CFD simulation-driven rare earth polishing powder jet classification optimization method and system

Abstract

The present invention discloses a CFD simulation-driven rare earth polishing powder jet classification optimization method and system, wherein the method includes: constructing a CFD model according to the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, introducing a turbulence model to simulate the influence of turbulence on particle motion; performing multi-physical field coupling simulation according to the fluid field, particle field and temperature field to obtain simulation results of fluid behavior and particle motion law; setting optimization goals of maximizing classification efficiency and minimizing energy consumption, iterating optimal jet conditions and model parameters; and classifying the rare earth polishing powder jet according to the optimal jet conditions and model parameters. The present invention simulates the changes in fluid behavior, particle motion law and temperature field of rare earth polishing powder in the jet classification process through a CFD simulation method, optimizes the jet classification process of rare earth polishing powder, improves classification efficiency and reduces energy consumption.

Description

Technical Field

[0001] The invention relates to the technical field of fluid mechanics, and in particular to a CFD simulation-driven rare earth polishing powder jet classification optimization method and system. Background Art

[0002] As an important industrial raw material, rare earth polishing powder is widely used in optics, electronics, ceramics and other fields. Its particle size, shape and distribution have a significant impact on the performance and quality of the product. Therefore, accurate classification and screening of rare earth polishing powder to meet the requirements of material performance in different fields is an important link in the production process of rare earth polishing powder.

[0003] Although traditional jet classification technology can achieve the classification of rare earth polishing powder, it faces some challenges in practical application. First, the particle size distribution, density and shape of rare earth polishing powder are complex and changeable, making the classification process difficult to control. Secondly, the jet classification process involves the coupling of multiple physical fields such as fluid field, particle field and temperature field, which increases the complexity and uncertainty of the classification process. In addition, traditional classification methods are often based on experience or trial and error, which makes it difficult to achieve efficient optimization and adjustment.

[0004] Computational fluid dynamics (CFD) is a numerical simulation technology that can simulate and analyze complex physical phenomena such as fluid flow, heat transfer, and mass transfer. In recent years, with the rapid development of computer technology and the popularization of CFD commercial software, CFD simulation technology has been widely used in many fields such as aerospace, chemical industry, automobile, and shipbuilding. However, in the field of rare earth polishing powder jet grading, there is a lack of industry standards and specifications for CFD simulation technology, which increases the difficulty of application. Although there are many mature commercial CFD software on the market, it is difficult to directly meet the specific needs of this field, resulting in limited applicability in the field of rare earth polishing powder jet grading.

[0005] Therefore, developing a CFD simulation-driven rare earth polishing powder jet classification optimization method has become an urgent problem to be solved. Summary of the invention

[0006] In view of the above problems, the present invention is proposed to provide a CFD simulation-driven rare earth polishing powder jet classification optimization method and system that overcomes the above problem of low classification efficiency.

[0007] According to one aspect of the present invention, a CFD simulation-driven rare earth polishing powder jet classification optimization method is provided, comprising:

[0008] A CFD model is constructed based on the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and a turbulence model is introduced to simulate the effect of turbulence on particle movement;

[0009] In the CFD model, multi-physics field coupling simulation is performed according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law;

[0010] Setting the optimization goal of maximizing classification efficiency and minimizing energy consumption, and iteratively calculating the optimal jet conditions and model parameters of the CFD model;

[0011] The rare earth polishing powder jet is graded according to the optimal jet conditions and model parameters of the CFD model.

[0012] In an optional manner, constructing a CFD model and introducing a turbulence model to simulate the effect of turbulence on particle motion further includes:

[0013] The geometry model of the nozzle and jet area is constructed through the Geometry module of ANSYS Fluent, and the calculation domain is set for the simulated area;

[0014] Meshing the computational domain using the Mesh module of ANSYS Fluent;

[0015] Set the inlet boundary conditions, outlet boundary conditions and wall boundary conditions for flow rate and pressure;

[0016] Select the SST k-ω turbulence model according to the Reynolds number, and set the parameters and boundary conditions of the turbulence model in the CFD software;

[0017] Set the particle size distribution, density and shape parameters, as well as the initial position and velocity distribution of the particles, enable the coupling option with the turbulence model, and simulate the movement of rare earth polishing powder particles through the DPM discrete phase model;

[0018] Set the simulation time step and total time to capture the dynamic changes of flow and particle motion; initialize the computational domain and particle field, and simulate the effect of turbulence on particle velocity, trajectory and concentration through the Post-Processing module of ANSYS Fluent.

[0019] In an optional manner, the multi-physics field coupling simulation based on the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process further includes:

[0020] Set the specific heat capacity and thermal conductivity of the fluid, and set the temperature boundary conditions of the inlet temperature and wall temperature;

[0021] Enable energy equations in CFD software to simulate heat exchange between fluid and particles;

[0022] The multi-physics field coupling function is enabled in the Fluent module of ANSYS to couple the fluid field, particle field and temperature field for simulation.

[0023] In an optional manner, the step of calculating the optimal jet conditions and model parameters of the CFD model further includes:

[0024] Creating a CFD simulation process through the script function of the Fluent module in the ANSYS software; wherein the jet conditions and model parameters are read in the CFD simulation process, and the classification efficiency and energy consumption are saved during the simulation;

[0025] The optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm;

[0026] Passing the new jet conditions and model parameters to the CFD simulation process to update the optimization target;

[0027] If the change in the optimization target is less than the preset threshold or the number of iterations reaches the preset number, the iteration is stopped and the optimal jet conditions and model parameters are output; otherwise, the iteration continues.

[0028] In an optional manner, the grading of the rare earth polishing powder jet according to the optimal jet condition and model parameters of the CFD model further comprises:

[0029] According to the optimal jet conditions of flow rate, pressure and nozzle shape and the model parameters, the classification efficiency and energy consumption value of the rare earth polishing powder jet are predicted by the CFD model;

[0030] By comparing the experimental data with the simulation results, it is evaluated whether the classification efficiency and energy consumption values ​​have reached or are close to the optimization target.

[0031] In an optional manner, the method further includes:

[0032] Performing a sensitivity analysis on the temperature field to evaluate the effect of temperature on fractionation efficiency and energy consumption;

[0033] Among them, by changing the inlet temperature and wall temperature, the impact of temperature field changes on fluid behavior and particle movement is evaluated, thereby determining the extent of temperature influence on classification efficiency and energy consumption.

[0034] In an optional manner, the particle swarm optimization algorithm adopts an adaptive weight strategy during the iteration process to balance the two objectives of maximizing classification efficiency and minimizing energy consumption;

[0035] In the early stage of iteration, a higher weight is set for maximizing classification efficiency, and as the iteration proceeds, the weight for minimizing energy consumption is gradually increased until a balance is reached.

[0036] In an optional manner, the method further includes:

[0037] Multiple nozzle shape parameters are set and the jet performance and classification efficiency under different parameters are iteratively calculated to find the optimal nozzle shape parameters.

[0038] In an optional manner, the method further includes:

[0039] The grid independence simulation of the CFD model is verified under different grid densities, the differences in classification efficiency and energy consumption under different grid densities are compared, and the optimal grid density simulation result is selected.

[0040] According to another aspect of the present invention, there is provided a CFD simulation-driven rare earth polishing powder jet classification optimization device, comprising:

[0041] CFD modeling and turbulence simulation module, which is used to build a CFD model based on the particle size distribution, density and shape of rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of rare earth polishing powder particles, and introduce a turbulence model to simulate the effect of turbulence on particle movement;

[0042] A multi-physics field coupling simulation module is used to perform multi-physics field coupling simulation in the CFD model according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle motion law;

[0043] An optimization target setting and iterative calculation module, used to set the optimization target of maximizing classification efficiency and minimizing energy consumption, and iteratively calculate the optimal jet conditions and model parameters of the CFD model;

[0044] The jet classification implementation module is used to classify the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model.

[0045] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus.

[0046] According to the scheme provided by the present invention, a CFD model is constructed according to the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and a turbulence model is introduced to simulate the influence of turbulence on particle movement; in the CFD model, a multi-physical field coupling simulation is performed according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law; the optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and the optimal jet conditions and model parameters of the CFD model are iteratively calculated; the rare earth polishing powder jet is classified according to the optimal jet conditions and model parameters of the CFD model. The present invention simulates the changes in the fluid behavior, particle movement law and temperature field of the rare earth polishing powder in the jet classification process through a CFD simulation method, optimizes the jet classification process of the rare earth polishing powder, improves the classification efficiency and reduces energy consumption.

[0047] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0049] Figure 1 A schematic flow chart of a rare earth polishing powder jet classification optimization method driven by CFD simulation according to an embodiment of the present invention is shown;

[0050] Figure 2 A schematic diagram of the framework of a rare earth polishing powder jet classification optimization device driven by CFD simulation according to an embodiment of the present invention is shown;

[0051] Figure 3 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0053] Figure 1 The schematic diagram of the process of the CFD simulation driven rare earth polishing powder jet classification optimization method of the embodiment of the present invention is shown. Figure 1 As shown, the following steps are included:

[0054] Step S101, constructing a CFD model according to the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and introducing a turbulence model to simulate the influence of turbulence on particle movement.

[0055] In this embodiment, by constructing a CFD (computational fluid dynamics) model to simulate the movement of rare earth polishing powder particles in a specific fluid environment, the particle size distribution, density, shape, flow rate, pressure, nozzle shape and other factors of the particles can be considered to more accurately predict the behavior of the particles in the fluid. The introduction of a turbulence model to simulate the effect of turbulence on particle movement can more realistically simulate the turbulence phenomenon in the fluid, and thus more accurately predict the behavior of particles in turbulence.

[0056] Among them, building a CFD model and introducing a turbulence model to simulate the effect of turbulence on particle motion further includes:

[0057] The geometry model of the nozzle and jet area is constructed through the Geometry module of ANSYS Fluent, and the calculation domain is set for the simulated area;

[0058] Meshing the computational domain using the Mesh module of ANSYS Fluent;

[0059] Set the inlet boundary conditions, outlet boundary conditions and wall boundary conditions for flow rate and pressure;

[0060] Select the SST k-ω turbulence model according to the Reynolds number, and set the parameters and boundary conditions of the turbulence model in the CFD software;

[0061] Set the particle size distribution, density and shape parameters, as well as the initial position and velocity distribution of the particles, enable the coupling option with the turbulence model, and simulate the movement of rare earth polishing powder particles through the DPM discrete phase model;

[0062] Set the simulation time step and total time to capture the dynamic changes of flow and particle motion; initialize the computational domain and particle field, and simulate the effect of turbulence on particle velocity, trajectory and concentration through the Post-Processing module of ANSYS Fluent.

[0063] Specifically, the geometry model was constructed based on the actual nozzle and jet area dimensions using the Geometry module of ANSYS Fluent. The computational domain of the simulation was set to ensure that the computational domain was large enough to include all important fluid dynamics phenomena.

[0064] The computational domain was meshed using the Mesh module of ANSYS Fluent, where finer meshes were used in the critical areas of the nozzle exit and particle motion to capture the details of the flow and particle motion.

[0065] Set inlet boundary conditions for flow rate and pressure, set outlet boundary conditions for pressure outlet or free flow outlet, and set wall boundary conditions (such as no-slip wall or slip wall).

[0066] The SST k-ω turbulence model is selected according to the Reynolds number, and the parameters and boundary conditions of the turbulence model are set in ANSYS Fluent.

[0067] Set the particle size distribution, density, and shape parameters. Set the initial position and velocity distribution of the particles. Enable the coupling option with the turbulence model to consider the effect of turbulence on the particle motion in the simulation.

[0068] The DPM (Discrete Phase Model) model is enabled in ANSYS Fluent to simulate the movement of rare earth polishing powder particles. The parameters of the DPM model include the interaction force between particles and fluid, the collision between particles, etc.

[0069] Set the time step and total time of the simulation. The time step should be small enough to capture the dynamic changes of flow and particle motion. Run the simulation in ANSYS Fluent to observe the dynamic changes of fluid flow and particle motion (you can use the monitor function of ANSYS Fluent to track the changes of key parameters).

[0070] Use ANSYS Fluent’s Post-Processing module to simulate the effects of turbulence on particle velocity, trajectory and concentration, and generate graphs and animations to visualize the results.

[0071] Step S102, performing multi-physical field coupling simulation in the CFD model according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law.

[0072] In this embodiment, multi-physics field coupling simulation is performed on the fluid field, particle field and temperature field, taking into account the interaction between particles and fluid, particles and particles, and fluid and temperature, which can more accurately simulate the complex physical phenomena in the rare earth polishing powder jet classification process. The simulation results include not only basic information such as the velocity, pressure and streamline of the fluid flow, but also key data such as the particle motion trajectory, velocity distribution, and concentration distribution. At the same time, the impact of changes in the temperature field on the behavior of fluids and particles is also included in the simulation range, making the simulation results more comprehensive.

[0073] Wherein, the multi-physics field coupling simulation based on the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process further includes:

[0074] Set the specific heat capacity and thermal conductivity of the fluid, and set the temperature boundary conditions of the inlet temperature and wall temperature;

[0075] Enable energy equations in CFD software to simulate heat exchange between fluid and particles;

[0076] The multi-physics field coupling function is enabled in the Fluent module of ANSYS to couple the fluid field, particle field and temperature field for simulation.

[0077] In this embodiment, the specific heat capacity (Cp) of the fluid describes the amount of heat that needs to be absorbed or released to increase or decrease the unit mass of the substance by 1 degree Celsius. The thermal conductivity (k) of the fluid is a physical quantity that describes the thermal conductivity of the substance. The inlet temperature (Tin) is the temperature of the fluid when it enters the simulation area. The wall temperature (Twall) depends on the actual boundary conditions of the simulation area, such as heating or cooling the wall. In CFD software (such as ANSYS Fluent), the energy equation (Energy Equation) needs to be enabled to simulate the heat transfer in the fluid. The energy equation describes the conservation relationship of heat transfer in the fluid, including convection, conduction, and radiation. If the temperature of the particles is different from that of the fluid, heat exchange will occur between them, which can be achieved by setting the heat exchange coefficient between the particles and the fluid in the DPM (Discrete Phase Model). The temperature change of the particles affects their movement behavior (such as buoyancy, resistance, etc.), so the simulation of heat exchange is crucial for accurately predicting the movement of particles. In the ANSYS Fluent module, the multi-physics coupling function (Multi-Physics Coupling) is enabled, allowing the software to simultaneously consider the interaction between the fluid field, particle field, and temperature field. The simulation is run to observe the dynamic changes of fluid flow, particle movement and temperature distribution. This embodiment realizes the multi-physics field coupling simulation of fluid field, particle field and temperature field in the rare earth polishing powder jet classification process, so as to more accurately predict and understand the physical phenomena in the process.

[0078] Step S103, setting the optimization goal of maximizing the classification efficiency and minimizing the energy consumption, and iteratively calculating the optimal jet conditions and model parameters of the CFD model.

[0079] The steps of calculating the optimal jet conditions and model parameters of the CFD model further include:

[0080] Creating a CFD simulation process through the script function of the Fluent module in the ANSYS software; wherein the jet conditions and model parameters are read in the CFD simulation process, and the classification efficiency and energy consumption are saved during the simulation;

[0081] The optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm;

[0082] Passing the new jet conditions and model parameters to the CFD simulation process to update the optimization target;

[0083] If the change in the optimization target is less than the preset threshold or the number of iterations reaches the preset number, the iteration is stopped and the optimal jet conditions and model parameters are output; otherwise, the iteration continues.

[0084] Specifically, the scripting capabilities of the ANSYS Fluent module (such as UDFs, TUI commands, Journal files, etc.) are used to create an automated CFD simulation process. The script reads the jet conditions and model parameters and then runs the simulation. After the simulation is completed, the script extracts and saves data such as fractional efficiency and energy consumption.

[0085] The optimization goals of maximizing the classification efficiency and minimizing the energy consumption are set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm. Among them, a set of initial jet conditions and model parameters are randomly generated as particles in the particle swarm. Each particle contains a set of parameter values ​​and a corresponding optimization target evaluation value. At the beginning of the iteration, for each particle in the particle swarm, the parameter value of the particle is passed to the CFD simulation process, the simulation is run and the classification efficiency and energy consumption data are obtained, and the optimization target evaluation value of the particle is calculated based on these data. According to the algorithm rules of PSO, the speed and position of the particle are updated to generate a new parameter combination.

[0086] Check whether the change in the optimization target is less than the preset threshold. If so, it is considered that a sufficiently good solution has been found and the iteration is stopped. Check whether the number of iterations has reached the preset maximum number of iterations. If so, the iteration is also stopped. If the stop condition is met, the global optimal solution, that is, the optimal jet conditions and model parameters, is output. By combining CFD simulation and particle swarm optimization algorithm, the optimal jet conditions and model parameters that maximize the classification efficiency and minimize the energy consumption can be automatically searched, which improves the efficiency and accuracy of the optimization process.

[0087] In an optional embodiment, the particle swarm optimization algorithm adopts an adaptive weight strategy during the iteration process to balance the two objectives of maximizing classification efficiency and minimizing energy consumption;

[0088] In the early stage of iteration, a higher weight is set for maximizing classification efficiency, and as the iteration proceeds, the weight for minimizing energy consumption is gradually increased until a balance is reached.

[0089] In this embodiment, the particle swarm optimization algorithm (PSO) adopts an adaptive weight strategy during the iteration process to better balance the two competing goals of maximizing classification efficiency and minimizing energy consumption. In the early stage of the iteration, a higher weight is set for maximizing classification efficiency, and as the iteration proceeds, the weight for minimizing energy consumption is gradually increased until the two goals reach a relatively balanced state.

[0090] Specifically, an initial particle swarm is randomly generated, where each particle represents a set of jet conditions and model parameters, and the initial velocity, position and individual optimal solution are set for each particle.

[0091] Initialize two weight parameters: w_efficiency (for maximizing classification efficiency) and w_consumption (for minimizing energy consumption).

[0092] In the early stages of an iteration, set w_efficiency to a higher value and w_consumption to a lower value.

[0093] A CFD simulation is run for each particle to calculate its corresponding fractionation efficiency and energy consumption. Each particle is evaluated using a fitness function that combines fractionation efficiency and energy consumption and is affected by a weight parameter.

[0094] As the iterations progress, gradually increase the weight of w_consumption while reducing the weight of w_efficiency. You can linearly reduce w_efficiency and linearly increase w_consumption, or use a nonlinear function (such as an exponential function) to more finely control the change of weights.

[0095] Update the particle's speed and position according to the particle's fitness value and the global optimal solution. Update the individual optimal solution of each particle if the new position has a better fitness value.

[0096] Check whether the preset stop conditions are met, such as reaching the maximum number of iterations, the change in fitness value is less than the preset threshold, etc. If the stop conditions are met, the global optimal solution, that is, the optimal jet conditions and model parameters, is output.

[0097] By adopting an adaptive weight strategy, the particle swarm optimization algorithm can focus more on improving classification efficiency in the early stage of iteration, and gradually increase the consideration of energy consumption in the later stage, so as to find a combination of jet conditions and model parameters that is both efficient and energy-saving. It helps to find a balance between multiple conflicting objectives and improve the quality and practicality of the optimization results.

[0098] Step S104, classifying the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model.

[0099] Wherein, the grading of the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model further comprises:

[0100] According to the optimal jet conditions of flow rate, pressure and nozzle shape and the model parameters, the classification efficiency and energy consumption value of the rare earth polishing powder jet are predicted by the CFD model;

[0101] By comparing the experimental data with the simulation results, it is evaluated whether the classification efficiency and energy consumption values ​​have reached or are close to the optimization target.

[0102] For example, experiments were conducted on a rare earth polishing powder jet classification device to record the actual classification efficiency and energy consumption values. This can be achieved by measuring parameters such as the polishing powder output and energy consumption in different particle size ranges after classification. The classification efficiency and energy consumption values ​​predicted by the CFD model were compared with the experimental results to evaluate the accuracy of the simulation and the optimization effect.

[0103] In an optional embodiment, the method further includes:

[0104] Performing a sensitivity analysis on the temperature field to evaluate the effect of temperature on fractionation efficiency and energy consumption;

[0105] Among them, by changing the inlet temperature and wall temperature, the impact of temperature field changes on fluid behavior and particle movement is evaluated, thereby determining the extent of temperature influence on classification efficiency and energy consumption.

[0106] For example, use CFD software such as ANSYS Fluent to set up a base case that uses initial inlet and wall temperatures. Then, create multiple new simulation cases by modifying the inlet and wall temperatures, either through scripts or manually. Run CFD simulations for each new simulation case. Collect data on fractional efficiency and energy consumption from each simulation case. Evaluate the effect of temperature on fractional efficiency and energy consumption using data analysis tools.

[0107] In an optional embodiment, the method further includes:

[0108] Multiple nozzle shape parameters are set and the jet performance and classification efficiency under different parameters are iteratively calculated to find the optimal nozzle shape parameters.

[0109] In an optional embodiment, the method further includes:

[0110] The grid independence simulation of the CFD model is verified under different grid densities, the differences in classification efficiency and energy consumption under different grid densities are compared, and the optimal grid density simulation result is selected.

[0111] For example, choose an initial mesh density (e.g., a coarser mesh) as a starting point.

[0112] Use CFD software (such as ANSYS Fluent, OpenFOAM, etc.) to run simulations on the initial mesh and collect data on the fractional efficiency and energy consumption.

[0113] Increase the mesh density gradually (for example, by decreasing the mesh size or increasing the number of meshes).

[0114] Comparison of simulation results for different mesh densities (difference in hierarchical efficiency and energy consumption).

[0115] When the mesh density continues to increase, if the changes in the graded efficiency and energy consumption are within an acceptable error range, the simulation results are considered to be mesh-independent.

[0116] The optimal mesh density is selected to ensure the simulation accuracy without causing excessive computational cost.

[0117] According to the scheme provided by the present invention, a CFD model is constructed according to the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and a turbulence model is introduced to simulate the influence of turbulence on particle movement; in the CFD model, a multi-physical field coupling simulation is performed according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law; the optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and the optimal jet conditions and model parameters of the CFD model are iteratively calculated; the rare earth polishing powder jet is classified according to the optimal jet conditions and model parameters of the CFD model. The present invention simulates the changes in the fluid behavior, particle movement law and temperature field of the rare earth polishing powder in the jet classification process through a CFD simulation method, optimizes the jet classification process of the rare earth polishing powder, improves the classification efficiency and reduces energy consumption.

[0118] Figure 2The schematic diagram of the structure of the CFD simulation-driven rare earth polishing powder jet classification optimization device of the embodiment of the present invention is shown. The CFD simulation-driven rare earth polishing powder jet classification optimization device comprises: a CFD modeling and turbulence simulation module 210, a multi-physics field coupling simulation module 220, an optimization target setting and iterative calculation module 230, and a jet classification implementation module 240.

[0119] The CFD modeling and turbulence simulation module 210 is used to build a CFD model based on the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and introduce a turbulence model to simulate the influence of turbulence on particle movement;

[0120] The multi-physics field coupling simulation module 220 is used to perform multi-physics field coupling simulation in the CFD model according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle motion law;

[0121] The optimization target setting and iterative calculation module 230 is used to set the optimization target of maximizing classification efficiency and minimizing energy consumption, and iteratively calculate the optimal jet conditions and model parameters of the CFD model;

[0122] The jet classification implementation module 240 is used to classify the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model.

[0123] In an optional manner, constructing a CFD model and introducing a turbulence model to simulate the effect of turbulence on particle motion further includes:

[0124] The geometry model of the nozzle and jet area is constructed through the Geometry module of ANSYS Fluent, and the calculation domain is set for the simulated area;

[0125] Meshing the computational domain using the Mesh module of ANSYS Fluent;

[0126] Set the inlet boundary conditions, outlet boundary conditions and wall boundary conditions for flow rate and pressure;

[0127] Select the SST k-ω turbulence model according to the Reynolds number, and set the parameters and boundary conditions of the turbulence model in the CFD software;

[0128] Set the particle size distribution, density and shape parameters, as well as the initial position and velocity distribution of the particles, enable the coupling option with the turbulence model, and simulate the movement of rare earth polishing powder particles through the DPM discrete phase model;

[0129] Set the simulation time step and total time to capture the dynamic changes of flow and particle motion; initialize the computational domain and particle field, and simulate the effect of turbulence on particle velocity, trajectory and concentration through the Post-Processing module of ANSYS Fluent.

[0130] In an optional manner, the multi-physics field coupling simulation based on the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process further includes:

[0131] Set the specific heat capacity and thermal conductivity of the fluid, and set the temperature boundary conditions of the inlet temperature and wall temperature;

[0132] Enable energy equations in CFD software to simulate heat exchange between fluid and particles;

[0133] The multi-physics field coupling function is enabled in the Fluent module of ANSYS to couple the fluid field, particle field and temperature field for simulation.

[0134] In an optional manner, the step of calculating the optimal jet conditions and model parameters of the CFD model further includes:

[0135] Creating a CFD simulation process through the script function of the Fluent module in the ANSYS software; wherein the jet conditions and model parameters are read in the CFD simulation process, and the classification efficiency and energy consumption are saved during the simulation;

[0136] The optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm;

[0137] Passing the new jet conditions and model parameters to the CFD simulation process to update the optimization target;

[0138] If the change in the optimization target is less than the preset threshold or the number of iterations reaches the preset number, the iteration is stopped and the optimal jet conditions and model parameters are output; otherwise, the iteration continues.

[0139] In an optional manner, the grading of the rare earth polishing powder jet according to the optimal jet condition and model parameters of the CFD model further comprises:

[0140] According to the optimal jet conditions of flow rate, pressure and nozzle shape and the model parameters, the classification efficiency and energy consumption value of the rare earth polishing powder jet are predicted by the CFD model;

[0141] By comparing the experimental data with the simulation results, it is evaluated whether the classification efficiency and energy consumption values ​​have reached or are close to the optimization target.

[0142] In an optional manner, the method further includes:

[0143] Performing a sensitivity analysis on the temperature field to evaluate the effect of temperature on fractionation efficiency and energy consumption;

[0144] Among them, by changing the inlet temperature and wall temperature, the impact of temperature field changes on fluid behavior and particle movement is evaluated, thereby determining the extent of temperature influence on classification efficiency and energy consumption.

[0145] In an optional manner, the particle swarm optimization algorithm adopts an adaptive weight strategy during the iteration process to balance the two objectives of maximizing classification efficiency and minimizing energy consumption;

[0146] In the early stage of iteration, a higher weight is set for maximizing classification efficiency, and as the iteration proceeds, the weight for minimizing energy consumption is gradually increased until a balance is reached.

[0147] In an optional manner, the method further includes:

[0148] Multiple nozzle shape parameters are set and the jet performance and classification efficiency under different parameters are iteratively calculated to find the optimal nozzle shape parameters.

[0149] In an optional manner, the method further includes:

[0150] The grid independence simulation of the CFD model is verified under different grid densities, the differences in classification efficiency and energy consumption under different grid densities are compared, and the optimal grid density simulation result is selected.

[0151] Figure 3 The schematic diagram of the structure of the computing device embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0152] like Figure 3 As shown, the computing device may include: a processor (processor) 302 , a communication interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

[0153] The processor 302, the communication interface 304, and the memory 306 communicate with each other via the communication bus 308. The communication interface 304 is used to communicate with other devices such as a client or other server network elements. The processor 302 is used to execute the program 310, which can specifically execute the relevant steps in the embodiment of the rare earth polishing powder jet classification optimization method driven by CFD simulation.

[0154] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0155] The processor 302 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0156] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0157] According to the scheme provided by the present invention, a CFD model is constructed according to the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and a turbulence model is introduced to simulate the influence of turbulence on particle movement; in the CFD model, a multi-physical field coupling simulation is performed according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law; the optimization goals of maximizing classification efficiency and minimizing energy consumption are set, and the optimal jet conditions and model parameters of the CFD model are iteratively calculated; the rare earth polishing powder jet is classified according to the optimal jet conditions and model parameters of the CFD model. The present invention simulates the changes in the fluid behavior, particle movement law and temperature field of the rare earth polishing powder in the jet classification process through a CFD simulation method, optimizes the jet classification process of the rare earth polishing powder, improves the classification efficiency and reduces energy consumption.

[0158] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose. In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and to form different embodiments. For example, in the following claims, any one of the claimed embodiments may be used in any combination. The present invention may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The steps in the above embodiments should not be understood as limiting the execution order unless otherwise specified.

Claims

1. A CFD simulation-driven rare earth polishing powder jet classification optimization method, characterized in that: include: A CFD model is constructed based on the particle size distribution, density and shape of the rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of the rare earth polishing powder particles, and a turbulence model is introduced to simulate the effect of turbulence on particle movement; In the CFD model, multi-physics field coupling simulation is performed according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle movement law; A CFD simulation process is created through the script function of the Fluent module in the ANSYS software; wherein, the jet conditions and model parameters are read in the CFD simulation process, and the classification efficiency and energy consumption data are saved during the simulation; the optimization goals of maximizing the classification efficiency and minimizing the energy consumption are set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm; the new jet conditions and model parameters are passed to the CFD simulation process to update the optimization goals; if the change in the optimization goal is less than a preset threshold or the number of iterations reaches a preset number, the iteration is stopped and the optimal jet conditions and model parameters are output; otherwise, the iteration is continued; The rare earth polishing powder jet is graded according to the optimal jet conditions and model parameters of the CFD model.

2. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 1, characterized in that: Building a CFD model and introducing a turbulence model to simulate the effect of turbulence on particle motion further includes: The geometry model of the nozzle and jet area is constructed through the Geometry module of ANSYS Fluent, and the calculation domain is set for the simulated area; Meshing the computational domain using the Mesh module of ANSYS Fluent; Set the inlet boundary conditions, outlet boundary conditions and wall boundary conditions for flow rate and pressure; Select the SST k-ω turbulence model according to the Reynolds number, and set the parameters and boundary conditions of the turbulence model in the CFD software; Set the particle size distribution, density and shape parameters, as well as the initial position and velocity distribution of the particles, enable the coupling option with the turbulence model, and simulate the movement of rare earth polishing powder particles through the DPM discrete phase model; Set the simulation time step and total time to capture the dynamic changes of fluid and particle motion; initialize the computational domain and particle field, and simulate the effect of turbulence on particle velocity, trajectory and concentration through the Post-Processing module of ANSYS Fluent.

3. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 1 or 2, characterized in that: The multi-physics field coupling simulation based on the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process further includes: Set the specific heat capacity and thermal conductivity of the fluid, and set the temperature boundary conditions of the inlet temperature and wall temperature; Enable energy equations in CFD software to simulate heat exchange between fluid and particles; The multi-physics field coupling function is enabled in the Fluent module of ANSYS to couple the fluid field, particle field and temperature field for simulation.

4. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 1 or 2, characterized in that: The step of classifying the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model further comprises: According to the optimal jet conditions of flow rate, pressure and nozzle shape and the model parameters, the classification efficiency and energy consumption value of the rare earth polishing powder jet are predicted by the CFD model; By comparing the experimental data with the simulation results, it is evaluated whether the classification efficiency and energy consumption values ​​have reached or are close to the optimization target.

5. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 3, characterized in that: The method further comprises: Performing a sensitivity analysis on the temperature field to evaluate the effect of temperature on fractionation efficiency and energy consumption; Among them, by changing the inlet temperature and wall temperature, the impact of temperature field changes on fluid behavior and particle movement is evaluated, thereby determining the extent of temperature influence on classification efficiency and energy consumption.

6. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 1, characterized in that: The particle swarm optimization algorithm adopts an adaptive weight strategy in the iteration process to balance the two goals of maximizing classification efficiency and minimizing energy consumption; At the beginning of the iteration, a higher weight is set for maximizing the classification efficiency relative to minimizing the energy consumption. As the iteration proceeds, the weight for minimizing the energy consumption is gradually increased until a relative balance between maximizing the classification efficiency and minimizing the energy consumption is reached.

7. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 2, characterized in that: The method further comprises: Multiple nozzle shape parameters are set and the jet performance and classification efficiency under different parameters are iteratively calculated to find the optimal nozzle shape parameters.

8. The CFD simulation-driven rare earth polishing powder jet classification optimization method according to claim 1, characterized in that: The method further comprises: The grid independence simulation of the CFD model is verified under different grid densities, the differences in classification efficiency and energy consumption under different grid densities are compared, and the optimal grid density simulation result is selected.

9. A CFD simulation-driven rare earth polishing powder jet classification optimization system, characterized in that: The CFD simulation-driven rare earth polishing powder jet classification optimization method according to any one of claims 1 to 8 comprises: CFD modeling and turbulence simulation module, which is used to build a CFD model based on the particle size distribution, density and shape of rare earth polishing powder particles, as well as the flow rate, pressure and nozzle shape of rare earth polishing powder particles, and introduce a turbulence model to simulate the effect of turbulence on particle movement; A multi-physics field coupling simulation module is used to perform multi-physics field coupling simulation in the CFD model according to the fluid field, particle field and temperature field involved in the rare earth polishing powder jet classification process to obtain simulation results of fluid behavior and particle motion law; The optimization target setting and iterative calculation module is used to create a CFD simulation process through the script function of the Fluent module in the ANSYS software; wherein, the jet conditions and model parameters are read in the CFD simulation process, and the classification efficiency and energy consumption data are saved during the simulation; the optimization target of maximizing the classification efficiency and minimizing the energy consumption is set, and new jet conditions and model parameters are iteratively generated through the particle swarm optimization algorithm; the new jet conditions and model parameters are transferred to the CFD simulation process to update the optimization target; if the change in the optimization target is less than a preset threshold or the number of iterations reaches a preset number, the iteration is stopped and the optimal jet conditions and model parameters are output; otherwise, the iteration is continued; The jet classification implementation module is used to classify the rare earth polishing powder jet according to the optimal jet conditions and model parameters of the CFD model.

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