Lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation

Abstract

The invention discloses a method for optimizing lithium battery slurry dispersion equipment based on CFD-DPM coupling simulation. The method comprises the following steps: establishing a plurality of different geometric models of the dispersion cavity assembly; extracting a flow field area model from the geometric model of the dispersion cavity assembly; determining a flow field control equation of the flow field area model; determining a turbulence model and a reference system model; performing steady-state operation to obtain an initial flow field; setting physical parameters of the particles; determining a motion control equation of the particles; performing transient solution to obtain a simulation result; performing post-processing on each simulation result to obtain a speed cloud picture and a pressure cloud picture of a set section, a particle motion state diagram and rotor wall surface shear force data; and determining an optimal geometric model of the dispersion cavity assembly. According to the invention, the time and money for optimizing the structure of the lithium battery slurry dispersing equipment can be effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of CFD-DPM coupling simulation, and in particular to a method for optimizing lithium battery slurry dispersion equipment based on CFD-DPM coupling simulation. Background Art

[0002] In recent years, my country's new energy vehicle industry has continued to develop. As the core part of new energy vehicles, the performance of lithium batteries is directly related to the mileage, safety performance, and use cost of new energy vehicles. Lithium-ion batteries have the advantages of high specific energy, long charge and discharge life, fast charging speed, and high safety performance, and are more suitable as power batteries for new energy vehicles. In the process of lithium battery pulping, in order to ensure the consistency and sedimentation stability of lithium battery slurry, high-speed dispersing equipment is required to disperse the slurry.

[0003] Existing high-speed dispersing equipment often has problems such as difficulty in dispersing powder and poor dispersing effect. Therefore, it is necessary to optimize the high-speed dispersing equipment. At present, the structure of the high-speed dispersing equipment is generally optimized through experimental means, that is, the structure of the high-speed dispersing equipment is actually changed, such as the rotor structure, and then the high-speed dispersing equipment with the changed structure is used to actually disperse the slurry, and then the actual effect is judged based on the dispersion results, which takes 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 dispersing equipment, and it is difficult to consider the simultaneous action of multiple factors.

[0004] Based on this, this application proposes a lithium battery slurry dispersion equipment optimization solution based on CFD-DPM coupling simulation. Summary of the invention

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

[0006] In view of this, in a first aspect, an embodiment of the present application provides a lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation, comprising: 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.

[0007] Optionally, 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, the turntable portion being provided with a circle of rotor dispersion teeth arranged circumferentially at 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.

[0008] Optionally, extracting the flow field region model from the dispersion chamber component geometric model includes: 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.

[0009] Optionally, after extracting the flow field region model from the dispersion chamber component geometric model, the method further includes: meshing the flow field region model and encrypting the rotor region mesh therein to obtain a mesh file of the flow field region 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.

[0010] Optionally, the fluid control equation includes a mass conservation equation and a momentum conservation equation; 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.

[0011] Optionally, the slurry properties include slurry composition, slurry solid content, slurry viscosity and slurry density.

[0012] Optionally, the motion control equation includes a translation equation and a rotation equation; 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.

[0013] Optionally, the physical parameters of the particles include Poisson's ratio, solid density, shear modulus, Young's modulus and diameter.

[0014] Optionally, 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.

[0015] Optionally, 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.

[0016] Optionally, 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, the slurry inlet, the powder inlet and the rotor are coaxially arranged; the set cross-section is mapped to a cross-section on the dispersion chamber assembly passing through a radial cross-section of the rotor.

[0017] In a second aspect, an embodiment of the present application further provides a lithium battery slurry dispersion equipment optimization device based on CFD-DPM coupling simulation, comprising a module for executing any of the methods described in the first aspect.

[0018] In a third aspect, an embodiment of the present application further provides a computer device, including a memory and a 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 any one of the methods in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing any one of the methods in the first aspect.

[0020] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0021] The above-mentioned lithium battery slurry dispersion equipment optimization method, device, computer equipment, computer readable storage medium and computer program product based on CFD-DPM coupling simulation first establish several different dispersion chamber component geometric models based on the key structural parameters of the dispersion chamber component of the dispersion equipment, and extract the flow field area model from the dispersion chamber component geometric model; then, determine the flow field control equation of the flow field area model, and determine the turbulence model and reference frame model according to the key operating parameters, slurry properties and flow field control equation of the dispersion equipment; then perform steady-state operation. Then, the flow field obtained by the steady-state operation is used as the initial flow field and combined with the particle parameters and the determined particle motion control equation to perform transient CFD-DPM coupling solution, and then the simulation results can be obtained. After the simulation results of each dispersion chamber component geometric model are obtained, post-processing is performed respectively to obtain the velocity cloud map and pressure cloud map, particle motion state map and rotor wall shear force data at the set cross section, and then the optimal dispersion chamber component geometric model can be determined based on the velocity cloud map and pressure cloud map, particle motion state map and rotor wall shear force data corresponding to each dispersion chamber component geometric model. In this way, the engineering problem of structural optimization of lithium battery slurry dispersion equipment is solved by combining computational fluid dynamics and discrete phase method for analysis. The implementation method is simpler and more efficient, and can effectively reduce the time and money spent on structural optimization of lithium battery slurry dispersion equipment. Moreover, the embodiment of the present application dynamically simulates the flow and dispersion process of the slurry inside the equipment. The technician can understand the specific flow characteristics of the slurry in the dispersion chamber of the lithium battery slurry dispersion equipment based on the velocity cloud map and the pressure cloud map, and can understand the specific situation of the particle distribution based on the particle motion state diagram. Based on the rotor wall shear force data, the improvement of the rotor wall shear force can be understood. The embodiment of the present application takes into account the dispersion problem of lithium battery slurry dispersion equipment affected by multi-factor coupling, combines computational fluid dynamics (CFD) with discrete phase method (DPM), and establishes a multi-scale simulation model that can simultaneously simulate the macroscopic flow behavior of the slurry and the microscopic particle dispersion. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is the geometric model of the lithium battery slurry dispersion equipment in the embodiment of the present application.

[0023] Figure 2 It is a schematic diagram of the cross-sectional structure of the geometric model of the dispersion chamber component of the embodiment of the present application.

[0024] Figure 3 It is a schematic cross-sectional structure diagram of the geometric model of the dispersion chamber component of the embodiment of the present application from another perspective.

[0025] Figure 4 This is a flow chart of a lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation in an embodiment of the present application.

[0026] Figure 5 This is a simplified geometric model obtained after simplifying the geometric model of the dispersion chamber component of the embodiment of the present application.

[0027] Figure 6 This is the flow field area model extracted in the embodiment of the present application.

[0028] Figure 7 This is the rotor model before simplification of the embodiment of the present application.

[0029] Figure 8 This is a simplified rotor model of the embodiment of the present application.

[0030] Fig. 9 This is a schematic diagram of a mesh file obtained after meshing a simplified geometric model according to an embodiment of the present application.

[0031] Figures 10 to 12 They are three simplified examples of the rotor model of the dispersion chamber component geometric model obtained by changing the rotor structural parameters in the present application.

[0032] Fig.13a This is a pressure cloud diagram obtained based on the original rotor model (before changing the rotor structural parameters) in the embodiment of the present application.

[0033] Fig.13b This is a velocity cloud diagram obtained based on the original rotor model in the embodiment of the present application.

[0034] Fig.14a Based on Fig.10 Pressure contour obtained for the mid-rotor model.

[0035] Fig.14b Based on Fig.10 Velocity contour obtained from the mid-rotor model.

[0036] Fig.15a Based on Fig.11 Pressure contour obtained for the mid-rotor model.

[0037] Fig.15b Based on Fig.11 Velocity contour obtained from the mid-rotor model.

[0038] Fig.16a Based on Fig.12 Pressure contour obtained for the mid-rotor model.

[0039] Fig.16b Based on Fig.12 Velocity contour obtained from the mid-rotor model.

[0040] Fig.17 This is a particle motion state diagram obtained based on the original rotor model in an embodiment of the present application.

[0041] Fig.18 Based on Fig.10 Particle motion state diagram obtained by the mesorotor model.

[0042] Fig.19 Based on Fig.11 Particle motion state diagram obtained by the mesorotor model.

[0043] Fig. 20 Based on Fig.12 Particle motion state diagram obtained by the mesorotor model.

[0044] Fig.21 It is the average value of the rotor wall shear force obtained in the embodiment of the present application based on the original rotor model.

[0045] Fig. 22 Based on Fig.10 Average value of rotor wall shear force obtained for the mid-rotor model.

[0046] Fig.23 Based on Fig.11 Average value of rotor wall shear force obtained for the mid-rotor model.

[0047] Fig.24 Based on Fig.12 Average value of rotor wall shear force obtained for the mid-rotor model.

[0048] Fig.25 A schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The embodiment of the present application discloses a lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation. The method is used to optimize the dispersion chamber component of the lithium battery slurry dispersion equipment.

[0051] See also Figures 1 to 3The dispersion chamber assembly 3 includes a dispersion chamber 11 and 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, enters the narrow gap between the rotor 10 and the stator 9, and is subjected to strong shearing, friction, impact and other effects to achieve the purpose of mixing, dispersing and refining.

[0052] Specifically, the main material and liquid material in the raw material of lithium battery slurry can be mixed in the slurry stirring tank (not shown) and then enter the dispersion chamber 11 through the slurry inlet 7. Further, the slurry can be circulated and stirred in the dispersion equipment and the stirring tank until the viscosity of the slurry reaches the target viscosity, thereby obtaining a slurry that meets the requirements.

[0053] In a specific example, 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, 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 component 3, and the dispersion chamber component 3 is not limited thereto.

[0054] Specifically, please combine Figure 2 and Figure 3 and Figure 7 The rotor includes a rotating shaft portion 101, a plurality of turbine blades 102 disposed on the rotating shaft portion 101, and a turntable portion 103 coaxially connected to the rotating shaft portion 101, and a circle of rotor dispersion teeth 104 arranged at intervals in the circumferential direction is provided on the turntable portion 103; the stator 9 includes a circle of stator dispersion teeth 91 coaxially disposed with the circle of rotor dispersion teeth 104. Since the rotor includes the turbine blades 102, the slurry can be fully stirred.

[0055] The narrow gap between the rotor 10 and the stator 9 refers to the gap 14 between the rotor dispersed teeth 104, the gap 15 between the stator dispersed 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 gap between the rotor dispersed teeth 104 and the stator dispersed teeth 91 in the radial direction.

[0056] Figure 1 A schematic diagram of an example of a lithium battery slurry dispersing device is given. In this example, the dispersing device includes a mounting bracket 4, on which a dispersing chamber assembly 3 and a driving assembly 1 are provided, and the driving assembly 1 is used to drive a rotor 10 to rotate. Specifically, one end of the dispersing chamber 11 provided with a slurry inlet 7 is sealed and mounted on the mounting bracket 4 through a connector 12.

[0057] See also Figure 4The lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation in the embodiment of the present application includes the following steps: S11, establishing a number of different dispersion chamber component geometric models based on key structural parameters of the dispersion chamber component of the dispersion device, wherein the key structural parameters at least include rotor structural parameters.

[0058] Key structural parameters refer to structural parameters that have a relatively greater impact on the performance of the dispersion chamber assembly. The rotor is the main functional component of the dispersion chamber assembly, and the relevant structural parameters of the rotor will undoubtedly have a greater impact on the performance of the dispersion chamber assembly. Therefore, the embodiment of the present application uses the rotor structural parameters as key structural parameters for optimization.

[0059] By changing the key structural parameters of the dispersion chamber component to be optimized, several different dispersion chamber component geometric models can be established to select the optimal dispersion chamber component geometric model. For example, the same key structural parameter can be changed differently, or different key structural parameters can be changed, etc., as long as different dispersion chamber component geometric models are obtained based on the adjustment of the key structural parameters.

[0060] Please combine Figure 2 and Figure 3 and Figure 7 In some embodiments, the rotor 10 includes a shaft portion 101, a plurality of turbine blades 102 disposed on the shaft portion 101, and a turntable portion 103 coaxially connected to the shaft portion 101, and a circle of rotor dispersion teeth 104 arranged at intervals in the circumferential direction is provided 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 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 distance between the rotor dispersion teeth 104 and the stator dispersion teeth 91 in the radial direction. Specifically, the key structural parameters also include at least one of the number of stator dispersion teeth 91 and the gap width between adjacent stator dispersion teeth 91.

[0061] Figures 10 to 12 An example of changing the rotor structural parameters to obtain different dispersion chamber component geometric models is given, which shows the simplified model of the rotor model in each dispersion chamber component geometric model. In this example, Figures 10 to 12 The rotor models of the present invention respectively make different changes to the shape of the turbine blades of the rotor, increasing the number of a group of circumferentially distributed turbine blades 102 from 5 to 6, and increasing the number of rotor dispersion teeth 104 from 10 to 12, thereby reducing the gap width between adjacent rotor dispersion teeth 104.

[0062] In some embodiments, a geometric model of the dispersion chamber component may be established in the three-dimensional modeling software Solidworks.

[0063] S12, extract the flow field area model (such as Figure 6 as shown).

[0064] In some embodiments, extracting the flow field region model from the dispersion chamber component geometric model includes: The geometric model of the dispersed cavity component is simplified to obtain a simplified geometric model (such as Figure 5 shown); The flow field region model is extracted from the simplified geometric model through volume extraction and Boolean operations.

[0065] Specifically, the geometric model of the dispersion chamber component is imported into the Space Claim function module of ANSYS (computer-aided engineering software, also known as CAE software) to simplify the model, and extract the flow field area model that needs to be calculated. In one embodiment, the simplified front rotor model is as follows: Figure 7 As shown in the figure, the features such as connectors, holes, and fillets that are not related to the simulation analysis are deleted, and the simplified rotor model is obtained as shown in the figure. Figure 8 After obtaining the simplified geometric model, the flow field area model is extracted from it as shown in Figure 6 As shown, it is divided into a stationary domain model 16 and a rotating domain model 17.

[0066] S13, meshing the flow field area model and encrypting the rotor area mesh therein to obtain mesh files of each flow field area model. Figure 6 After meshing the flow field area model, the schematic diagram of the obtained mesh file is as follows Fig. 9 As shown, it is divided into a stationary domain grid 18 and a rotating domain grid 19.

[0067] Specifically, the rotor area grid is encrypted, including: In the rotor area, the mesh is encrypted using the unstructured tetrahedral mesh method.

[0068] The flow field area model extracted after simplification in the ANSYS Space Claim module can be imported into the Mesh module of ANSYS. The flow field area model is divided into finite element grids by the unstructured tetrahedron method of the Mesh module of ANSYS, and the local area of ​​the rotor (rotor area grid) in the flow field area model is encrypted to obtain the grid file corresponding to each flow field area model, while ensuring the grid quality and calculation accuracy.

[0069] Furthermore, the local area of ​​the rotor of each geometric model is encrypted to obtain n computational domain meshes with different numbers of meshes, and the mesh independence is verified, and 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 and the number of meshes is 2.7 million, the best is to select the computational domain mesh with the number of meshes of 2.7 million.

[0070] Specifically, the local area of ​​the rotor refers to the flow field area within a set range (smaller range) from the rotor wall. The outer boundary of this range can be set to 1~5mm from the rotor wall, and the inner boundary is the rotor wall. The rotor is set as a wall. When simulating the flow, the boundary layer close to the wall in the flow field is generally encrypted to make the flow field more accurate and adapt to the complex geometric shape of the rotor.

[0071] It should be noted that, for the present application, step S13 is not a necessary step, that is, before determining the flow field control equation of the flow field area model, the above step of obtaining the grid file of the flow field area model may not be performed.

[0072] S14, determining the flow field control equation of the flow field regional model.

[0073] Specifically, the fluid control equations include the mass conservation equation and the momentum conservation equation.

[0074] 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.

[0075] S15, determining a turbulence model and a reference frame model according to key operating parameters of the dispersion device, slurry properties and flow field control equations, wherein the key operating parameters include rotor speed. The slurry properties are material properties of the slurry.

[0076] Specifically, slurry properties include slurry composition, slurry solid content, slurry viscosity and slurry density.

[0077] In one embodiment, the viscosity variation 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.

[0078] For example, the rotor speed can be set to 2000 rpm, the lithium battery slurry is set according to the material properties of the solvent NMP in the dispersion process, and the density is set to 1000 kg / m 3 , the viscosity is set to 1.65 mPa·s. Based on the corresponding values ​​and the determined flow field control equations, the appropriate turbulence model and reference frame model are selected.

[0079] Turbulence models include k-ε model, k-ω model, Reynolds stress model, etc. Different turbulence models take different solution times and have different accuracy. Among them, the standard k-ε model is widely used and has moderate computational complexity.

[0080] When determining the turbulence model, the Reynolds number can be used to determine it. Reynolds number Re<2000 is a laminar model, and Reynolds number Re>4000 is a turbulent model. The Reynolds number calculation formula is Re=ρND² / μ, Re is the Reynolds number, ρ is the density, N is the speed, D is the rotor outer diameter, and μ is the viscosity. In one example, ρ is 1000kg / m3, N is 2000rpm, D is 140mm, and μ is 0.00165paꞏs. The calculated Reynolds number is>4000, so the turbulence model is selected.

[0081] Reference frame models include multiple reference frame models, sliding grids, etc. Among them, the multiple reference frame model is generally suitable for steady-state analysis, and the sliding grid model is generally suitable for transient analysis.

[0082] In one embodiment, the turbulence model is determined to be a standard k-ε model, and the reference frame model is determined to be a multiple reference frame model.

[0083] S16, performing steady-state calculations according to the 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.

[0084] The transient solution depends on the selection of initial values, so a steady-state calculation is performed first to obtain the initial flow field and then a transient solution is performed based on the initial flow field.

[0085] Specifically, steady-state operations are performed based on grid files, key operating parameters, slurry properties, flow field boundary conditions, flow field control equations, turbulence models, and reference frame models.

[0086] 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.

[0087] Specifically, the slurry properties involved in steady-state calculations may include slurry viscosity and slurry density.

[0088] S17, setting the physical parameters of the particles (powder particles).

[0089] In order to perform transient CFD-DPM coupling 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.

[0090] In one embodiment, the particle diameter is set to 0.1 mm.

[0091] S18, determine the particle motion control equation.

[0092] After determining the physical parameters of the particles in the dispersion chamber, the motion control equations applicable to the particles can be selected.

[0093] Specifically, the motion control equations include translation equations and rotation equations.

[0094] 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.

[0095] S19, 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 equations, the motion control equations, 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 results.

[0096] Specifically, the particle velocity at the powder inlet is set to 0.009 m / s.

[0097] Specifically, the turbulence model and reference frame model used in the transient CFD-DPM coupling solution are the same as the turbulence model and reference frame model used in the steady-state operation. In one embodiment, the turbulence model is a standard k-ε model, and the reference frame model is a multiple reference frame model.

[0098] Specifically, the transient CFD-DPM coupling solution includes: Carry out continuous phase flow field calculation to obtain basic flow field information.

[0099] Based on the external forces acting on particles in the continuous phase flow field, the discrete phase calculation is performed using the motion control equation. That is, the discrete phase (particles) entering the flow field from the particle injection source are calculated. The external forces acting on the particles include drag force, the force of the fluid on the particles, gravity, etc.

[0100] Update the position of particles in the continuous phase flow field.

[0101] Determine whether the results of the continuous phase flow field calculation and the discrete phase calculation are convergent.

[0102] If the judgment result is no, return to the step of continuous phase flow field calculation to repeat the above steps.

[0103] If the judgment result is yes, the calculation of the current time step is ended.

[0104] It can be understood that if the results of the continuous phase flow field calculation and the discrete phase calculation are converged within the current time step, the solution results can be directly saved as simulation results. Of course, the calculation of subsequent time steps can also be continued, and then the corresponding solution results can be saved as simulation results as needed.

[0105] When each equation is numerically calculated iteratively, the difference between the result after calculation and the result obtained in the previous calculation, that is, the residual, is lower than the set threshold, then the calculation is considered to have converged. In one embodiment, the residual can be set to 0.001.

[0106] Specifically, the time step is determined based on the rotation 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.

[0107] For example, the time length for the rotor to rotate 1° may be regarded as a time step.

[0108] For example, when the rotor speed is 2000 rpm, a time step should be set to 0.0005s. Considering the amount of calculation, the actual time step is set to 0.01s. To simulate the flow within 1s, 100 time steps are set. 20 iterative calculations are set in each time step. The particles enter the dispersion chamber from the powder inlet within 0s to 0.5s, simulating the particle movement from 0s to 1s.

[0109] S20, post-processing the simulation results based on the geometric models of each dispersion chamber component, respectively, to obtain a velocity cloud map and a pressure cloud map at a set cross section, a particle motion state map, and rotor wall shear force data.

[0110] After each dispersion chamber component geometric model completes the above simulation process to obtain the corresponding simulation results, the simulation results can be post-processed to obtain the velocity cloud map and pressure cloud map at the set cross section, the particle motion state diagram and the rotor wall shear force data.

[0111] 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.

[0112] See also 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, the powder inlet 5 and the rotor 10 are coaxially arranged; the section mapped on the dispersion chamber assembly 3 passes through the radial section of the rotor 10. Through this arrangement, a set section that reflects the flow characteristics of the flow field can be obtained.

[0113] S21, determining the optimal geometric model of the dispersion chamber component based on the velocity cloud map, pressure cloud map, particle motion state map and rotor wall shear force data corresponding to each dispersion chamber component geometric model.

[0114] Please combine Figures 13a to 16b , which shows the optimized rotor model and Figures 10 to 12 The velocity and pressure cloud diagrams are obtained from the rotor model (except for the rotor model, the other parts of the geometric model of each dispersion chamber component are the same). By comparison, we know that Fig.10 The rotor model has the best effect, which enables the lithium battery slurry dispersion equipment to provide greater pressure and speed (such as Fig.14a and Fig.14b As shown), it has a better negative pressure effect.

[0115] Please combine Figures 17 to 20 , which shows the optimized rotor model and Figures 10 to 12 The particle motion state diagram obtained by the rotor model. By comparison, we know that based on Fig.10The particle motion state diagram obtained by the rotor model is the best. In the optimized dispersion chamber component, the number of particles staying is significantly reduced (such as Fig.18 As shown in the figure), the material feeding is faster and the dispersion efficiency is improved.

[0116] Specifically, the rotor wall shear force data refers to the average value of the rotor wall shear force. Figure 21 to Figure 24 , which shows the optimized rotor model and Figures 10 to 12 The average shear force of the rotor wall is obtained by the rotor model. By comparison, the average shear force of the rotor wall of the optimized dispersion chamber assembly is 107.75494, 125.19491, and 114.81726, respectively, which is higher than the average shear force of the rotor wall of the dispersion chamber assembly before optimization, which is 102.9554, that is, the dispersion effect is improved.

[0117] Comprehensively consider the velocity cloud map, pressure cloud map, particle motion state map and rotor wall shear force data corresponding to each optimized rotor model. Fig.10 The rotor model of the proposed method has better results in terms of velocity cloud diagram, pressure cloud diagram and particle motion state diagram, but the improvement in the average value of rotor wall shear force is not as good as that of the proposed method. Fig.11 and Fig.12 The rotor model is still improved compared with the rotor model before optimization. Fig.10 The overall performance of the rotor model is better, so the dispersed cavity component geometric model corresponding to this rotor model can be determined as the optimal dispersed cavity component geometric model.

[0118] The above-mentioned lithium battery slurry dispersion equipment optimization method based on CFD-DPM coupling simulation first establishes several different dispersion chamber component geometric models based on the key structural parameters of the dispersion chamber component of the dispersion equipment, and extracts the flow field area model from the dispersion chamber component geometric model; then, the flow field control equation of the flow field area model is determined, and the turbulence model and the reference frame model are determined according to the key operating parameters of the dispersion equipment, the slurry properties and the flow field control equation; then the steady-state operation is performed. Then the flow field obtained by the steady-state operation is used as the initial flow field and combined with the particle parameters and the determined particle motion control equation to perform transient CFD-DPM coupling solution, and then the simulation results can be obtained. After the simulation results of each dispersion chamber component geometric model are obtained, post-processing is performed respectively to obtain the velocity cloud map and pressure cloud map, particle motion state map and rotor wall shear force data at the set cross section, and then the optimal dispersion chamber component geometric model can be determined based on the velocity cloud map and pressure cloud map, particle motion state map and rotor wall shear force data corresponding to each dispersion chamber component geometric model. In this way, the engineering problem of structural optimization of lithium battery slurry dispersion equipment is solved by combining computational fluid dynamics and discrete phase method for analysis. The implementation method is simpler and more efficient, and can effectively reduce the time and money spent on structural optimization of lithium battery slurry dispersion equipment. Moreover, the embodiment of the present application dynamically simulates the flow and dispersion process of the slurry inside the equipment. The technician can understand the specific flow characteristics of the slurry in the dispersion chamber of the lithium battery slurry dispersion equipment based on the velocity cloud map and the pressure cloud map, and can understand the specific situation of the particle distribution based on the particle motion state diagram. Based on the rotor wall shear force data, the improvement of the rotor wall shear force can be understood. The embodiment of the present application takes into account the dispersion problem of lithium battery slurry dispersion equipment affected by multi-factor coupling, combines computational fluid dynamics (CFD) with discrete phase method (DPM), and establishes a multi-scale simulation model that can simultaneously simulate the macroscopic flow behavior of the slurry and the microscopic particle dispersion.

[0119] Based on the same inventive concept, the embodiment of the present application also provides a lithium battery slurry dispersion equipment optimization device, which includes a module for executing the above method.

[0120] Fig.25 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Fig.25As shown, the computer device may include: a processor 301 and a memory 302. The memory 302 is connected to the processor 301, and the memory 302 is used to store a computer program. The processor 301 is used to call the computer program so that the computer device executes the method described in the above embodiment. In addition, the above computer device may also 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 can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory.

[0121] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. The computer program is suitable for being loaded by a processor and executing the method described in the above embodiment.

[0122] The present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the electronic device executes the method described in the above embodiment.

[0123] It should be understood that in the embodiments of the present application, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by hardware related to computer program instructions, and 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-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0125] The above disclosure is only a preferred example of the present application and cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are all 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 fluid control equations include a mass conservation equation and a momentum conservation equation; 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, 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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