Optimization Method for Lithium Battery Slurry Dispersion Equipment Based on CFD-DPM Coupled Simulation

Abstract

The present application discloses an optimization method for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation. The method includes: establishing a number of different geometric models of the dispersion chamber assembly; extracting a flow field region model from the geometric model of the dispersion chamber assembly; determining the flow field control equation of the flow field region model; determining the turbulence model and the reference frame model; performing a steady-state operation to obtain an initial flow field; setting the physical parameters of the particles; determining the motion control equation of the particles; performing a transient solution to obtain a simulation result; respectively post-processing each simulation result to obtain a velocity contour map and a pressure contour map at a set cross-section, a particle motion state map, and rotor wall shear force data; determining the optimal geometric model of the dispersion chamber assembly. The present application can effectively reduce the time and money spent on the structural optimization of the lithium battery slurry dispersion device.

Description

Technical Field

[0001] The present application relates to the technical field of CFD-DPM coupled simulation, and particularly to an optimization method for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation. Background Art

[0002] In recent years, China's new energy vehicle industry has been continuously developing. As the core part of new energy vehicles, the performance of lithium batteries is directly related to the driving range, safety performance, and usage cost of new energy vehicles. Lithium-ion batteries have advantages such as high specific energy, long charge-discharge life, fast charging speed, and high safety performance, and are more suitable as power batteries for new energy vehicles. In the lithium battery slurry-making process, to ensure the consistency and sedimentation stability of the lithium battery slurry, a high-speed dispersion device needs to be used to disperse the slurry.

[0003] Existing high-speed dispersion devices often have problems such as difficult powder feeding and poor dispersion effect. Therefore, it is necessary to optimize the high-speed dispersion device. Currently, generally, the structure of the high-speed dispersion device is optimized through experimental means, that is, actually changing the structure of the high-speed dispersion device, such as the rotor structure, and then using the high-speed dispersion device with the changed structure to actually disperse the slurry, and then judging the actual effect based on the dispersion result. This requires a lot of time and money. Moreover, based on the current experimental means, technicians cannot understand the specific situation of the flow characteristics of the slurry in the dispersion chamber of the high-speed dispersion device, and it is also difficult to consider the simultaneous action of multiple factors.

[0004] Based on this, the present application proposes an optimization scheme for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation. Summary of the Invention

[0005] The embodiments of the present application provide an optimization method, device, computer device, computer-readable storage medium, and computer program product for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation, which can solve at least one technical problem existing in the background art.

[0006] In view of this, in the first aspect, the embodiments of the present application provide an optimization method for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation, including:

[0007] Establishing a number of different geometric models of the dispersion chamber assembly based on the key structural parameters of the dispersion chamber assembly of the dispersion device, where the dispersion chamber assembly includes a dispersion chamber and a rotor and a stator located in the dispersion chamber, the dispersion chamber is provided with a slurry inlet, a slurry outlet, and a powder inlet, and the key structural parameters at least include rotor structure parameters;

[0008] Extracting a flow field region model from the geometric model of the dispersion chamber assembly;

[0009] Determine the flow field control equation of the flow field region model;

[0010] Determine the turbulence model and the reference frame model according to the key operating parameters of the dispersion device, the slurry properties, and the flow field control equation, where the key operating parameters include the rotor speed;

[0011] Perform a steady-state operation according to the flow field boundary conditions to obtain the steady-state operation result as the initial flow field for transient CFD-DPM coupling solution, where the flow field boundary conditions include the slurry inlet velocity and the slurry outlet velocity;

[0012] Set the physical parameters of the particles;

[0013] Determine the motion control equation of the particles;

[0014] Based on the initial flow field, the physical parameters of the particles, the velocity of the particles at the powder inlet, the flow field control equation, the motion control equation, the turbulence model, and the reference frame model, determine the time step for transient CFD-DPM coupling solution according to the set dispersion time, and perform transient CFD-DPM coupling solution to obtain the simulation result;

[0015] Post-process the simulation results based on each geometric model of the dispersion chamber assembly respectively to obtain the velocity contour, pressure contour, particle motion state diagram, and rotor wall shear force data at the set cross-section;

[0016] Based on the velocity contour, pressure contour, particle motion state diagram, and rotor wall shear force data corresponding to each geometric model of the dispersion chamber assembly, determine the optimal geometric model of the dispersion chamber assembly.

[0017] Optionally, the rotor includes a rotating shaft portion, a plurality of turbine blades provided on the rotating shaft portion, and a turntable portion coaxially connected to the rotating shaft portion. A circle of rotor dispersion teeth are arranged at circumferential intervals on the turntable portion; the stator includes a circle of stator dispersion teeth coaxially arranged with the circle of rotor dispersion teeth; the rotor structure parameters include at least one of the number of the rotor dispersion teeth, the gap width between adjacent rotor dispersion teeth, the gap width between the rotor and the stator, the shape of the turbine blades, and the number of the turbine blades.

[0018] Optionally, the extraction of the flow field region model from the geometric model of the dispersion chamber assembly includes:

[0019] Perform a simplification process on the geometric model of the dispersion chamber assembly to obtain a simplified geometric model;

[0020] Extract the flow field region model from the simplified geometric model through volume extraction and Boolean operations.

[0021] Optionally, after extracting the flow field region model from the geometric model of the dispersion chamber assembly, the method further includes: performing mesh division on the flow field region model and encrypting the mesh of the rotor region therein to obtain a mesh file of the flow field region model;

[0022] The steady-state operation is performed based on the mesh file, the key operating parameters, the slurry properties, the flow field boundary conditions, the flow field control equations, the turbulence model, and the reference frame model.

[0023] Optionally, the fluid control equations include the mass conservation equation and the momentum conservation equation;

[0024] The mass conservation equation is:

[0025] ;

[0026] The momentum conservation equation is:

[0027] ;

[0028] Wherein, is the fluid density, t is the time, u , v , w are the local velocity components of the flow velocity V at any point in the flow field, x , y , z are the spatial directions, p is the pressure in the fluid microelement, is the viscous stress acting on the surface of the microelement, is the gravitational body force in the direction, is the external body force in the

[0029] Optionally, the slurry properties include slurry composition, slurry solids content, slurry viscosity, and slurry density.

[0030] Optionally, the motion control equations include the translation equation and the rotation equation;

[0031] The translation equation is:

[0032] ;

[0033] The rotation equation is:

[0034] ;

[0035] Where is the mass of particle i, is the velocity vector of particle i, is the acceleration vector of particle i, and g is the acceleration due to gravity, is the drag force, is the pressure gradient force, is the particle collision force, is the force exerted by the fluid on the particle, is the other force; is the moment of inertia of particle i; is the angular velocity vector of particle i; is the angular acceleration vector of particle i; is the total torque vector acting on particle i.

[0036] Optionally, the physical parameters of the particles include Poisson's ratio, solid density, shear modulus, Young's modulus, and diameter.

[0037] Optionally, the transient CFD-DPM coupling solution includes:

[0038] (1) Performing a continuous phase flow field calculation to obtain basic flow field information;

[0039] (2) Performing a discrete phase calculation using the motion control equation according to the external forces acting on the particles in the continuous phase flow field;

[0040] (3) Updating the positions of the particles in the continuous phase flow field;

[0041] (4) Judging whether the results of the continuous phase flow field calculation and the discrete phase calculation both converge;

[0042] If the judgment result is no, return to step (1);

[0043] If the judgment result is yes, end the calculation of the current time step.

[0044] Optionally, the time step is determined based on the rotational speed of the rotor. The duration of each rotation of the rotor by a set angle is one such time step, and the dispersion time is equal to the sum of all time steps.

[0045] Optionally, the slurry inlet is located at the bottom of the dispersion chamber and opens downward, the powder inlet is located at the top of the dispersion chamber and opens upward, and the slurry inlet, the powder inlet, and the rotor are coaxially arranged; the cross-section mapped on the dispersion chamber assembly of the set cross-section passes through the radial cross-section of the rotor.

[0046] In a second aspect, an optimization device for a lithium battery slurry dispersion device based on CFD-DPM coupling simulation according to an embodiment of the present application includes a module for executing the method according to any one of the first aspect.

[0047] In a third aspect, an embodiment of the present application further provides a computer device, including a memory and a processor;

[0048] The memory is connected to the processor. The memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method according to any one of the first aspect.

[0049] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and the computer program is adapted to be loaded and executed by a processor to execute the method according to any one of the first aspect.

[0050] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method according to any one of the first aspect.

[0051] The above optimization method, device, computer device, computer-readable storage medium, and computer program product for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation first establish several different geometric models of the dispersion chamber assembly based on the key structural parameters of the dispersion chamber assembly of the dispersion device, and extract the flow field region model from the geometric models of the dispersion chamber assembly; then, determine the flow field control equation of the flow field region model, and determine the turbulence model and reference frame model according to the key operating parameters of the dispersion device, slurry properties, and flow field control equation; then perform a steady-state operation. Then, use the flow field obtained from the steady-state operation as the initial flow field and combine the particle parameters and the determined motion control equation of the particles to perform transient CFD-DPM coupled solution, and the simulation results can be obtained. After the simulation results of each geometric model of the dispersion chamber assembly are obtained, post-processing is performed respectively to obtain the velocity contour and pressure contour at the set cross-section, the particle motion state diagram, and the rotor wall shear force data. Then, based on the velocity contour and pressure contour, particle motion state diagram, and rotor wall shear force data corresponding to each geometric model of the dispersion chamber assembly, the optimal geometric model of the dispersion chamber assembly can be determined. In this way, the engineering problem of optimizing the structure of the lithium battery slurry dispersion device is realized through the combined analysis of computational fluid dynamics and discrete phase method, and the implementation method is simpler and more efficient, which can effectively reduce the time and money spent on optimizing the structure of the lithium battery slurry dispersion device. Moreover, the embodiments of the present application dynamically simulate the flow and dispersion process of the slurry inside the device. Based on the velocity contour and pressure contour, technicians can understand the specific situation of the flow characteristics of the slurry in the dispersion chamber of the lithium battery slurry dispersion device. Based on the particle motion state diagram, they can understand the specific situation of the particle distribution. Based on the rotor wall shear force data, they can understand the improvement of the rotor wall shear force. The embodiments of the present application consider the problem of the dispersion of the lithium battery slurry dispersion device affected by multi-factor coupling, combine computational fluid dynamics (CFD) with discrete phase method (DPM), and establish a multi-scale simulation model, which can simultaneously simulate the macroscopic flow behavior and microscopic particle dispersion of the slurry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a geometric model of the lithium battery slurry dispersion device according to an embodiment of the present application.

[0053] Figure 2 It is a schematic cross-sectional structure diagram of the geometric model of the dispersion chamber assembly according to an embodiment of the present application.

[0054] Figure 3 It is a schematic cross-sectional structure diagram of the geometric model of the dispersion chamber assembly from another perspective according to an embodiment of the present application.

[0055] Figure 4 It is a flowchart of the optimization method for the lithium battery slurry dispersion device based on CFD-DPM coupled simulation according to an embodiment of the present application.

[0056] Figure 5 This is the simplified geometric model obtained after the geometric model of the dispersion chamber component in the embodiment of the present application is simplified.

[0057] Figure 6 This is the flow field region model extracted in the embodiment of the present application.

[0058] Figure 7 This is the rotor model before simplification in the embodiment of the present application.

[0059] Figure 8 This is the rotor model after simplification in the embodiment of the present application.

[0060] Figure 9 This is a schematic diagram of the mesh file obtained after meshing the simplified geometric model in the embodiment of the present application.

[0061] Figures 10 to 12 These are three examples of the rotor model of the geometric model of the dispersion chamber component obtained by changing the rotor structure parameters in the present application after simplification.

[0062] Figure 13a This is the pressure contour map obtained based on the original rotor model (before changing the rotor structure parameters) in the embodiment of the present application.

[0063] Figure 13b This is the velocity contour map obtained based on the original rotor model in the embodiment of the present application.

[0064] Figure 14a is based on Figure 10 The pressure contour map obtained from the rotor model in.

[0065] Figure 14b is based on Figure 10 The velocity contour map obtained from the rotor model in.

[0066] Figure 15a is based on Figure 11 The pressure contour map obtained from the rotor model in.

[0067] Figure 15b is based on Figure 11 The velocity contour map obtained from the rotor model in.

[0068] Figure 16a is based on Figure 12 The pressure contour map obtained from the rotor model in.

[0069] Figure 16b is based on Figure 12 The velocity contour map obtained from the rotor model in.

[0070] Figure 17 This is the particle motion state diagram obtained based on the original rotor model in the embodiment of the present application.

[0071] Figure 18 The particle motion state diagram obtained based on Figure 10 the rotor model in

[0072] Figure 19 The particle motion state diagram obtained based on Figure 11 the rotor model in

[0073] Figure 20 The particle motion state diagram obtained based on Figure 12 the rotor model in

[0074] Figure 21 The average value of the rotor wall shear force obtained based on the original rotor model in the embodiments of the present application.

[0075] Figure 22 The average value of the rotor wall shear force obtained based on Figure 10 the rotor model in

[0076] Figure 23 The average value of the rotor wall shear force obtained based on Figure 11 the rotor model in

[0077] Figure 24 The average value of the rotor wall shear force obtained based on Figure 12 the rotor model in

[0078] Figure 25 The schematic diagram of the computer device in the embodiments of the present application. Specific embodiments

[0079] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0080] The embodiments of the present application disclose an optimization method for a lithium battery slurry dispersion device based on CFD-DPM coupled simulation. This method is used to optimize the dispersion chamber assembly of the lithium battery slurry dispersion device.

[0081] Please refer to Figures 1 to 3, the dispersion chamber assembly 3 includes a dispersion chamber 11, a rotor 10 and a stator 9 located in the dispersion chamber 11. The dispersion chamber 11 is provided with a slurry inlet 7, a slurry outlet 6 and a powder inlet 5. The slurry enters the dispersion chamber 11 from the slurry inlet 7, and the powder (granular material) enters the dispersion chamber 11 from the powder inlet 5 and is mixed into the slurry. The slurry mixed with the powder is stirred under the high-speed rotation of the rotor 10, and under the action of centrifugal force, it enters the narrow gap between the rotor 10 and the stator 9, and is subjected to strong shearing, friction, impact and other actions to achieve the purpose of mixing, dispersing and refining.

[0082] Specifically, the main material and liquid material in the raw materials of the lithium battery slurry can be mixed in a slurry stirring tank (not shown in the figure) and then enter the dispersion chamber 11 through the slurry inlet 7. Further, the slurry can be circulated and stirred in the dispersion device and the stirring tank until the viscosity of the slurry reaches the target viscosity to obtain a slurry that meets the requirements.

[0083] In a specific example, the slurry inlet 7 is located at the bottom end of the dispersion chamber 11 and opens downward, the powder inlet 5 is located at the top end of the dispersion chamber 11 and opens upward, the slurry inlet 7, the powder inlet 5 and the rotor 10 are coaxially arranged, and the slurry outlet 6 is connected to one side of the dispersion chamber 11. Of course, this is only an example of the dispersion chamber assembly 3, and the dispersion chamber assembly 3 is not limited thereto.

[0084] Specifically, please refer to Figure 2 and Figure 3 and Figure 7 , the rotor includes a rotating shaft portion 101, a plurality of turbine blades 102 provided on the rotating shaft portion 101, and a turntable portion 103 coaxially connected to the rotating shaft portion 101. A circle of rotor dispersion teeth 104 are arranged at circumferential intervals on the turntable portion 103; the stator 9 includes a circle of stator dispersion teeth 91 coaxially arranged with the circle of rotor dispersion teeth 104. Since the rotor includes turbine blades 102, the slurry can be fully stirred.

[0085] The narrow gap between the above-mentioned rotor 10 and stator 9 refers to the gap 14 between the rotor dispersion teeth 104, the gap 15 between the stator dispersion teeth 91, and the gap 13 between the rotor 10 and the stator 9. The gap 13 between the rotor 10 and the stator 9 refers to the radial gap between the rotor dispersion teeth 104 and the stator dispersion teeth 91.

[0086] Figure 1 The schematic diagram of an example of the lithium battery slurry dispersion device is given. In this example, the dispersion device includes a mounting bracket 4, and the mounting bracket 4 is provided with a dispersion chamber assembly 3 and a driving assembly 1. The driving assembly 1 is used to drive the rotor 10 to rotate. Specifically, one end of the dispersion chamber 11 provided with the slurry inlet 7 is hermetically installed on the mounting bracket 4 through a connecting body 12.

[0087] Please refer to Figure 4, the optimization method of the lithium battery slurry dispersion device based on CFD-DPM coupling simulation in the embodiments of the present application includes the following steps:

[0088] S11, establish several different geometric models of the dispersion chamber assembly based on the key structural parameters of the dispersion chamber assembly of the dispersion device, and the key structural parameters at least include the rotor structural parameters.

[0089] The key structural parameters refer to the structural parameters that have a relatively large impact on the performance of the dispersion chamber assembly. Among them, the rotor is the main functional component of the dispersion chamber assembly, and the related structural parameters of the rotor will undoubtedly have a greater impact on the performance of the dispersion chamber assembly. Therefore, the embodiments of the present application use the rotor structural parameters as the key structural parameters to participate in the optimization.

[0090] Several different geometric models of the dispersion chamber assembly can be established by changing the key structural parameters of the dispersion chamber assembly to be optimized, so as to select the optimal geometric model of the dispersion chamber assembly from them. For example, the same key structural parameter can be changed in different ways, or different key structural parameters can be changed, etc., as long as different geometric models of the dispersion chamber assembly are obtained based on the adjustment of the key structural parameters.

[0091] Please combine Figure 2 and Figure 3 and Figure 7 , in some embodiments, the rotor 10 includes a rotating shaft portion 101, several turbine blades 102 arranged on the rotating shaft portion 101, and a turntable portion 103 coaxially connected to the rotating shaft portion 101. A circle of rotor dispersion teeth 104 is arranged at circumferential intervals on the turntable portion 103; the stator 9 includes a circle of stator dispersion teeth 91 coaxially arranged with the circle of rotor dispersion teeth 104; the rotor structural parameters at least include at least one of the number of rotor dispersion teeth 104, the gap width between adjacent rotor dispersion teeth 104, the gap width between the rotor 10 and the stator 9, the shape of the turbine blades 102, and the number of turbine blades 102. Among them, the gap width between the rotor 10 and the stator 9 refers to the radial distance between the rotor dispersion teeth 104 and the stator dispersion teeth 91. Specifically, the key structural parameters further include at least one of the number of stator dispersion teeth 91 and the gap width between adjacent stator dispersion teeth 91.

[0092] Figures 10 to 12 Examples of obtaining different geometric models of the dispersion chamber assembly by changing the rotor structural parameters are given, and what is shown is the simplified model of the rotor model in each geometric model of the dispersion chamber assembly. In this example, Figures 10 to 12 the rotor models of respectively make different changes to the shape of the turbine blades of the rotor, both increase the number of turbine blades 102 in a circumferential distribution group from 5 to 6, and increase the number of rotor dispersion teeth 104 from 10 to 12, and reduce the gap width between adjacent rotor dispersion teeth 104.

[0093] In some embodiments, a geometric model of the dispersion cavity assembly can be established in the 3D modeling software Solidworks.

[0094] S12. Extract the flow field region model from the geometric model of the dispersion cavity assembly (as Figure 6 shown).

[0095] In some embodiments, extracting the flow field region model from the geometric model of the dispersion cavity assembly includes:

[0096] Performing a simplification process on the geometric model of the dispersion cavity assembly to obtain a simplified geometric model (as Figure 5 shown);

[0097] Extracting the flow field region model from the simplified geometric model through volume extraction and Boolean operations.

[0098] Specifically, import the geometric model of the dispersion cavity assembly into the Space Claim functional module of ANSYS (i.e., computer-aided engineering software, also known as CAE software) for model simplification processing, and extract the flow field region model to be calculated. In one embodiment, the rotor model before simplification is as Figure 7 shown. Delete features such as connectors, holes, and fillets that are irrelevant to the simulation analysis to obtain the simplified rotor model as Figure 8 shown for mesh generation. After obtaining the simplified geometric model, then extract the flow field region model from it as Figure 6 shown, which is divided into a stationary domain model 16 and a rotating domain model 17.

[0099] S13. Perform mesh generation on the flow field region model and encrypt the mesh in the rotor region thereof to obtain the mesh files of each flow field region model. After performing mesh generation on the Figure 6 flow field region model, the schematic diagram of the obtained mesh file is as Figure 9 shown, which is divided into a stationary domain mesh 18 and a rotating domain mesh 19.

[0100] Specifically, encrypting the mesh in the rotor region includes:

[0101] Performing encryption processing on the mesh in the rotor region by using the method of unstructured tetrahedral meshes.

[0102] The simplified and extracted flow field region model in the ANSYS Space Claim module can be imported into the ANSYS Mesh module. The finite element mesh of the flow field region model is divided by the unstructured tetrahedron method of the ANSYS Mesh module, and the local region of the rotor in the flow field region model (rotor region mesh) is encrypted to obtain the mesh file corresponding to each flow field region model, while ensuring the mesh quality and calculation accuracy.

[0103] Furthermore, the local region of the rotor of each geometric model is encrypted to obtain the computational domain meshes with n different numbers of meshes, and the mesh independence verification is carried out. The computational domain mesh with the optimal number of meshes is selected to better ensure the mesh quality and calculation accuracy, where n is an integer. For example, when n is 4, the optimal number of meshes is 2.7 million, that is, the computational domain mesh with 2.7 million meshes is selected.

[0104] Specifically, the local region of the rotor refers to the flow field region within a set range (a relatively small range) from the rotor wall surface. The outer boundary of this range can be set to be 1 - 5 mm away from the rotor wall surface, and the inner boundary is the rotor wall surface. The rotor is set as the wall surface. When simulating the flow, generally the boundary layer near the wall surface in the flow field is encrypted to make the flow field more accurate and adapt to the complex geometric shape of the rotor.

[0105] It should be noted that for this application, step S13 is not a necessary step. That is to say, before determining the flow field control equation of the flow field region model, the above step of obtaining the mesh file of the flow field region model can be not executed.

[0106] S14. Determine the flow field control equation of the flow field region model.

[0107] Specifically, the fluid control equations include the mass conservation equation and the momentum conservation equation.

[0108] The mass conservation equation is:

[0109] ;

[0110] The momentum conservation equation is:

[0111] ;

[0112] Among them, is the fluid density, t is the time, u , v , w are respectively the local velocity components of the flow velocity V at any point in the flow field, x , y , zare spatial directions respectively, p is the pressure in the fluid microelement, is the viscous stress acting on the surface of the microelement, is the gravitational body force in the is the external body force in the

[0113] S15. Determine the turbulence model and the reference frame model according to the key operating parameters of the dispersion equipment, the slurry properties and the flow field control equation. The key operating parameters include the rotor speed. The slurry properties are the material properties of the slurry.

[0114] Specifically, the slurry properties include slurry composition, slurry solids content, slurry viscosity and slurry density.

[0115] In one embodiment, the viscosity change of the lithium battery slurry conforms to that of a pseudoplastic fluid; the viscosity and density of the lithium battery slurry are set according to the viscosity and density of the actual solvent.

[0116] Exemplarily, the rotor speed can be set to 2000 rpm, and the lithium battery slurry is set according to the material properties of the solvent NMP during the dispersion process. The density is set to 1000 kg / m 3 , and the viscosity is set to 1.65 mPa·s. Select a suitable turbulence model and reference frame model based on the corresponding values and the determined flow field control equation, etc.

[0117] The turbulence models include the k-ε model, the k-ω model, the Reynolds stress model, etc. Different turbulence models take different solution times and have different accuracies. Among them, the standard k-ε model is widely used and has a moderate amount of calculation.

[0118] When determining the turbulence model, it can be judged by the Reynolds number. When the Reynolds number Re < 2000, it is a laminar model; when the Reynolds number Re > 4000, it is a turbulence model. The Reynolds number calculation formula is Re = ρND² / μ, where Re is the Reynolds number, ρ is the density, N is the speed, D is the outer diameter of the rotor, and μ is the viscosity. In one example, ρ is 1000 kg / m3, N is 2000 rpm, D is 140 mm, and μ is 0.00165 Pa·s. The calculated Reynolds number > 4000, so the turbulence model is selected.

[0119] The reference frame models include the multiple reference frame model, the sliding mesh, etc. Among them, the multiple reference frame model is generally applicable to steady-state analysis, and the sliding mesh model is generally applicable to transient analysis.

[0120] In one embodiment, the determined turbulence model is the standard k-ε model, and the determined reference frame model is the multiple reference frame model.

[0121] S16. Perform a steady-state operation according to the flow field boundary conditions to obtain the steady-state operation result as the initial flow field for transient CFD-DPM coupled solution, where the flow field boundary conditions include the slurry inlet velocity and the slurry outlet velocity.

[0122] Transient solution depends on the selection of the initial value. Therefore, a steady-state calculation is first performed to obtain the initial flow field, and then a transient solution is carried out based on the initial flow field.

[0123] Specifically, the steady-state operation is based on the mesh file, key operating parameters, slurry properties, flow field boundary conditions, flow field control equations, turbulence models, and reference frame models.

[0124] Exemplarily, the slurry inlet velocity is set to 0.086 m / s, and the slurry outlet velocity can be obtained according to the slurry inlet velocity. The rotor speed, as a key operating parameter, is set to 2000 rpm.

[0125] Specifically, the slurry properties involved in the steady-state operation may include slurry viscosity and slurry density.

[0126] S17. Set the physical parameters of the particles (powder particles).

[0127] To perform transient CFD-DPM coupled solution, it is necessary to first set the physical parameters of the particles. Specifically, the physical parameters of the particles include Poisson's ratio, solid density, shear modulus, Young's modulus, and diameter.

[0128] In one embodiment, the particle diameter is set to 0.1 mm.

[0129] S18. Determine the motion control equations of the particles.

[0130] After determining the physical parameters of the particles in the dispersion chamber, the applicable motion control equations of the particles can be selected.

[0131] Specifically, the motion control equations include the translational equation and the rotational equation.

[0132] The translational equation is:

[0133] ;

[0134] The rotational equation is:

[0135] ;

[0136] Where is the mass of particle i, is the velocity vector of particle i, is the acceleration vector of particle i, g is the acceleration due to gravity, is the drag force, is the pressure gradient force, is the particle collision force, is the force exerted by the fluid on the particle, is other forces; is the moment of inertia of particle i; is the angular velocity vector of particle i; is the angular acceleration vector of particle i; is the total torque vector acting on particle i.

[0137] S19. Based on the initial flow field, physical parameters of the particles, velocity of the particles at the powder inlet, flow field control equations, motion control equations, turbulence model, and reference frame model, determine the time step of the transient CFD-DPM coupling solution according to the set dispersion time, and perform the transient CFD-DPM coupling solution to obtain the simulation results.

[0138] Specifically, the velocity of the particles at the powder inlet is set to 0.009 m / s.

[0139] Specifically, the turbulence model and reference frame model used in the transient CFD-DPM coupling solution are the same as those used in the steady-state operation. In one embodiment, the turbulence model is the standard k-ε model, and the reference frame model is the multiple reference frame model.

[0140] Specifically, the transient CFD-DPM coupling solution includes:

[0141] Perform the continuous phase flow field calculation to obtain the basic flow field information.

[0142] According to the external forces acting on the particles in the continuous phase flow field, perform the discrete phase calculation using the motion control equations. That is, calculate the discrete phase (particles) entering the flow field from the particle injection source. The external forces acting on the particles include drag force, force exerted by the fluid on the particles, gravity, etc.

[0143] Update the positions of the particles in the continuous phase flow field.

[0144] Judge whether the results of the continuous phase flow field calculation and the discrete phase calculation both converge.

[0145] If the judgment result is no, return to the continuous phase flow field calculation step to repeat the above steps.

[0146] If the judgment result is yes, end the calculation of the current time step.

[0147] It can be understood that if the results of the continuous phase flow field calculation and the discrete phase calculation both converge within the current time step, the solution results can be directly saved as the simulation results. Of course, it is also possible to continue the calculation of the subsequent time steps and then save the corresponding solution results as the simulation results according to needs.

[0148] When performing numerical calculation iterations for each equation, when the difference between the calculated result and the result obtained from the previous calculation, that is, the residual, is lower than the set threshold, it is considered that the calculation converges. In one embodiment, the residual can be set to 0.001.

[0149] Specifically, the time step is determined based on the rotational speed of the rotor. The duration for the rotor to rotate a set angle is one time step, and the dispersion time is equal to the sum of all time steps.

[0150] Exemplarily, the duration for the rotor to rotate 1° can be one time step.

[0151] Exemplarily, when the rotational speed of the rotor is 2000 revolutions per minute, one time step should be set to 0.0005 s. Considering the amount of calculation, the actual time step is set to 0.01 s. To simulate the flow situation within 1 s, 100 time steps are set. 20 iterative calculations are set within each time step. The particles enter the dispersion chamber from the powder inlet within 0 s to 0.5 s, and the particle motion condition from 0 s to 1 s is simulated.

[0152] S20, post-process the simulation results based on the geometric models of each dispersion chamber component respectively to obtain the velocity contour map, pressure contour map, particle motion state map, and rotor wall shear force data at the set cross-section.

[0153] After each geometric model of the dispersion chamber component completes the above simulation process to obtain the corresponding simulation results, the simulation results can be post-processed to obtain the velocity contour map, pressure contour map, particle motion state map, and rotor wall shear force data at the set cross-section.

[0154] How to select the set cross-section is known to those skilled in the art, and it should reflect the flow characteristics of the flow field.

[0155] Please refer to Figure 2 , in one embodiment, the slurry inlet 7 is located at the bottom of the dispersion chamber 11 and opens downward, the powder inlet 5 is located at the top of the dispersion chamber 11 and opens upward, and the slurry inlet 7, powder inlet 5, and rotor 10 are coaxially arranged; the cross-section mapped on the dispersion chamber component 3 for the set cross-section passes through the radial cross-section of the rotor 10. Through this setting, a set cross-section that can reflect the flow characteristics of the flow field can be obtained.

[0156] S21, determine the optimal geometric model of the dispersion chamber component based on the velocity contour map, pressure contour map, particle motion state map, and rotor wall shear force data corresponding to each geometric model of the dispersion chamber component.

[0157] Please combine Figures 13a to 16b , which successively shows the rotor model before optimization and Figures 10 to 12The velocity contour and pressure contour obtained from the rotor model (except for the rotor model, the other parts of the geometric models of each dispersion chamber component are the same). By comparison, Figure 10 The rotor model of Figure 14a and Figure 14b has the best effect, enabling the lithium battery slurry dispersion equipment to provide greater pressure and speed (as shown in

[0158] ), and has a better negative pressure effect. Figures 17 to 20 Please refer to Figures 10 to 12 which successively shows the particle motion state diagrams obtained based on the rotor model before optimization and the rotor model of Figure 10 . By comparison, the particle motion state diagram obtained based on the rotor model of Figure 18 has the best effect. In the optimized dispersion chamber component, the number of particles staying is significantly reduced (as shown in

[0159] ), that is, the feeding is faster and the dispersion efficiency is improved. Figures 21 to 24 Please refer to Figures 10 to 12 which successively shows the average values of the rotor wall shear force obtained based on the rotor model before optimization and the rotor model of

[0160] . By comparison, the average values of the rotor wall shear force of the optimized dispersion chamber component are 107.75494, 125.19491, and 114.81726 respectively, all of which are higher than the average value of the rotor wall shear force of 102.9554 of the dispersion chamber component before optimization, that is, the dispersion effect is improved. Figure 10 The rotor model of Figure 11 and Figure 12 has better effects in terms of the velocity contour, pressure contour, and particle motion state diagram. Although the increase amplitude of the average value of the rotor wall shear force is not as high as that of the rotor models of Figure 10 , it still has a certain increase compared with the rotor model before optimization. Considering comprehensively, the overall performance of the rotor model of

[0161] is better. Therefore, the geometric model of the dispersion chamber component corresponding to this rotor model can be determined as the optimal geometric model of the dispersion chamber component.The above optimization method for the lithium battery slurry dispersion device based on CFD-DPM coupled simulation first establishes several different geometric models of the dispersion chamber assembly based on the key structural parameters of the dispersion chamber assembly of the dispersion device, and extracts the flow field region model from the geometric models of the dispersion chamber assembly; then, determines the flow field control equation of the flow field region model, and determines the turbulence model and the reference frame model according to the key operating parameters of the dispersion device, the slurry properties and the flow field control equation; then, a steady-state operation is carried out. Then, the flow field obtained from the steady-state operation is used as the initial flow field and combined with the particle parameters and the determined motion control equation of the particles to perform transient CFD-DPM coupled solution, and the simulation results can be obtained. After the simulation results of each geometric model of the dispersion chamber assembly are obtained, post-processing is performed respectively to obtain the velocity cloud diagram and pressure cloud diagram at the set section, the particle motion state diagram and the rotor wall shear force data. Then, the optimal geometric model of the dispersion chamber assembly can be determined based on the velocity cloud diagram and pressure cloud diagram, the particle motion state diagram and the rotor wall shear force data corresponding to each geometric model of the dispersion chamber assembly. In this way, the engineering problem of optimizing the structure of the lithium battery slurry dispersion device is realized through the combined analysis of computational fluid dynamics and discrete phase method. The implementation method is simpler and more efficient, and can effectively reduce the time and money spent on optimizing the structure of the lithium battery slurry dispersion device. Moreover, the embodiment of the present application dynamically simulates the flow and dispersion process of the slurry inside the device. Based on the velocity cloud diagram and pressure cloud diagram, technicians can understand the specific situation of the flow characteristics of the slurry in the dispersion chamber of the lithium battery slurry dispersion device. Based on the particle motion state diagram, they can understand the specific situation of the particle distribution. Based on the rotor wall shear force data, they can understand the improvement of the rotor wall shear force. The embodiment of the present application considers the problem of the dispersion of the lithium battery slurry dispersion device affected by multi-factor coupling, combines computational fluid dynamics (CFD) with discrete phase method (DPM), and establishes a multi-scale simulation model, which can simultaneously simulate the macroscopic flow behavior of the slurry and the microscopic particle dispersion situation.

[0162] Based on the same inventive concept, the embodiment of the present application also provides an optimization device for a lithium battery slurry dispersion device. The device includes a module for executing the method as described above.

[0163] Figure 25 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 25As shown in the figure, the computer device may include: a processor 301 and a memory 302. The memory 302 is connected to the processor 301. The memory 302 is used to store a computer program, and the processor 301 is used to call the computer program to enable the computer device to execute the method described in the above embodiments. In addition, the above computer device may further include: at least one communication bus 303. Among them, the communication bus 303 is used to realize the connection and communication between components. The memory 302 may be a high-speed RAM memory or a non-volatile memory, etc., such as at least one disk memory.

[0164] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, and the computer program is adapted to be loaded and executed by a processor to execute the method described in the above embodiments.

[0165] An embodiment of the present application further provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the electronic device to execute the method described in the above embodiments.

[0166] It should be understood that in the embodiments of the present application, the so-called processor may be a central processing module (Central Processing Unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0167] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by hardware related to computer program instructions. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM), etc.

[0168] The above-disclosed are only the preferred examples of the present application and cannot be used to limit the scope of rights of the present application. Therefore, all equivalent changes made according to the claims of the present application fall within the scope covered by the present application.

Claims

1. A lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation, characterized in that: include: A plurality of different geometric models of dispersion chamber components are established based on key structural parameters of the dispersion chamber component of the dispersion device, wherein the dispersion chamber component includes a dispersion chamber and a rotor and a stator located in the dispersion chamber, the dispersion chamber is provided with a slurry inlet, a slurry outlet and a powder inlet, and the key structural parameters at least include rotor structural parameters; Extracting a flow field region model from the dispersion chamber component geometric model; Determining the flow field control equation of the flow field regional model; Determining a turbulence model and a reference frame model according to key operating parameters of the dispersion device, slurry properties and the flow field control equations, wherein the key operating parameters include rotor speed; Performing steady-state calculations according to flow field boundary conditions to obtain steady-state calculation results of the initial flow field as a transient CFD-DPM coupling solution, wherein the flow field boundary conditions include a slurry inlet velocity and a slurry outlet velocity; Set the physical parameters of the particles; Determine the governing equations of motion for the particles; Based on the initial flow field, the physical parameters of the particles, the velocity of the particles at the powder inlet, the flow field control equation, the motion control equation, the turbulence model and the reference frame model, the time step of the transient CFD-DPM coupling solution is determined according to the set dispersion time, and the transient CFD-DPM coupling solution is performed to obtain the simulation result; Post-processing the simulation results based on the geometric models of each dispersion chamber component to obtain velocity cloud maps and pressure cloud maps, particle motion state maps and rotor wall shear force data at a set cross section; Based on the velocity cloud map, pressure cloud map, particle motion state map and rotor wall shear force data corresponding to each of the dispersion chamber component geometric models, the optimal dispersion chamber component geometric model is determined.

2. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The rotor includes a rotating shaft portion, a plurality of turbine blades arranged on the rotating shaft portion, and a turntable portion coaxially connected to the rotating shaft portion, wherein the turntable portion is provided with a circle of rotor dispersion teeth arranged at circumferential intervals; the stator includes a circle of stator dispersion teeth coaxially arranged with the circle of rotor dispersion teeth; the rotor structural parameters include at least one of the number of the rotor dispersion teeth, the gap width between adjacent rotor dispersion teeth, the gap width between the rotor and the stator, the shape of the turbine blades, and the number of the turbine blades.

3. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The extracting of the flow field region model from the dispersion chamber component geometric model comprises: Simplifying the geometric model of the dispersion chamber component to obtain a simplified geometric model; The flow field region model is extracted from the simplified geometric model through volume extraction and Boolean operation.

4. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: After extracting the flow field area model from the dispersion chamber component geometric model, the method further includes: Meshing the flow field area model, and encrypting the rotor area mesh therein to obtain a mesh file of the flow field area model; The steady-state operation is performed based on the grid file, the key operating parameters, the slurry properties, the flow field boundary conditions, the flow field control equations, the turbulence model, and the reference frame model.

5. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The flow field control equations include mass conservation equations and momentum conservation equations; The mass conservation equation is: ； The momentum conservation equation is: ； in, is the fluid density, t For time, u , v , w are the flow velocities at any point in the flow field. V The local velocity component of x , y , z are the spatial directions respectively, p is the pressure in the fluid microelement, is the viscous stress acting on the surface of the microelement, for Gravity body force in direction, for External body forces in the direction.

6. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The slurry properties include slurry composition, slurry solid content, slurry viscosity and slurry density.

7. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The motion control equations include translation equations and rotation equations; The translation equation is: ； The rotation equation is: ； in is the mass of particle i, is the velocity vector of particle i, is the acceleration vector of particle i, g is the gravitational acceleration, is the drag force, is the pressure gradient force, is the particle collision force, is the force exerted by the fluid on the particle, For other forces; is the moment of inertia of particle i; is the angular velocity vector of particle i; is the angular acceleration vector of particle i; is the total moment vector acting on particle i.

8. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The physical parameters of the particles include Poisson's ratio, solid density, shear modulus, Young's modulus and diameter.

9. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The transient CFD-DPM coupling solution includes: (1) Perform continuous phase flow field calculation to obtain basic flow field information; (2) performing discrete phase calculations using the motion control equations according to the external forces acting on the particles in the continuous phase flow field; (3) updating the position of particles in the continuous phase flow field; (4) determining whether the result of the continuous phase flow field calculation and the result of the discrete phase calculation are both converged; If the judgment result is no, return to step (1); If the judgment result is yes, the calculation of the current time step is ended.

10. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The time step is determined based on the rotation speed of the rotor, the duration for each rotation of the rotor by a set angle is one time step, and the dispersion time is equal to the sum of all time steps.

11. The lithium battery slurry dispersion equipment optimization method according to claim 1, characterized in that: The slurry inlet is located at the bottom end of the dispersion chamber and opens downward, the powder inlet is located at the top end of the dispersion chamber and opens upward, and the slurry inlet, the powder inlet and the rotor are coaxially arranged; The set cross section maps a cross section on the dispersion chamber assembly through a radial cross section of the rotor.

12. A lithium battery slurry dispersion equipment optimization device based on CFD-DPM coupling simulation, characterized in that: Comprising means for performing the method of any one of claims 1 to 11.

13. A computer device, characterized in that: including memory and processor; The memory is connected to the processor, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the method according to any one of claims 1 to 11.

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