Optimal design method and system for classifying disc structure of moxa sand mill based on numerical simulation

Abstract

The invention belongs to the technical field of preparation equipment, and discloses an optimal design method for a grading disc structure of a moxa sand mill based on numerical simulation, which comprises the following steps of: establishing a simulation calculation model of an internal flow field and a particle field of a high-precision moxa sand mill based on experimental verification and correction; determining structure parameters, operation parameters and material properties of the moxa sand mill under the target working condition; a computational fluid mechanics-discrete element (CFD-DEM) coupling method is adopted to simulate an ore grinding medium and fluid movement in an Aimian sand mill, so that a high-precision flow field environment is obtained; secondly, on the basis of a stable flow field, motion parameters of particles with different properties are extracted through a discrete phase (DPM) model in Fluent software, and the axial displacement and the escape rate of the particles are calculated and serve as evaluation indexes of the grading effect of the grading disc; the structures such as the diameter, the thickness, the disc hole diameter and the position of the grading disc are further changed, and the optimal disc body structure is optimized by comparing the grading effects under different grading disc structures.

Description

Technical Field

[0001] This invention belongs to the field of mineral processing equipment technology, and in particular relates to a method and system for optimizing the design of the classifying disc structure of an ammonia mill based on numerical simulation. Background Technology

[0002] As my country's easily processed high-quality mineral resources become increasingly depleted, the characteristics of ores—being "poor, fine, and complex"—are becoming more prominent. Therefore, they must be thoroughly ground until the minerals are completely liberated for efficient utilization. Against this backdrop, the abrasive mill, which integrates grinding and classification functions, has gained widespread application. Compared to traditional equipment, the abrasive mill excels in grinding efficiency, energy consumption control, and product particle size distribution uniformity. The classification disc inside the abrasive mill is the core component that enables these characteristics. Its function is to allow fine particles that have reached the required particle size to pass through and be discharged through centrifugal force, while simultaneously blocking coarse particles that do not meet the size requirements and returning them to the grinding chamber for further grinding. This achieves the effect of integrated grinding and classification. An ideal classification disc should be able to accurately separate particles of different sizes, thereby avoiding "coarse particles running away" (coarse particles being discharged too early) and "over-grinding" (fine particles being repeatedly ground).

[0003] However, the internal structural design of the abrasive mill relies heavily on production experience and limited experimental data, lacking systematic scientific theoretical guidance and quantitative design basis. This empirical design method suffers from drawbacks such as long cycle time, high cost, and difficulty in obtaining optimal solutions. Furthermore, it cannot accurately predict the classification performance of the new structure during the design phase, hindering further improvements in equipment performance. In recent years, with the continuous development of computer technology, coupled simulation techniques of computational fluid dynamics (CFD) and discrete element method (DEM) have been proven effective in analyzing the flow field and media motion inside the mill. Currently, there is an urgent need to systematically apply advanced numerical simulation technology to the "classification" stage of the abrasive mill. High-precision numerical simulation has proven its value, effectively reducing experimental costs and revealing internal dynamics. However, establishing a scientific, universal, and efficient structural design and optimization method for the classification disc, a core component, remains a significant research gap that urgently needs to be filled.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] The internal structural design of the sand mill relies heavily on production experience and limited experimental data, lacking systematic scientific theoretical guidance and quantitative design basis. This empirical design method has drawbacks such as long cycle time, high cost, and difficulty in obtaining optimal solutions. Furthermore, it cannot accurately predict the graded performance of the new structure during the design phase, thus restricting further improvement of equipment performance. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for optimizing the design of the classifying disc structure in an abrasive mill based on numerical simulation.

[0007] This invention is implemented as follows: A method for optimizing the design of the classifier disc structure in an abrasive mill based on numerical simulation includes:

[0008] S1. Based on the verification and correction of actual experiments, a high-precision numerical simulation model of the flow field and particle field of the sand mill is determined.

[0009] S2. Determine the structural parameters, operating conditions, grinding media, and properties of the target particles of the sand mill under the target working conditions;

[0010] S3. Use the 3D modeling software SolidWorks to construct the geometric model inside the sand mill. Import the fluid domain geometric model into ANSYS fluent meshing software for mesh discretization and set the mesh type to tetrahedral mesh.

[0011] S4. Import the geometric model generated in step S3 into the EDEM software to set the physical property parameters, particle model and motion parameters.

[0012] S5. Import the mesh generated in step S3 into the CFD software Fluent, set the boundary conditions of the computational domain, and then set the multiphase flow model and turbulence model.

[0013] S6. A stable flow field is obtained by performing bidirectional coupling calculations on EDEM and Fluent through the API coupling interface.

[0014] The stable flow field refers to the state in which the flow field parameters and the motion state of the grinding media reach a stable state as time increases.

[0015] S7. Under a stable flow field environment, set the DPM model parameters in Fluent software, introduce particles of different densities and sizes into the flow field, analyze the axial migration behavior of the particles, calculate the escape rate of the particles, and obtain the classification effect of the classifying disc in the sand mill under the target working conditions.

[0016] S8. Modify the structure of the classifying disc in the sand mill, conduct numerical experiments using a high-precision calculation model, obtain the classification effect under the condition of the classifying disc, and compare it with the classification effect of the classifying disc in the sand mill under the target working condition. Continuously optimize to obtain the optimal classifying disc structure.

[0017] Furthermore, the structural parameters include: the outer shell size of the sand mill, the size and position of the inlet and outlet, the radius of the mixing disc, the distance between the mixing discs, the size of the holes in the mixing disc and their position on the mixing disc, the interval between the grading disc and the mixing disc, and the number of side holes on the grading disc.

[0018] The operating conditions include: rotational speed of the mixing and classifying discs, feed flow rate, grinding media filling rate, and grinding media size;

[0019] The material properties include the particle size and density of the target particles.

[0020] Furthermore, the physical properties include the collision recovery coefficient, static friction coefficient, dynamic friction coefficient, and Young's modulus of the grinding media and the mill casing material;

[0021] The particle model is the Hertz-Mindlin no-slip model;

[0022] The motion parameters refer to the rotational speeds of the stirring disc and grading disc inside the sand mill.

[0023] Furthermore, the boundary conditions include setting the interface between the rotational and static domains of the sand mill, the outer shell wall, the velocity inlet, and the pressure outlet;

[0024] The multiphase flow model is the Eulerian multiphase flow model, in which water and air are the main phase and the secondary phase, respectively, and the specific model equations are the continuity equation and the momentum equation, respectively.

[0025] The turbulence model is the SST kw turbulence model.

[0026] Furthermore, the DPM model parameters include: setting the step size factor to 1 in the tracking settings; setting the physical model to calculate Suffman lift, virtual mass force, and pressure gradient force; using the Runge-Kutta model for calculation accuracy; and introducing a random trajectory model and rough wall settings.

[0027] The particle axial migration behavior refers to the spatial distribution of particles along the axial direction at different times, with a focus on the axial distribution near the classifier, which is used to analyze the impact of the classifier on particle reflux.

[0028] The escape rate is defined as the percentage of particles escaping from the exit point out of the total number of particles deployed within a set tracking time; the specific formula is as follows:

[0029] E=c / n

[0030] Where E is the escape rate, c is the number of particles that escaped from the exit during the tracking time, and n is the total number of particles deployed.

[0031] The grading effect refers to the difference between the escape rates of fine and coarse particles; the larger the difference, the more significant the grading effect.

[0032] Furthermore, the grading disc structure includes: grading disc diameter, number of disc holes, disc hole position, and disc hole diameter.

[0033] Another objective of this invention is to provide a numerical simulation-based optimization design system for the classifier disc structure of an abrasive mill, comprising:

[0034] The correction module is used for verification and correction based on actual experiments to determine a high-precision numerical simulation model of the flow field and particle field of the abrasive mill.

[0035] The determination module is used to determine the structural parameters, operating conditions, grinding media, and properties of target particles of the sand mill under target operating conditions.

[0036] The import module is used to construct the geometric model inside the grinding mill using the 3D modeling software SolidWorks. The fluid domain geometric model is then imported into ANSYS Fluent Meshing software for mesh discretization, with the mesh type set to tetrahedral. The generated geometric model is imported into EDEM software for setting physical property parameters, particle models, and motion parameters. The generated mesh is then imported into the CFD software Fluent, where boundary conditions for the computational domain are set, followed by the establishment of multiphase flow and turbulence models. The stable flow field is obtained through bidirectional coupling calculations between EDEM and Fluent via the API coupling interface. The stable flow field refers to the state where various flow field parameters and grinding media motion reach a stable state over time.

[0037] The analysis module is used to set the DPM model parameters in Fluent software under a stable flow field environment, introduce particles of different densities and sizes into the flow field, analyze the axial migration behavior of the particles, calculate the escape rate of the particles, and obtain the classification effect of the classifying disc in the sand mill under the target working conditions.

[0038] The comparison module is used to change the structure of the classifying disc in the sand mill. Numerical experiments are carried out using a high-precision calculation model to obtain the classification effect under the condition of the classifying disc. The classification effect is compared with that of the classifying disc in the sand mill under the target working condition. The optimal classifying disc structure is obtained through continuous optimization.

[0039] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the numerical simulation-based optimization design method for the classifier disc structure of an abrasive mill.

[0040] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the numerical simulation-based optimization design method for the classifier disc structure of an abrasive mill.

[0041] Another objective of this invention is to provide an information data processing terminal, which is used to implement the numerical simulation-based optimization design system for the grading disc structure of an abrasive mill.

[0042] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0043] In existing technologies, the structural design and optimization of agitator mills and similar stirred mills mainly rely on empirical experiments and local parameter adjustments. On the one hand, the internal flow field of the mill is highly turbulent, with high solid particle concentration and complex fluid-structure interaction. It is difficult to accurately characterize the actual flow and particle migration patterns near the classifier using only external operating conditions and limited sampling data, resulting in structural designs often remaining at the level of empirical correction. On the other hand, traditional multi-factor experiments require frequent replacement of the classifier, adjustment of rotation speed and feed conditions, and evaluation of the classification effect through extensive manual sampling, sieving, and analysis. The experimental cycle is long and costly, and factors such as fluctuations in operating conditions and operational errors can easily introduce significant uncertainties, making it difficult to reliably obtain the "optimal classifier structure" under controllable conditions. These objective difficulties determine that relying solely on traditional methods to systematically and quantitatively optimize the classifier structure of agitator mills has a high technical threshold.

[0044] The numerical simulation-based optimization design method for the classifying disc structure of an abrasive mill proposed in this invention addresses the aforementioned challenges by offering novel technical solutions from three levels: modeling mechanism, evaluation indicators, and optimization process. First, this invention verifies and corrects the numerical model using actual experimental results. A multiphase flow model is adopted in the fluid phase, and contact collision and friction parameters are introduced in the particle phase. Through bidirectional coupling of the fluid calculation model and the particle calculation model, a high-precision simulation model that stably reflects the coupling relationship between the internal flow field and particle field of the abrasive mill is obtained. Under given structural parameters and operating conditions, this model can repeatedly calculate the motion state of the grinding media and the migration behavior of target particles, fundamentally solving the technical problem that traditional methods cannot directly observe the flow and classification mechanism within the mill's internal space.

[0045] Secondly, this invention introduces the residence and escape behaviors of particles in the mill as a quantitative evaluation criterion for structural quality. By tracking the axial migration process of target particles of different sizes under stable flow field conditions, the number of particles escaping from the discharge port within a set tracking time is statistically analyzed, and the escape ratio of fine and coarse particles is distinguished, forming a classification effect evaluation system with the difference between particle residence time and escape rate as the core. Unlike existing technologies that only make rough judgments based on discharge particle size distribution or classification curves under a single operating condition, this invention characterizes the backflow and passage behavior of particles in the classification disc area through both time and spatial dimensions, presenting the influence of the classification disc structure in a clear physical quantity form, thus demonstrating significant innovation in the evaluation mechanism.

[0046] Furthermore, this invention combines the aforementioned high-precision simulation model with quantitative evaluation indicators to construct a closed-loop optimization process: "parameter setting—fluid-structure interaction calculation—stable flow field acquisition—particle migration analysis—grading effect evaluation—structural parameter update." By systematically changing structural parameters such as the grading disk diameter, number of disk holes, disk hole position, and disk hole diameter in a computer environment, the optimal structural combination for grading effect can be quickly selected without the need for numerous physical prototypes and repeated physical experiments. This process not only significantly saves manpower and resources required for experimental setup, manual sampling, and data analysis, reducing the R&D and design costs of the abrasive mill, but also, because the numerical model has been experimentally corrected, the calculation results have good stability and repeatability, effectively avoiding experimental errors caused by operating condition fluctuations and human operation.

[0047] Finally, the numerical simulation and optimization design method constructed in this invention is not limited to a specific model of sand mill, but is based on a general technical approach of fluid-structure interaction modeling, particle residence and escape behavior analysis, and quantitative evaluation of classification effect. It is also applicable to stirred mills, sand mills, and other equipment with similar working principles and the presence of classification discs or internal separation structures. Therefore, this invention not only solves the technical problem of the difficulty in finely optimizing the classification disc of sand mills under complex fluid-structure interaction conditions, but also proposes a new technical path with promotional value in structural evaluation methods and design processes, demonstrating substantial characteristics and significant technological progress overall.

[0048] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0049] Currently, the volume of sand mills used in various mines ranges from 5L to 10000L, and their manufacturing costs and production cycles increase exponentially with the increase in volume. This invention, however, employs numerical simulation technology to scientifically and objectively optimize the parameters of the internal grading discs in sand mills. Through computer simulation, suitable grading disc parameters can be precisely matched to the characteristics of different minerals, thereby saving mining companies significant manpower and material costs, while effectively shortening the product particle size adjustment cycle and optimizing particle size distribution. Especially given the increasingly prominent characteristics of lean, fine, and complex minerals in mines nationwide, the application of this invention further helps reduce mine operating costs, thereby effectively improving the economic benefits of enterprises.

[0050] The commercial value of this invention lies in its ability to provide customized design schemes for the classifying disc parameters of sand mills based on the ore characteristics, processing capacity, and target product requirements of different mines. This scheme, through efficient matching of equipment and processes, significantly reduces mine production costs, improves product quality, and achieves a substantial increase in output.

[0051] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0052] Current research on sand mills, both domestically and internationally, largely focuses on the macroscopic level, such as the analysis of internal particle velocity distribution, product particle size characteristics, and overall collision behavior. However, in-depth exploration from a microscopic perspective is lacking. This invention innovatively constructs a refined model of the flow field and particle field within the sand mill from a microscopic perspective using high-precision numerical simulation methods. By importing and tracking the trajectory of individual particles in the flow field using the DPM model, it clearly reveals the influence mechanism of the classifying disc on the motion behavior of particles of different sizes.

[0053] (3) Whether the technical solution of the present invention solves the technical problem that people have long wanted to solve but have never been able to solve successfully:

[0054] The classifying disc inside the sand mill is a key component affecting the particle size distribution of the final product. In the past, the selection of parameters for this component relied heavily on subjective experience or intuition, lacking objective and effective evaluation standards, which easily led to a waste of human and material resources. This invention uses numerical simulation to intuitively analyze the sorting effect of the classifying disc, and uses the comparison of the escape rates of large and small particles as its performance evaluation index. This method provides an objective basis for the selection of classifying disc parameters, thereby supporting the accurate selection of classifying discs for sand mills in actual production. Attached Figure Description

[0055] Figure 1 This is a flowchart of the method for optimizing the structure of the classifying disc in an abrasive mill based on numerical simulation, provided in an embodiment of the present invention.

[0056] Figure 2 This is a structural block diagram of the system for optimizing the structure of the classifying disc of an abrasive mill based on numerical simulation, provided in an embodiment of the present invention.

[0057] Figure 3 This is a diagram showing the influence of the grading disc diameter on the axial migration of quartz particles inside the sand mill, as provided in an embodiment of the present invention.

[0058] Figure 4 This is a graph showing the effect of the grading disk diameter on the escape rate of quartz particles, provided in an embodiment of the present invention.

[0059] Figure 5 This is a graph showing the effect of the grading disk diameter on the escape rate of fluorite particles, provided in an embodiment of the present invention.

[0060] Figure 6 This is a graph showing the change in the axial position of fluorite particles of different sizes over time when the diameter of the grading disc is 132 mm, as provided in this embodiment of the invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0062] like Figure 1 As shown in the figure, the method for optimizing the structure of the classifying disc in an abrasive mill based on numerical simulation provided by this invention includes the following steps:

[0063] S1. Based on the verification and correction of actual experiments, a high-precision numerical simulation model of the flow field and particle field of the sand mill is determined.

[0064] S2. Determine the structural parameters, operating conditions, grinding media, and properties of the target particles of the sand mill under the target working conditions;

[0065] S3. Use the 3D modeling software SolidWorks to construct the geometric model inside the sand mill. Import the fluid domain geometric model into ANSYS fluent meshing software for mesh discretization and set the mesh type to tetrahedral mesh.

[0066] S4. Import the geometric model generated in step S3 into the EDEM software to set the physical property parameters, particle model and motion parameters.

[0067] S5. Import the mesh generated in step S3 into the CFD software Fluent, set the boundary conditions of the computational domain, and then set the multiphase flow model and turbulence model.

[0068] S6. A stable flow field is obtained by performing bidirectional coupling calculations on EDEM and Fluent through the API coupling interface.

[0069] The stable flow field refers to the state in which the flow field parameters and the movement of the grinding media reach a stable state as time increases.

[0070] S7. Under a stable flow field environment, set the DPM model parameters in Fluent software, introduce particles of different densities and sizes into the flow field, analyze the axial migration behavior of the particles, calculate the escape rate of the particles, and obtain the classification effect of the classifying disc in the sand mill under the target working conditions.

[0071] S8. Modify the structure of the classifying disc in the sand mill, conduct numerical experiments using a high-precision calculation model, obtain the classification effect under the condition of the classifying disc, and compare it with the classification effect of the classifying disc in the sand mill under the target working condition. Continuously optimize to obtain the optimal classifying disc structure.

[0072] The present invention provides a numerical simulation-based optimization design method for the classifier disc structure of an abrasive mill. Its core working principle is to construct a fluid-solid multi-field coupled numerical model that is highly consistent with the actual working conditions, quantitatively reveal the influence mechanism of the classifier disc structure parameters on the flow field morphology, particle motion behavior and classification performance in the mill, and realize the iterative optimization design of the classifier disc structure on this basis.

[0073] First, this method uses actual experimental data to verify and correct the numerical simulation model of the internal flow field and particle field of the sand mill, ensuring that the established model can truly reflect the fluid flow characteristics and grinding media motion laws of the mill under target operating conditions. Through this process, the numerical simulation model maintains a high degree of consistency with the actual operating conditions in key indicators such as velocity distribution, turbulence intensity, pressure gradient, and media motion state, providing a reliable foundation for subsequent analysis.

[0074] Based on this, the structural parameters, operating conditions, grinding media characteristics, and physical properties of the target particles of the ammonia mill under the target working conditions were clearly defined, and a geometric model of the mill's interior was constructed using 3D modeling techniques. By discretizing the fluid domain with a fine mesh, the complex internal space of the mill was transformed into discrete elements suitable for numerical calculations, thus providing a computational platform for fluid dynamics solutions. Simultaneously, the physical properties and motion models of the grinding media and target particles were set within the particle discrete element environment, enabling accurate description of the contact, collision, and energy transfer processes between particles and between particles and structures.

[0075] Subsequently, real-time interactive calculations of the fluid field and particle field were achieved using CFD-DEM bidirectional coupling technology. During this coupling process, the fluid field exerts drag and lift forces on the particles, while the presence and movement of the particles, in turn, affect the local flow field structure, thus forming a stable fluid-solid coupled field that truly reflects the internal working conditions of the grinding mill. When the flow field parameters and the motion state of the grinding media no longer change significantly over time, the system is considered to have reached a stable flow field state.

[0076] Under stable flow field conditions, a discrete phase model is introduced to track and calculate target particles of different densities and sizes. By analyzing the migration path, residence time, and escape behavior of particles in the axial direction, the escape rate of particles is quantitatively calculated, thereby characterizing the classification effect of the classification disk under the target working conditions. This process essentially reveals the influence mechanism of the classification disk structure on the force balance, migration trend, and classification selectivity of particles.

[0077] Finally, by changing the structural form and key geometric parameters of the classifying disk, the above numerical simulation and classification performance evaluation process was repeated to compare and analyze the classification effects under different structural conditions. Through continuous iterative optimization, the classifying disk achieves a synergistic improvement in particle classification efficiency and operational stability while meeting the target operating conditions, thereby obtaining the optimal classifying disk structure scheme. This method replaces extensive experimental exploration with numerical simulation, significantly improving the scientific nature, controllability, and optimization efficiency of the classifying disk structure design.

[0078] The structural parameters provided in this embodiment of the invention include: the outer shell size of the sand mill, the size and position of the inlet and outlet, the radius of the mixing disc, the distance between the mixing discs, the size of the holes in the mixing disc and their position on the mixing disc, the interval between the grading disc and the mixing disc, and the number of side holes of the grading disc.

[0079] The operating conditions include: rotational speed of the mixing and classifying discs, feed flow rate, grinding media filling rate, and grinding media size;

[0080] The material properties include the particle size and density of the target particles.

[0081] The physical property parameters provided in the embodiments of the present invention include the collision recovery coefficient, static friction coefficient, dynamic friction coefficient, and Young's modulus of the grinding media and the mill shell material;

[0082] The particle model is the Hertz-Mindlin no-slip model;

[0083] The motion parameters refer to the rotational speeds of the stirring disc and grading disc inside the sand mill.

[0084] The boundary conditions provided in this embodiment of the invention include setting the interface between the rotational and static domains of the sand mill, the outer shell wall, the velocity inlet, and the pressure outlet;

[0085] The multiphase flow model is the Eulerian multiphase flow model, in which water and air are the main phase and the secondary phase, respectively, and the specific model equations are the continuity equation and the momentum equation, respectively.

[0086] The turbulence model is the SST kw turbulence model.

[0087] The DPM model parameters provided in this embodiment of the invention include: setting the step size factor to 1 in the tracking settings; setting the physical model to calculate Suffman lift, virtual mass force, and pressure gradient force; using the Runge-Kutta model for calculation accuracy; and introducing a random trajectory model and rough wall settings.

[0088] The particle axial migration behavior refers to the spatial distribution of particles along the axial direction at different times, with a focus on the axial distribution near the classifier, which is used to analyze the impact of the classifier on particle reflux.

[0089] The escape rate is defined as the percentage of particles escaping from the exit point out of the total number of particles deployed within a set tracking time; the specific formula is as follows:

[0090] E=c / n

[0091] Where E is the escape rate, c is the number of particles that escaped from the exit during the tracking time, and n is the total number of particles deployed.

[0092] The grading effect refers to the difference between the escape rates of fine and coarse particles; the larger the difference, the more significant the grading effect.

[0093] The grading disc structure provided in this embodiment of the invention includes: grading disc diameter, number of disc holes, disc hole position, and disc hole diameter.

[0094] This invention, based on the principles of numerical simulation and multiphysics coupled computation, provides a unified modeling and analysis of fluid flow, grinding media movement, and target particle migration behavior within an abrasive mill, thereby enabling quantitative optimization design of the classifying disc structure. Its core idea is to systematically evaluate the impact of classifying disc structural parameters on the classification effect by replacing traditional empirical experiments with high-precision numerical simulation, while accurately reflecting the internal operating mechanism of the abrasive mill.

[0095] In this invention, the numerical model is first verified and corrected by combining actual experimental results to construct a high-precision simulation model that can accurately describe the coupling characteristics of the internal flow field and particle field of the sand mill. This model is based on real structural parameters and operating conditions, ensuring that subsequent calculation results can reflect the actual operating state. By establishing a geometric model of the sand mill in 3D modeling software and discretizing the fluid domain using tetrahedral meshes, the fluid flow characteristics and local velocity gradients are effectively characterized.

[0096] Subsequently, momentum exchange and interaction between the fluid phase and the grinding media were realized through bidirectional coupling of CFD and Discrete Element Method (DEM) software. The fluid phase employed an Eulerian multiphase flow model to describe the flow behavior of water and air, while turbulence characteristics were characterized using the SST kw model. The particle phase utilized a Hertz-Mindlin no-slip model to describe the collision responses between the grinding media and target particles, and between particles and the shell. During the bidirectional coupling calculation, fluid forces drove particle motion, and particle reaction forces fed back and corrected the flow field until the system's flow field parameters and particle motion stabilized over time, forming a stable flow field environment.

[0097] Under steady flow conditions, a DPM model is introduced to perform Lagrangian tracking of target particles with different sizes and densities. By simulating the forces, motion, and trajectory evolution of particles in the flow field, the migration behavior of particles in the axial direction is analyzed, with a focus on the particle distribution characteristics in the region near the classification disk to reveal the influence of the classification disk structure on particle backflow and passage behavior. Simultaneously, by counting the number of particles escaping from the outlet within a set tracking time, the escape rate of particles with different sizes is calculated, and the difference between the escape rates of fine and coarse particles is used as an evaluation index of the classification effect.

[0098] Based on the above calculation results, numerical simulation experiments were repeatedly conducted by changing the structural parameters of the classifying disc, including the disc diameter, number of holes, hole position, and hole diameter, to compare and analyze the classification effect under different structural conditions. By continuously adjusting the classifying disc structure and evaluating its influence on particle axial migration and escape patterns, the classifying disc structure with the optimal classification effect under the target operating conditions was finally selected. This achieves the scientific optimization design of the classifying disc structure in the sand mill, improving classification efficiency and system performance.

[0099] like Figure 2 As shown in the figure, an embodiment of the present invention provides a numerical simulation-based optimization design system for the classifier disc structure of an abrasive mill, comprising:

[0100] The correction module is used for verification and correction based on actual experiments to determine a high-precision numerical simulation model of the flow field and particle field of the abrasive mill.

[0101] The determination module is used to determine the structural parameters, operating conditions, grinding media, and properties of target particles of the sand mill under target operating conditions.

[0102] The import module is used to construct the geometric model inside the grinding mill using the 3D modeling software SolidWorks. The fluid domain geometric model is then imported into ANSYS Fluent Meshing software for mesh discretization, with the mesh type set to tetrahedral. The generated geometric model is imported into EDEM software for setting physical property parameters, particle models, and motion parameters. The generated mesh is then imported into the CFD software Fluent, where boundary conditions for the computational domain are set, followed by the establishment of multiphase flow and turbulence models. The stable flow field is obtained through bidirectional coupling calculations between EDEM and Fluent via the API coupling interface. The stable flow field refers to the state where various flow field parameters and grinding media motion reach a stable state over time.

[0103] The analysis module is used to set the DPM model parameters in Fluent software under a stable flow field environment, introduce particles of different densities and sizes into the flow field, analyze the axial migration behavior of the particles, calculate the escape rate of the particles, and obtain the classification effect of the classifying disc in the sand mill under the target working conditions.

[0104] The comparison module is used to change the structure of the classifying disc in the sand mill. Numerical experiments are carried out using a high-precision calculation model to obtain the classification effect under the condition of the classifying disc. The classification effect is compared with that of the classifying disc in the sand mill under the target working condition. The optimal classifying disc structure is obtained through continuous optimization.

[0105] This invention provides a numerical simulation-based optimization design system for the classifier disc structure of an abrasive mill. Addressing the challenges of quantitatively assessing the classification efficiency of abrasive mills under complex multiphase flow and particle coupling conditions, and the reliance on experience for structural optimization, this system constructs a systematic optimization design framework centered on high-precision numerical simulation. The system incorporates actual experimental results through a correction module to verify and correct the numerical models of the flow field and particle field, effectively reducing the deviation between numerical simulation and actual operating conditions, thereby establishing a high-precision simulation foundation model with engineering credibility.

[0106] The module defines the structural parameters, operating conditions, grinding media, and target particle properties under the target working conditions, achieving standardization and repeatability of simulation input parameters and ensuring the comparability of different structural schemes under consistent working conditions. The import module organically integrates 3D geometric modeling, multiphase flow calculation, and discrete element particle motion. Through separate modeling of the fluid domain and particle domain and bidirectional coupling calculation using API, it achieves simultaneous solution of the internal flow field evolution and grinding media motion behavior of the mill, obtaining a realistic flow environment that evolves over time and eventually reaches a steady state.

[0107] Building upon this foundation, the analysis module further introduces target particles with varying densities and size distributions. Using a discrete phase model, it quantitatively analyzes the axial migration behavior of these particles in a stable flow field, calculates the particle escape rate, and characterizes the classification performance of the classifying disk under target conditions, thus achieving visualization and quantifiable evaluation of the classification effect. The comparison module, by systematically changing the structural parameters of the classifying disk, conducts comparative numerical experiments under the same high-precision numerical model conditions. Based on the analysis of differences in classification effects, iterative optimization is continuously performed, ultimately selecting the optimal classifying disk structure that meets the requirements of the target operating conditions.

[0108] This invention further provides computer equipment, computer-readable storage media, and information data processing terminals corresponding to the method, enabling the system to operate stably in a software-based and modular manner, and realizing the automation, scalability, and engineering application of the classification disc structure optimization design process. The overall solution significantly improves the scientificity and reliability of the classification disc structure design of the sand mill, and provides a reusable technical path for the structural optimization of complex grinding equipment.

[0109] The present invention provides a specific implementation scheme for the numerical simulation-based optimization design method of the classifier disc structure in an abrasive mill, comprising the following steps:

[0110] S1. Based on the verification and correction of actual experiments, a high-precision numerical calculation model for the internal flow field and solid particle field of the sand mill is established.

[0111] The numerical calculation methods for the fluid field and solid particle field have been carefully selected and systematically verified. The experimental data of particle velocity distribution inside the sand mill under the same working conditions are used as a benchmark and compared with the results calculated by the fluid-structure interaction model. Based on the results, the fluid-structure interaction model is calibrated and optimized, thereby constructing a high-precision numerical calculation model for the sand mill.

[0112] S2, determine the structural parameters, operating conditions, mill materials, grinding particle properties, and properties of the particles to be processed of the sand mill under the target working conditions.

[0113] The structural parameters include the length L and diameter D of the outer shell of the sand mill, the number N of the internal stirring discs, the diameter D1 of the stirring discs, the thickness δ of the discs, the disc spacing l, the number n and diameter d of the stirring disc holes, the diameter D2 of the grading disc, and the thickness δ1 of the grading disc.

[0114] The structure of the sand mill used in this case is shown in Table 1:

[0115] Table 1. Specific structural parameters of the sand mill

[0116]

[0117] The operating parameters include: feed flow rate: 150L / h, rotation speed of the internal stirring disc and grading disc of the artemisia argyi: 1681.07rpm, and particle filling rate of 80%.

[0118] The properties of the mill material and the grinding particles are shown in Table 2:

[0119] Table 2. Properties of materials used in mills and grinding media inside mills

[0120]

[0121] The granular material to be processed has the following properties: density of 2650 kg / m³. 3 Quartz particles with a size of 74μm have an ideal processing size of 38μm.

[0122] S3. The geometric model inside the sand mill was constructed using the 3D modeling software SolidWorks. The fluid domain geometric model was then imported into ANSYS fluent meshing software for mesh discretization, and the mesh type was set to tetrahedral mesh.

[0123] The geometric model was created using SolidWorks 3D modeling software, which included the outer shell, internal stirring plate, and grading plate of the sand mill, and the overall assembly was completed. Finally, the model was exported in STEP format.

[0124] The mesh generation process involves importing the geometric model generated in step S3 into the designmodel module of the Workbench software, dividing it into static domains, fluid domains, and various boundary surfaces, and then importing it into Fluent for mesh discretization. The mesh type is set to tetrahedral mesh, and three boundary layers are set near each interface to make the simulation structure more accurate.

[0125] S4. Import the physical model file of the sand mill into the EDEM software, and set the parameters for the particles and the materials used in the sand mill (as shown in Table 2). Set up the particle factory and add the calculated particle quantity. Then reset the calculation time of the EDEM file and set the rotation of the stirring plate and the classifying plate.

[0126] The physical model file of the sand mill is a STEP format file exported from S3.

[0127] The properties of the particles include: particle density ρ, coefficient of restitution v, Young's modulus G, static friction coefficient between particles, sliding friction coefficient, and rolling friction coefficient. Specific parameters are shown in Table 2.

[0128] The number of particles is calculated by combining the particle filling rate determined in S2, the volume inside the sand mill, and the porosity between particles.

[0129] The rotational speed is determined in S2.

[0130] S5. Import the mesh generated in step S3 into the CFD software Fluent, set the boundary conditions of the computational domain, then set the Eulerian multiphase flow model and the turbulence model, further setting the inlet and outlet operating parameters and the air recirculation coefficient, and then perform flow field calculations until the flow field reaches relative stability. Direct coupling will result in extremely poor continuity of the calculation file, which can easily lead to non-convergence or inaccurate results.

[0131] The boundary conditions include the velocity inlet and pressure outlet of the sand mill, wherein the pressure outlet is set to the local atmospheric pressure, i.e., the relative pressure is 0.

[0132] The operating parameters are mainly the rotational speed of the rotational domain defined in S4, and are set to the parameter value of 1681.07 rpm determined in S2.

[0133] S6 uses the API coupling interface to bidirectionally couple the steady-state flow field in S5 with the particle field in S4 after the particles have been added. At the same time, the calculation time of the flow field is set to be consistent with that of the particle field. In this experiment, the numerical calculation time step is set to 2000, each step is calculated for 0.001s, and the total coupling calculation time is 2s.

[0134] S7. After obtaining a stable flow field, set the DPM model parameters in Fluent software, introduce particles of different densities and sizes into the flow field, analyze the axial migration behavior of the particles, calculate the escape rate of the particles, and obtain the classification effect of the classifying disc in the sand mill under the target working conditions.

[0135] The DPM model parameter settings include: setting the step size factor to 1 in the tracking settings; setting the calculation of Saffman lift, virtual mass force, and pressure gradient force in the physical model; setting the calculation accuracy model to Runge-Kutta model; and enabling the random orbit model and rough wall model.

[0136] The particle axial migration behavior refers to the spatial distribution of particles along the axial direction at different times, with a focus on the axial distribution near the classifier, which is used to analyze the impact of the classifier on particle reflux.

[0137] The escape rate is defined as the percentage of particles that escape from the exit point within a set tracking time, out of the total number of particles deployed. The specific formula is as follows:

[0138] E=c / n

[0139] Where E is the escape rate, c is the number of particles that escape from the exit during the tracking time, and n is the total number of particles deployed.

[0140] The grading effect is evaluated based on the difference between the escape rates of fine and coarse particles; the larger the difference, the more significant the grading effect.

[0141] S8 modifies the structure of the classifying disc in the sand mill, employs a high-precision computational model to conduct numerical experiments, obtains the classification effect under the new disc conditions, and compares it with the classification effect of the classifying disc in the sand mill under target operating conditions. Through continuous optimization, the optimal classifying disc structure is obtained. This patent uses sand mills with different classifying disc diameters as examples. It compares the differences in axial migration behavior of particles of different sizes under different classifying disc diameters, calculates the particle escape rate, and pays particular attention to whether particles experience backflow due to the classifying disc. It also compares the differences in the escape rates of large and small particles under different structures.

[0142] Create a density of 2650 kg / m³ in Fluent. 3Quartz material was used, and quartz particles of 38μm and 74μm were set in the Injections section below the DPM model, respectively, and fed in from the inlet at a velocity of 0.05m / s. To pursue accurate particle trajectory tracking, a random trajectory model and a rough wall model were enabled. The particle flow was tracked using the particle tracking function in the Results tab. By changing the maximum integration step, the maximum particle tracking time was set to 10s, and the axial migration trajectory and escape rate of the particle flow within 10s were calculated. The axial migration trajectory results of a single particle are shown below. Figure 3 .

[0143] from Figure 3 It can be seen that within the range of 122mm or less in diameter of the classifying disc, both coarse and fine quartz particles can escape from the flow field outlet within 10s, and no backflow phenomenon was observed near the classifying disc. Therefore, it is concluded that the classifying disc within this diameter range does not have a classifying function. However, when the diameter of the classifying disc is greater than 127mm, it can be observed that under the action of centrifugal force inside the flow field, coarse particles are thrown to the periphery of the classifying disc and return to the grinding zone for re-crushing, while fine particles are smoothly discharged from the outlet with the fluid, thus achieving effective particle classification. This phenomenon is consistent with the design concept of the abrasive mill: near the outlet, particles that have reached the ideal particle size can smoothly escape from the outlet, while particles that have not been crushed to the ideal particle size return to the grinding zone for further crushing due to the centrifugal force within the flow field until they are crushed to the specified particle size. It was also observed that the escape time of small particles decreases with the increase of the classifying disc diameter, suggesting that the separation effect increases with the increase of the classifying disc diameter. Subsequently, the escape rate variation graph of large and small particles of the abrasive mill under various classifying disc diameters was extracted to further verify the above analysis, such as... Figure 4 As shown.

[0144] from Figure 4 It is evident that within the 112-122mm diameter range of the classifying disc, the escape rates of coarse and fine quartz particles are relatively similar. Therefore, ash mills with classifying disc diameters within this range do not possess integrated grinding and classification functions. However, when the classifying disc diameter exceeds 122mm, the escape rate of fine particles inside the ash mill continuously increases, with almost all escaping through the outlet. Coarse particles, due to the reflux effect, remain within the flow field, undergoing reciprocating motion, resulting in an escape rate of almost zero, thus achieving a good classification effect. Therefore, as the diameter of the classifying disc increases, the classification effect of the ash mill gradually increases. This aligns with the aforementioned... Figure 2 The analysis results are consistent.

[0145] Example 1

[0146] This embodiment focuses on the operating state of the ammonia mill under target conditions. First, based on actual experimental data, the numerical simulation models of the flow field and particle field are verified and corrected to obtain a high-precision numerical model that reflects the actual operating characteristics. On this basis, structural parameters such as the outer shell size, inlet and outlet positions and sizes, stirring disc radius and spacing, and the distance between the classifying disc and the stirring disc are determined. Simultaneously, operating conditions such as stirring disc rotation speed, classifying disc rotation speed, feed flow rate, grinding media filling rate, and grinding media size are determined. At the same time, the particle size and density of the target particles are set as simulation input parameters.

[0147] By constructing a numerical simulation model using the above parameter system, it is possible to quantitatively analyze the differences in grading effects caused by changes in the grading disk structure, while ensuring consistency in structure and operating conditions. This provides complete support for the technical steps of changing the grading disk structure and comparing the grading effects, ensuring that the optimization design process of the grading disk structure is repeatable and engineering feasible.

[0148] Example 2

[0149] In this embodiment, when establishing the particle calculation model, collision recovery coefficient, static friction coefficient, dynamic friction coefficient, and Young's modulus are respectively set for the grinding media and the shell material of the ammonia mill. A no-slip contact model is used to describe the contact and collision behavior between the grinding media, target particles, and the internal structure of the equipment. At the same time, the rotational speeds of the stirring disc and the classifying disc are used as particle motion parameters input into the model to describe the actual motion state of the grinding media and target particles in the rotating flow field.

[0150] By using the above particle and property modeling methods, the influence of grinding media motion on the migration behavior of target particles can be accurately reflected, so that the simulation results are consistent with the actual working conditions in terms of particle collision, backflow and classification behavior, thereby supporting the technical features of particle model, property parameters and motion parameter settings.

[0151] Example 3

[0152] In this embodiment, the internal space of the sand mill is divided into a rotating domain and a static domain in the fluid calculation model, with an interface between them. The outer shell of the sand mill is set as the wall boundary, the feed inlet is set as the velocity inlet, and the discharge outlet is set as the pressure outlet. The fluid phase adopts the Eulerian multiphase flow model, in which water is the main phase and air is the secondary phase. Turbulent behavior is described by the shear stress transfer model.

[0153] By setting the fluid model and boundary conditions as described above, a stable flow field distribution that conforms to the actual flow characteristics inside the sand mill can be obtained, providing a reliable flow field basis for subsequent particle migration analysis, thus fully supporting the fluid model and boundary conditions.

[0154] Example 4

[0155] In the numerical calculation process, a two-way coupling calculation between the fluid model and the particle model is used to allow the forces exerted by the fluid on the particles and the reaction forces exerted by the particles on the fluid to feedback each other until the flow field parameters and the motion state of the grinding media tend to stabilize over time, forming a stable flow field. Under this stable flow field condition, target particles of different sizes and densities are introduced, and Lagrange tracking calculations are performed on the target particles.

[0156] By setting up force and motion models of target particles in a stable flow field, the motion trajectory and migration characteristics of target particles inside the sand mill can be accurately described, providing a reliable data basis for calculating the escape rate and classification effect of target particles, thereby supporting the implementation process of evaluation and optimization mechanisms.

[0157] Example 5

[0158] In this embodiment, the spatial distribution of target particles along the axial direction of the sand mill at different times is statistically analyzed, with a focus on the enrichment, backflow, and passage behavior of target particles in the vicinity of the classifying disc. By comparing the axial migration characteristics of target particles under different classifying disc structures, the influence of the classifying disc structure on particle backflow suppression and passage capacity is evaluated.

[0159] The above-mentioned axial migration behavior analysis method can intuitively reflect the regulatory effect of the classifying disk structure on the particle movement path, providing a basis for judging whether the classifying disk structure is conducive to the passage of fine particles and restricts the backflow of coarse particles, and fully supporting the limitation of particle axial migration behavior.

[0160] Example 6

[0161] Within the set particle tracking time, the number of target particles escaping from the discharge port of the sand mill is counted, and the ratio of this number to the total number of target particles is calculated to obtain the escape rate of the target particles. At the same time, the escape rates of fine particles and coarse particles are calculated separately, and the difference between the two escape rates is used as the evaluation index of the grading effect.

[0162] By using the escape rate difference as the basis for evaluating the classification effect, the selective separation capability of the classification disk structure for particles of different sizes can be quantitatively reflected, giving the classification effect evaluation process clear physical meaning and comparability, thereby supporting the classification effect evaluation method.

[0163] Example 7

[0164] In this embodiment, by changing the diameter of the grading disk, a grading disk structure model is constructed, and numerical simulation calculations are carried out under the same working conditions to obtain the grading effect corresponding to different grading disk structures.

[0165] By comparing and analyzing the grading effects of different grading disc structures, we can clarify the influence of each structural parameter on particle escape rate and grading effect, thereby selecting the grading disc structure with the best grading effect and supporting the technical solution for optimizing the grading disc structure.

[0166] Example 8

[0167] This embodiment provides a numerical simulation-based optimization design system for the classifier disc structure of an abrasive mill. The system includes a correction module, a parameter determination module, a coupled calculation module, an analysis module, and a comparative optimization module. During operation, the system first corrects the numerical model based on actual experimental data, then performs bidirectional coupled calculations on the fluid and particles until the flow field parameters and the motion state of the grinding media stabilize over time.

[0168] Through the above system structure and stable flow field determination mechanism, it can be ensured that the evaluation of the graded effect is based on the stable operating state, avoiding the interference of transient fluctuations on the calculation results, thereby fully supporting the technical characteristics of the system structure and stable flow field definition.

[0169] Example 9

[0170] In this embodiment, the numerical simulation-based structural optimization design system for the classifying disc of the sand mill is applied to the design stage of the classifying disc of the sand mill. Through systematic numerical simulation and structural comparison, the optimized selection of the structural parameters of the classifying disc is achieved.

[0171] By using the system for the structural optimization design of the classifying disc in an abrasive mill, quantitative optimization of the classifying disc structure can be achieved without conducting extensive physical experiments, thereby improving design efficiency and reducing R&D costs, thus supporting the limitation of the system's application.

[0172] To verify the effectiveness of the numerical simulation-based optimization design method for the classifying disc structure of an alumina mill proposed in this invention in predicting particle classification, fluorite particles with diameters of 74 μm and 25 μm were further selected as research objects, and their escape rates under stable flow field conditions were statistically analyzed. The results are as follows: Figure 5 As shown. By Figure 5 It can be seen that when the diameter of the classifying disk is small (112 mm to 122 mm), the escape rates of both particle sizes remain at a high level, and the difference between the two is not significant. This indicates that at this structural scale, the classifying disk has limited selectivity for particles of different sizes and it is difficult to form effective classification.

[0173] When the diameter of the classifying disk increases to over 127 mm, the escape behavior of particles of different sizes shows significant differentiation. The escape rate of 74 μm fluorite particles decreases rapidly and remains below 10% even when the disk diameter exceeds 132 mm; while the escape rate of 25 μm fluorite particles remains above 80%, exhibiting significant particle size-selective separation characteristics. Particularly when the disk diameter is 132 mm, the difference in escape rates between the two particle sizes reaches its maximum, indicating that this structural parameter can achieve optimal classification performance under current operating conditions. These results demonstrate that the method of this invention can accurately capture the influence of changes in the classifying disk structure on particle escape behavior and can be used to guide the quantitative optimization of classifying disk structural parameters.

[0174] To further reveal the formation mechanism of the difference in escape rate of particles with different sizes, a classification disk with a diameter of 132 mm was selected. The axial trajectory of 74 μm and 25 μm fluorite particles was extracted and analyzed. The relationship between their positions and time is as follows: Figure 6 As shown. By Figure 6 It can be seen that under stable flow field conditions, after entering the classification disk region, 25 μm fluorite particles can continuously migrate along the axial direction towards the outlet. Their axial position quickly exceeds the right end of the classification disk and achieves stable escape, indicating that they are less affected by resistance and inertia and can easily pass through the classification region with the fluid movement.

[0175] In contrast, 74 μm fluorite particles, upon approaching the classification disk region, exhibit a clear reciprocating fluctuation in their axial position between the two ends of the disk, remaining near the disk for an extended period and struggling to escape beyond the outlet. This indicates that larger particles are significantly more affected by the combined effects of the rotating flow field, reflux structure, and inertia near the classification disk, thus being effectively trapped. The simulation results corroborate the escape rate statistics and are consistent with the separation patterns observed in actual grinding and classification processes, further demonstrating that the numerical simulation-based classification prediction method of this invention possesses good accuracy and universality in analyzing particle separation behavior.

[0176] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0177] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of the classifying disc structure in an abrasive mill based on numerical simulation, characterized in that, Includes the following steps: Obtain a numerical simulation model of the flow field and particle field of the sand mill, which has been verified and corrected by actual experimental results; Determine the structural parameters, operating conditions, grinding media parameters, and target particle parameters of the sand mill under the target working conditions; Based on the structural parameters, a geometric model of the fluid domain of the sand mill was established, and the fluid domain was discretized by mesh. A fluid computation model is established based on the fluid domain mesh, and a particle computation model is established based on the geometric model. The fluid computational model and the particle computational model are coupled in two directions to obtain a stable flow field; Under the stable flow field conditions, target particles are introduced, and the axial migration behavior of the target particles is numerically tracked to calculate the escape rate of the target particles and obtain the classification effect of the classification disk. By changing the grading disk structure parameters and repeating the above numerical calculation process, the grading effects of different grading disk structures are compared to determine the grading disk structure with the best grading effect.

2. The method as described in claim 1, characterized in that, The structural parameters include: The dimensions of the outer shell of the sand mill, the dimensions of the feed inlet, the dimensions of the discharge outlet, the position of the feed inlet, the position of the discharge outlet, the radius of the mixing plate, the spacing between the mixing plates, the dimensions of the holes in the mixing plates, the position of the holes in the mixing plates, the spacing between the grading plate and the mixing plate, and the number of side holes of the grading plate. The operating conditions include: stirring disc speed, classifying disc speed, feed flow rate, grinding media filling rate, and grinding media size; The target particle parameters include the particle size and density of the target particles.

3. The method as described in claim 1, characterized in that, The particle calculation model sets the collision recovery coefficient, static friction coefficient, dynamic friction coefficient, and Young's modulus between the grinding media and the shell of the abrasive mill. The particle model adopts a non-slip contact model, and the particle motion parameters include the rotational speeds of the stirring disc and the classifying disc.

4. The method as described in claim 1, characterized in that, The fluid computation model sets the interface between the rotating domain and the static domain, the outer shell wall, the velocity inlet, and the pressure outlet as boundary conditions. The fluid phase adopts the Eulerian multiphase flow model, in which water is the main phase and air is the secondary phase. The turbulence model adopts the shear stress transfer model.

5. A method for evaluating and optimizing the structure of the classifying disc in an abrasive mill based on numerical simulation, characterized in that: Under stable flow field conditions, target particles of different sizes and densities were introduced, and the spatial distribution of the target particles in the axial direction of the sand mill was analyzed over time. The number of target particles leaving the Aisha mill from the discharge port within the set tracking time is counted, and the proportion of this number to the total number of target particles is used as the escape rate of the target particles. The difference between the escape rate of fine particles and the escape rate of coarse particles is used as the evaluation index for the grading effect; By changing the structural parameters of the grading disc and repeating the above calculation process, the quality of the grading disc structure is determined based on the magnitude of the grading effect evaluation index.

6. The method as described in claim 5, characterized in that, The axial migration behavior refers to the spatial distribution change process of target particles in the axial direction of the sand mill, and is mainly used to analyze the influence of the classification disk area on the backflow behavior of target particles.

7. The method as described in claim 5, characterized in that, The structural parameters of the grading disc include: the diameter of the grading disc, the number of disc holes, the position of the disc holes, and the diameter of the disc holes.

8. A numerical simulation-based optimization design system for the classifier disc structure of an abrasive mill, characterized in that, include: The correction module is used to correct the numerical simulation model of the flow field and particle field of the sand mill based on actual test results; The parameter determination module is used to determine the structural parameters, operating conditions, grinding media parameters, and target particle parameters under the target working conditions. The coupled calculation module is used to perform bidirectional coupled calculations between the fluid calculation model and the particle calculation model to obtain a stable flow field. The analysis module is used to calculate the axial migration behavior and escape rate of target particles under stable flow field conditions, and obtain the classification effect. The comparison and optimization module is used to determine the optimal grading disk structure by changing the grading disk structure parameters and comparing the grading effects.

9. The system as described in claim 8, characterized in that, The stable flow field is the state in which the flow field parameters and the motion state of the grinding media tend to stabilize over time.

10. The system as described in claim 8, characterized in that, The system is used for numerical simulation optimization design of the grading disc structure of the sand mill.

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