirring tank is set as a static boundary, the blade wall is in the dynamic area, the fluid relative to the dynamic area is static, the boundary is set to a rotating wall, and the speed is set to 0. The stirring shaft wall is in the static area, and the liquid relative to the surrounding static liquid is moving. The boundary is set to a rotating wall, and the speed is consistent with the stirring paddle speed.

[0110] S113, performing residual judgment on the solution results corresponding to each of the geometric models; if the residual converges, saving the solution results.

[0111] In this embodiment, the residual judgment is performed on the solution results corresponding to each geometric model; if the residual converges, the solution result is saved so as to be used as the initial condition for the concentration field calculation later; the flow field velocity cloud map and trajectory map of the original propeller type are as follows Figure 4 As shown in the figure, the velocity cloud diagram and trajectory diagram of the optimal propeller type are as follows Figure 5 As shown; on the contrary, if the residual does not converge, the boundary conditions are reset, such as Figure 6 shown.

[0112] S115, establishing a component transport equation, adding a tracer, and then performing transient solution on the computational fluid model in the stirred tank of each geometric model to obtain fluid parameters of the stirred tanks of the multiple geometric models.

[0113] Figure 7The figure shows a schematic diagram of the tracer feeding position and monitoring point. In this embodiment, the component transport equation is established, and the tracer is added, and then the computational fluid model in the stirred tank of each geometric model is transiently solved to obtain the fluid parameters of the stirred tanks of multiple geometric models and realize the concentration field calculation. The result of the steady-state solution of each geometric model is used as the initial value of the component transport equation.

[0114] In one embodiment, the material parameters of the tracer are the same as the material parameters of the lithium battery slurry. In this embodiment, the material parameter characteristics of the tracer are set to be the same as the material parameter characteristics of the lithium battery slurry. In the Patch module of ANSYSFLUENT, the concentration of the feeding area is set to 1, the concentration of other areas is set to 0, and a concentration monitoring point is established, such as Figure 7 As shown, the tracer addition area is confirmed. Further, the computational fluid model in the stirred tank is solved transiently, only the component transport equation is enabled and the flow equation is disabled.

[0115] In one embodiment, the component transport equation is: Where ρ is the density of the fluid, μ is the velocity of the fluid, is the mass fraction of the tracer, Γ is the diffusion coefficient of the tracer in the fluid, in is the diffusion rate of the tracer, is the source term. The numerical simulation process of lithium battery slurry mixing does not involve chemical reactions. The value of is 0.

[0116] S117, post-processing the fluid parameters of the stirring tank of each geometric model to obtain stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model changing with time and flow field velocity cloud map data in the stirring tank.

[0117] In this embodiment, the fluid parameters of the stirring tank of each geometric model are post-processed to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model varying with time.

[0118] Furthermore, the step of post-processing the fluid parameters of the stirring tank of each geometric model includes: firstly establishing the data of multiple monitoring points of each geometric model to obtain a curve of tracer concentration changing with time, and obtaining multiple curves of tracer concentration changing with time; wherein, the curve of concentration changing with time of the monitoring point of the original slurry type is as follows: Figure 8a As shown in the figure, the concentration curve of the monitoring point of the optimal pulp type changes with time is as follows Figure 8b shown.

[0119] S119, comparing the stirring and mixing data of the stirring tanks of the plurality of geometric models, and using the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment.

[0120] In this embodiment, the mixing data of the stirring tanks of the plurality of geometric models are compared, and the geometric model corresponding to the optimal mixing data is used as the design model of the lithium battery slurry mixing equipment (i.e., the optimal slurry type lithium battery slurry mixing equipment). In other words, the geometric model corresponding to the shortest uniform mixing time required under the same stirring effect conditions is used as the design model of the lithium battery slurry mixing equipment. Figure 8a and Figure 8b By comparing the stirring and mixing data of the stirring tanks of multiple geometric models, it can be learned that the monitoring point concentration change curve of the optimized slurry type of the stirring paddle is better than that of the original slurry type of the stirring paddle.

[0121] Specifically, the optimal (i.e., optimal slurry type) stirring and mixing data is 100s, that is, the shortest stirring and uniform mixing time is 100s, which is shorter than the traditional (i.e., original slurry type) stirring and uniform mixing time (350s). In this way, the structure that takes the shortest time determines the best mixing effect of lithium battery slurry, and realizes the optimal structure of lithium battery slurry mixing equipment. Fig. 9 shown.

[0122] The above-mentioned optimization method of lithium battery slurry mixing equipment based on fluid mechanics simulation first establishes several geometric models of lithium battery slurry mixing equipment; then simplifies each geometric model and extracts the fluid area to be calculated, that is, extracts the slurry area of ​​the stirring tank of the geometric model; then divides each geometric model into finite element grids to obtain a grid file of each geometric model; then determines the basic control equation of the computational flow field of lithium battery slurry mixing, and performs volume integration on the basic control equation; then obtains the material properties corresponding to the lithium battery slurry added to the geometric model, and determines the medium flow model according to the calculation equation of the Reynolds coefficient; then obtains the physical parameters and boundary conditions of the initial conditions, and uses the solver to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model to obtain the corresponding solution results; then performs residual judgment on the solution results corresponding to each geometric model; if the residual converges, the solution result is saved; then establishes the component transport equation, adds tracers, and then performs transient solution on the computational fluid model in the stirring tank of each geometric model to obtain the fluid parameters of the stirring tanks of the multiple geometric models, and each The result of the steady-state solution of a geometric model is used as the initial value of the component transport equation; then the fluid parameters of the stirring tank of each geometric model are post-processed to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model changing with time and the flow field velocity cloud map data in the stirring tank; finally, the stirring and mixing data of the stirring tanks of multiple geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment, so that the engineering problem of structural optimization of the lithium battery mixing equipment is realized through computational fluid dynamics analysis, making the design method of the lithium battery slurry mixing equipment simple and convenient; the above-mentioned optimization method solves the problem that the traditional optimization design needs to spend a lot of time and money to judge the stirring effect of the lithium battery mixing equipment through experiments, so that the research and development cost of the optimization method is relatively low, and the flow field simulation analysis in the stirring tank not only greatly shortens the time required for the structural optimization of the lithium battery mixing equipment, that is, shortens the research and development time of the lithium battery mixing equipment, but also solves the problem of poor test results of the traditional lithium battery mixing equipment, so that the optimized lithium battery mixing equipment better meets the actual production needs of the battery mixing equipment.

[0123] In one embodiment, the step of extracting the fluid region to be calculated is specifically: extracting the fluid region to be calculated by volume extraction and Boolean operation to improve the extraction reliability of the fluid region.

[0124] Furthermore, the steps of performing transient solution on the computational fluid model in the stirred tank of each of the geometric models are as follows: solving the mixing transient of the tracer based on the calculation results of the steady-state flow field, taking the result after the steady-state calculation converges as the initial value, and then performing transient calculation, automatically locking the transport equations of parameters such as velocity and turbulence, and only calculating the component transport equations.

[0125] Compared with the traditional method of optimizing design through test structure, the above optimization method has the following advantages:

[0126] (1) Computational fluid dynamics software can provide data and flow field information that are difficult to obtain using experimental methods; the tracer method can be used to describe the mixing effect of lithium battery slurry in the stirring tank.

[0127] (2) In the actual test process of lithium battery mixing equipment, lithium battery slurry is used as the stirring material, which requires huge costs and does not necessarily achieve good results. Computational fluid dynamics software only needs to perform simulation calculations on computer equipment, saving a lot of costs.

[0128] (3) Researchers can use computational fluid dynamics software to directly evaluate data and verify new designs, which helps to carry out technological innovation and greatly shortens the research and development process of lithium battery slurry mixing equipment.

[0129] The present application also provides an optimization device for lithium battery slurry mixing equipment based on fluid mechanics simulation, including a geometric model building module, a model simplification and extraction module, a partitioning module, an integral processing module, a flow type determination module, a flow field solving module, a residual judgment module, an equation building module, a parameter processing module and a comparison output module; the geometric model building module is used to establish a plurality of geometric models of lithium battery slurry mixing equipment; the model simplification and extraction module is used to perform model simplification processing on each of the geometric models respectively, and extract the fluid area that needs to be calculated; the partitioning module is used to divide the finite element grids for each of the geometric models, and encrypt the local area of ​​the stirring paddle of each of the geometric models to obtain the grid file of each of the geometric models; the integral processing module is used to determine the basic control equations of the calculation flow field of the lithium battery slurry mixing, and perform volume integral processing on the basic control equations; the flow type determination module is used to obtain the material properties corresponding to the lithium battery slurry added to the geometric model, and determine the medium flow model according to the calculation equation of the Reynolds coefficient; the flow field solving module is used to obtain the physical parameters and boundary conditions of the initial conditions, and calculate the medium flow model through the solver for each A computational fluid model of a stirring tank of the geometric model is solved for a steady-state flow field to obtain a corresponding solution result; wherein the physical parameter includes a preset rotation speed of a stirring paddle in the stirring tank of the geometric model; a residual judgment module is used to perform residual judgment on the solution result corresponding to each of the geometric models; if the residual converges, the solution result is saved; an equation establishment module is used to establish a component transport equation, add a tracer, and then perform transient solutions on the computational fluid model in the stirring tank of each of the geometric models to obtain fluid parameters of the stirring tanks of multiple geometric models, and use the steady-state solution result of each geometric model as the initial value of the component transport equation; a parameter processing module is used to post-process the fluid parameters of the stirring tank of each of the geometric models to obtain stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model that changes with time and flow field velocity cloud map data in the stirring tank; a comparison output module is used to compare the stirring and mixing data of the stirring tanks of multiple geometric models, and use the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment.

[0130] In this embodiment, after the geometric model establishment module establishes geometric models of several lithium battery slurry mixing equipment, the model simplification and extraction module performs model simplification processing on each geometric model respectively, and extracts the fluid area that needs to be calculated, that is, extracts the slurry area of ​​the stirring tank of the geometric model; then the division module divides each geometric model into finite element grids to obtain a grid file for each geometric model; then the integral processing module determines the basic control equations of the calculation flow field of the lithium battery slurry mixing, and performs volume integral processing on the basic control equations; then the flow type determination module obtains the material properties corresponding to the lithium battery slurry added to the geometric model , and determine the medium flow model according to the calculation equation of the Reynolds coefficient; then the flow field solution module obtains the physical parameters and boundary conditions of the initial conditions, and performs steady-state flow field solution on the computational fluid model of the stirred tank of each geometric model through the solver to obtain the corresponding solution results; then the residual judgment module performs residual judgment on the solution results corresponding to each geometric model respectively; if the residual converges, the solution result is saved; then the equation establishment module establishes the component transport equation, adds a tracer, and then performs transient solution on the computational fluid model in the stirred tank of each geometric model respectively to obtain the stirred tanks of multiple geometric models The fluid parameters of each geometric model are obtained, and the result of the steady-state solution of each geometric model is used as the initial value of the component transport equation; then the parameter processing module post-processes the fluid parameters of the stirring tank of each geometric model to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model changing with time and the flow field velocity cloud map data in the stirring tank; finally, the comparison output module compares the stirring and mixing data of the stirring tanks of multiple geometric models, and uses the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment, so that the engineering problem of the structural optimization of the lithium battery mixing equipment is realized through computational fluid dynamics analysis, making the design method of the lithium battery slurry mixing equipment simple and convenient; the above-mentioned optimization method solves the problem that the traditional optimization design needs to spend a lot of time and money to judge the stirring effect of the lithium battery mixing equipment through experiments, so that the research and development cost of the optimization method is low, and the flow field simulation analysis in the stirring tank not only greatly shortens the time required for the structural optimization of the lithium battery mixing equipment, that is, shortens the research and development time of the lithium battery mixing equipment, but also solves the problem of poor test results of the traditional lithium battery mixing equipment, so that the optimized lithium battery mixing equipment better meets the actual production needs of the battery mixing equipment.

[0131] The present application also provides a computer device, which may be a server, and its internal structure diagram may be as shown in Fig.10As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as geometric models, material properties, and calculation equations of the Reynolds coefficient. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the method described in any of the above embodiments are implemented.

[0132] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In one of the embodiments, the present application further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0134] In one of the embodiments, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0136] Compared with the prior art, this application has at least the following advantages:

[0137] 1. The above-mentioned optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation first establishes several geometric models of lithium battery slurry mixing equipment; then simplifies each geometric model and extracts the fluid area to be calculated, that is, extracts the slurry area of ​​the stirring tank of the geometric model; then divides each geometric model into finite element grids to obtain a grid file for each geometric model; then determines the basic control equations of the computational flow field of lithium battery slurry mixing, and performs volume integration on the basic control equations; then obtains the material properties corresponding to the lithium battery slurry added to the geometric model, and determines the medium flow model according to the calculation equation of the Reynolds coefficient; then obtains the physical parameters and boundary conditions of the initial conditions, and uses the solver to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model to obtain the corresponding solution results; then performs residual calculations on the solution results corresponding to each geometric model. Difference judgment; if the residual converges, the solution result is saved; then the component transport equation is established, and the tracer is added, and then the computational fluid model in the stirring tank of each geometric model is transiently solved to obtain the fluid parameters of the stirring tanks of multiple geometric models, and the result of the steady-state solution of each geometric model is used as the initial value of the component transport equation; then the fluid parameters of the stirring tank of each geometric model are post-processed to obtain the stirring and mixing data of the tracer concentration of the stirring tank of the corresponding geometric model that changes with time and the flow field velocity cloud map data in the stirring tank; finally, the stirring and mixing data of the stirring tanks of multiple geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment, so that the engineering problem of structural optimization of the lithium battery mixing equipment is realized through computational fluid dynamics analysis, making the design method of the lithium battery slurry mixing equipment simple and convenient;

[0138] 2. The above-mentioned optimization method solves the problem that traditional optimization design needs to spend a lot of time and money to judge the stirring effect of lithium battery mixing equipment through experiments, so that the research and development cost of the optimization method is relatively low. The flow field simulation analysis in the stirring tank not only greatly shortens the time required for structural optimization of lithium battery mixing equipment, that is, shortens the research and development time of lithium battery mixing equipment, but also solves the problem of poor test effect of traditional lithium battery mixing equipment. The lithium battery mixing equipment after optimization better meets the actual production needs of battery mixing equipment.

[0139] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation, characterized in that: include: Establish geometric models of several lithium battery slurry mixing equipment; Simplifying each of the geometric models and extracting the fluid region that needs to be calculated; Dividing each of the geometric models into finite element grids, and performing encryption processing on a local area of ​​the stirring blade of each of the geometric models to obtain a grid file of each of the geometric models; Determine the basic control equation of the flow field of the lithium battery slurry mixing, and perform volume integration processing on the basic control equation; Obtaining material properties corresponding to the lithium battery slurry added to the geometric model, and determining a medium flow model according to a calculation equation for the Reynolds coefficient; wherein the material properties include density and viscosity; Obtaining physical parameters and boundary conditions of the initial conditions, and performing steady-state flow field solving on the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results, wherein the physical parameters include a preset rotation speed of a stirring paddle in the stirring tank of the geometric model; Performing residual judgment on the solution results corresponding to each of the geometric models; If the residual converges, the solution result is saved; Establishing a component transport equation, adding a tracer, and then performing transient solutions to the computational fluid model in the stirred tank of each of the geometric models to obtain fluid parameters of the stirred tanks of the multiple geometric models, and using the results of the steady-state solutions of each geometric model as the initial values ​​of the component transport equation; Post-processing the fluid parameters of the stirring tank of each geometric model to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model changing with time and the flow field velocity cloud map data in the stirring tank; The stirring and mixing data of the stirring tanks of the plurality of geometric models are compared, and the geometric model corresponding to the optimal stirring and mixing data is used as the design model of the lithium battery slurry mixing equipment.

2. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The basic control equations include the mass conservation equation and the momentum conservation equation; The steps of determining the basic control equation of the flow field of the lithium battery slurry mixing and performing volume integration processing on the basic control equation include: Determine the basic governing equations for the computational flow field of lithium battery slurry mixing; Volume integration processing is performed on the mass conservation equation and the momentum conservation equation respectively.

3. The optimization method of lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 2, characterized in that: The mass conservation equation is: The momentum conservation equation is: Where ρ is the fluid density, t is the time, the local velocity components of the velocity V at any point in the fluid field are u, v, and w respectively; the x, y, and z directions are the spatial directions and positions respectively; p is the pressure in the fluid microelement; τ ij is the viscous stress acting on the surface of the microelement; ρg i is the gravitational body force in the i direction, F i is the external body force in the i direction.

4. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The steps of extracting the fluid region to be calculated are specifically as follows: The fluid area that needs to be calculated is extracted through volume extraction and Boolean operations.

5. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The step of dividing each geometric model into finite element grids is specifically as follows: dividing each geometric model into finite element grids by using an unstructured tetrahedral grid method, and performing encryption processing on the region of the stirring paddle corresponding to each geometric model.

6. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The viscosity change of the lithium battery slurry conforms to that of a pseudoplastic fluid; A power-law fluid model is used to simulate non-Newtonian fluids, where the power-law fluid satisfies τ = Kγ. n , where τ is the shear stress, K is the consistency coefficient, γ is the shear strain rate, n is the power law exponent, and n<1.

7. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The calculation equation for the Reynolds coefficient of non-Newtonian fluid is: Where: Re is a dimensionless number; V is the average flow velocity, m / s; D is the diameter of the impeller, mm; ρ is the fluid density, kg / m 3 .

8. The optimization method for lithium battery slurry mixing equipment based on fluid mechanics simulation according to claim 1, characterized in that: The component transport equation is: Where ρ is the density of the fluid, μ is the velocity of the fluid, is the mass fraction of the tracer, Γ is the diffusion coefficient of the tracer in the fluid, in is the diffusion rate of the tracer, is the source term. The numerical simulation process of lithium battery slurry mixing does not involve chemical reactions. The value of is 0.

9. An optimization device for lithium battery slurry mixing equipment based on fluid mechanics simulation, characterized in that: include: The geometric model building module is used to build geometric models of several lithium battery slurry mixing equipment; A model simplification and extraction module, used to perform model simplification processing on each of the geometric models and extract the fluid area that needs to be calculated; A partitioning module, used to partition each of the geometric models into finite element meshes, and to perform encryption processing on a local area of ​​the stirring paddle of each of the geometric models to obtain a mesh file of each of the geometric models; An integral processing module, used to determine the basic control equation of the flow field of the lithium battery slurry mixing, and perform volume integral processing on the basic control equation; Determine the flow pattern module, which is used to obtain the material properties corresponding to the lithium battery slurry added to the geometric model, and determine the medium flow model according to the calculation equation of the Reynolds coefficient; A flow field solving module, used to obtain physical parameters and boundary conditions of the initial conditions, and to solve the steady-state flow field of the computational fluid model of the stirring tank of each geometric model through a solver to obtain corresponding solution results; wherein the physical parameters include a preset rotation speed of a stirring paddle in the stirring tank of the geometric model; A residual judgment module, used for performing residual judgment on the solution results corresponding to each of the geometric models; If the residual converges, the solution result is saved; An equation building module is used to build a component transport equation, add a tracer, and then perform transient solutions to the computational fluid model in the stirred tank of each geometric model to obtain fluid parameters of the stirred tanks of multiple geometric models, and use the results of the steady-state solution of each geometric model as the initial value of the component transport equation; A parameter processing module, used for post-processing the fluid parameters of the stirring tank of each geometric model, to obtain stirring and mixing data of the concentration of the tracer in the stirring tank of the corresponding geometric model varying with time and the flow field velocity cloud map data in the stirring tank; The comparison output module is used to compare the stirring and mixing data of the stirring tanks of the multiple geometric models, and use the geometric model corresponding to the optimal stirring and mixing data as the design model of the lithium battery slurry mixing equipment.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

Images