Paddle type stirrer structure parameter optimization method, device and equipment
By optimizing the structural parameters of the paddle mixer through 3D modeling, discrete element simulation, and NSGA-II multi-objective genetic algorithm, the problem of balancing mixing quality and energy consumption in the existing technology is solved, realizing efficient and low-consumption dry mortar mixing, which is adaptable to different material ratios and equipment models.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAQIAO UNIVERSITY
- Filing Date
- 2025-12-15
- Publication Date
- 2026-04-21
AI Technical Summary
Existing methods for optimizing the structural parameters of paddle mixers rely on trial and error based on experience, lacking systematic multi-objective collaborative optimization. This makes it difficult to achieve a balance between mixing quality and energy consumption, and also makes it difficult to adapt to different material ratios and equipment models.
A method for optimizing the structural parameters of a paddle mixer is constructed using 3D modeling, discrete element simulation, and the NSGA-II multi-objective genetic algorithm. By generating an assembly model, conducting simulation experiments, and using a polynomial prediction model, the number of blades, installation angle, and phase angle are optimized. Multi-objective optimization is achieved by combining the coefficient of variation and specific power index.
It achieves efficient and low-consumption mixing under different material ratios and equipment models, shortens the optimization cycle, improves mixing uniformity and energy efficiency, and provides a variety of feasible engineering design solutions.
Smart Images
Figure CN121902533A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of engineering machinery parameter optimization technology, and more specifically, to a method, apparatus, and equipment for optimizing the structural parameters of a paddle mixer. Background Technology
[0002] Dry-mixed mortar, a key material widely used in modern construction engineering, directly determines the mechanical properties, construction consistency, and final project quality of the finished mortar through its mixing quality. In the production process of dry-mixed mortar, paddle mixers have become the mainstream equipment due to their excellent convection and diffusion mixing capabilities, high mixing efficiency, and good adaptability to materials of different particle sizes. However, current methods for optimizing the structural parameters of paddle mixers still have significant limitations. Traditional optimization methods often rely on trial and error based on experience, such as adjusting the number of paddles according to a fixed ratio, or focusing only on a single objective—such as unilaterally pursuing mixing uniformity while ignoring energy consumption, or sacrificing mixing quality for the sake of reducing energy consumption. This single-objective or experience-driven approach fails to fully consider the strong coupling effect between core structural parameters such as the number of paddles, paddle installation angle, and paddle phase angle, often leading to contradictory results such as "increased energy consumption due to improved uniformity" or "energy saving but uneven mixing."
[0003] Furthermore, while some existing studies incorporate simulation techniques, they generally suffer from limitations in tools or methods. For example, some only use discrete element method (such as EDEM software) for particle motion simulation without effectively integrating it with statistical modeling and intelligent optimization algorithms; others only use response surface methodology for parameter analysis without combining it with high-precision physical simulation to obtain reliable input data. This fragmented optimization process not only makes it difficult to accurately quantify the combined impact of various structural parameters and their interactions on mixing uniformity (usually characterized by the coefficient of variation) and energy consumption (usually measured by specific power), but also results in long optimization cycles, poor generalization ability, and an inability to quickly adapt to different material ratios (such as combinations of sand and cement with different particle sizes) or different types of mixing equipment. More importantly, due to the lack of a systematic multi-objective collaborative optimization mechanism, existing methods struggle to achieve an engineering-acceptable balance between mixing quality and energy consumption, limiting their guiding value in actual production.
[0004] In view of the above, this application is hereby submitted. Summary of the Invention
[0005] The present invention aims to provide a method, apparatus and equipment for optimizing the structural parameters of a paddle mixer, in order to solve the common technical defects in existing methods for optimizing the structural parameters of paddle mixers, including relying on experience-based trial and error, using only a single-objective optimization strategy, ignoring the number of paddles, the coupling effect between the installation angle and the phase angle, the lengthy optimization cycle and poor adaptability to different material systems.
[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: A method for optimizing the structural parameters of a paddle mixer includes: S1. In 3D modeling software, a 3D model of a paddle mixer with variable structural parameters including the number of blade pairs, blade installation angle, and blade phase angle is created. An assembly model of the paddle mixer with the corresponding combination of structural parameters is generated according to the requirements of the response surface methodology. S2, Based on the assembly model of the paddle mixer, a particle mixing process simulation model is constructed in discrete element simulation software; S3, Mesh the internal computational domain of the three-dimensional model of the paddle mixer, conduct simulation experiments based on the particle mixing process simulation model, and calculate the coefficient of variation and specific power corresponding to each combination of structural parameters; S4. Based on the simulation results, analyze the structural parameters, establish a quadratic polynomial prediction model of structural parameters, coefficient of variation, and specific power, and use the NSGA-II multi-objective genetic algorithm to find the optimal solution set.
[0007] Preferably, the three-dimensional model of the paddle mixer includes a mixing container, two parallel mixing shafts, and multiple paddles installed on each mixing shaft, and the number of paddles on a single mixing shaft, the installation angle of the paddles, and the phase angle of the paddles are set as variable structural parameters.
[0008] Preferably, the steps for constructing the simulation model of the particle mixing process are as follows: First, set the material property parameters and interaction coefficients in the discrete element simulation software and add particles; Next, the assembly model of the paddle mixer was imported into the discrete element simulation software. The geometric model of the mixer was set to steel plate material, and the rotation speed and direction of the two mixing shafts were configured according to the actual working conditions. Two particle plants are set up diagonally above the mixer to generate cement particles and fine sand particles respectively. The particles are randomly generated in the particle plants and enter the mixing container under the action of gravity. Define the material properties and contact parameters of fine sand particles, cement particles, and the geometry of the mixer. Set the two mixing shafts to rotate in the same direction and their corresponding speeds. Set the simulation time step, total simulation duration, and mesh cell size to complete the construction of the simulation model of the particle mixing process.
[0009] Preferably, when meshing the internal computational domain of the three-dimensional model of the paddle mixer, the mesh size is 4 to 5 times the average particle size.
[0010] Preferably, the coefficient of variation is a core indicator used to measure the dispersion of particle distribution; the smaller the value, the more uniform the mixing. The formula is: ; ; in, The coefficient of variation; This represents the standard deviation of the target particle mass percentage within each grid. For the first The overall average concentration of target particles in the sample; Q is the total number of samples; For the first The mass of the target particles in each sample; For the first The total mass of particles in each sample.
[0011] Preferably, the specific power is used to reflect the energy consumption required to mix a unit mass of particles; the smaller the value, the higher the energy efficiency. The formula is: ; in, Specific power; This refers to the power consumption generated throughout the entire hybrid cycle; This represents the total mass of the stirred particles.
[0012] Preferably, the structural parameters and the coefficient of variation Specific power The formulas for the quadratic polynomial prediction model are as follows: ; ; in, The number of structural parameters; For constant terms; The coefficient of the first-order term represents the linear effect of a single structural parameter on the coefficient of variation. The coefficient of the quadratic term represents the nonlinear effect of a single structural parameter on the coefficient of variation. The interaction coefficients represent structural parameters. and The effect of coupling between them on the coefficient of variation; , Different structural parameters for the mixer; For constant terms; The coefficient for the first-order term represents the linear effect of a single structural parameter on the comparative power. The coefficients of the quadratic term represent the nonlinear influence of a single structural parameter on the comparative power. The interaction coefficients represent structural parameters. and The coupling effect between them affects the power.
[0013] Preferably, the NSGA-II multi-objective genetic algorithm takes minimizing the coefficient of variation and minimizing the specific power as its core objectives, and the formula is: ; in, It is a vector of decision variables composed of structural parameters, and satisfies the range constraints of each structural parameter. , These are the objective functions for minimizing the coefficient of variation and minimizing the specific power, respectively. This allows us to select the Pareto optimal solution set that satisfies the dominance relationship, and then combine the weights to obtain the optimal solution, expressed as: ; in, This is the optimal solution; , These are the weights for the coefficient of variation and the specific power, respectively.
[0014] The present invention also provides a device for optimizing the structural parameters of a paddle mixer, comprising: The 3D model building unit is used to build a 3D model of a paddle mixer in 3D modeling software, which includes variable structural parameters such as the number of blade pairs, blade installation angle, and blade phase angle. It also generates an assembly model of the paddle mixer with the corresponding combination of structural parameters according to the requirements of the response surface methodology. The simulation model building unit is used to build a particle mixing process simulation model in discrete element simulation software based on the assembly model of the paddle mixer. The simulation experiment unit is used to mesh the internal computational domain of the three-dimensional model of the paddle mixer, conduct simulation experiments based on the particle mixing process simulation model, and calculate the coefficient of variation and specific power corresponding to each combination of structural parameters. The simulation analysis unit is used to analyze structural parameters based on simulation results, establish a quadratic polynomial prediction model of structural parameters, coefficient of variation, and specific power, and use the NSGA-II multi-objective genetic algorithm to find the optimal solution set.
[0015] The present invention also provides a device for optimizing the structural parameters of a paddle mixer, including a processor and a memory. The memory stores a computer program that can be executed by the processor to implement the paddle mixer structural parameter optimization method described above.
[0016] The present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor of the device on which the computer-readable storage medium is located, implement a method for optimizing the structural parameters of a paddle mixer as described above.
[0017] In summary, compared with the prior art, the present invention has the following beneficial effects: This invention does not rely on a specific mixer model or fixed material ratio. Its parametric modeling and simulation process can be adapted to dry-mixed mortar systems with different sizes of mixing containers, different speed configurations, and different particle sizes. By adjusting material properties, particle plant settings, and simulation boundary conditions, this method can be directly transferred to the structural optimization of other dry particle mixing equipment, and has universality, systematicity, and engineering feasibility.
[0018] This invention is the first to deeply integrate the discrete element method, response surface modeling, and the NSGA-II multi-objective optimization algorithm, constructing a complete digital closed loop from geometric modeling to performance prediction and parameter optimization. By quantifying the coupling influence of the main and interaction effects of blade structural parameters on mixing uniformity and energy consumption, it breaks through the limitations of "single parameter adjustment" or "single objective orientation" in traditional optimization methods. The established quadratic polynomial prediction model can accurately reflect the nonlinear relationship between structural parameters and performance indicators, while the NSGA-II algorithm effectively solves the trade-off decision problem under multi-objective conflict. The final optimal solution set provides multiple feasible solutions for engineering design, supporting the flexible selection of optimal parameter combinations according to actual production needs.
[0019] This invention provides a scientific, reliable, and reproducible digital optimization path for the efficient and low-consumption design of dry-mixed mortar mixing equipment, significantly shortening the research and development cycle and improving equipment performance and energy utilization efficiency. Attached Figure Description
[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of a method for optimizing the structural parameters of a paddle mixer provided in Example 1.
[0022] Figure 2 This is a schematic diagram of the structure of the twin-shaft paddle mixer provided in Example 1.
[0023] Figure 3 This is a schematic diagram of the structure with the number of blade pairs (N=6\7\8) provided in Example 1.
[0024] Figure 4 The blade angle provided in Example 1 ( Schematic diagram of the structure (35°\45°\55°).
[0025] Figure 5 The blade phase angle provided for Embodiment 1 ( ) structural diagram.
[0026] Figure 6 The graph shows the coefficient of variation for different blade pairs (N) provided in Example 1.
[0027] Figure 7 Different blade angles provided for Example 1 ( The coefficient of variation plot.
[0028] Figure 8 Different blade phase angles provided for Embodiment 1 The coefficient of variation plot.
[0029] Figure 9 The power ratio curves for different structural parameters (number of blade pairs, blade angle, blade phase angle) provided in Example 1 are shown.
[0030] Figure 10 A graph showing the Pareto optimal solution set for multi-objective optimization provided in Example 1.
[0031] Figure 11 This is a schematic diagram of a paddle mixer structural parameter optimization device provided in Embodiment 2.
[0032] Figure descriptions: 1. Stirring container; 2. Paddle blade; 3. Stirring shaft; 4. Reverse-rotating paddle blade.
[0033] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation
[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0035] Example 1 Embodiment 1 of the present invention provides a method for optimizing the structural parameters of a paddle mixer, which can be implemented by a paddle mixer structural parameter optimization device (hereinafter referred to as parameter optimization device), specifically, executed by one or more processors within the parameter optimization device.
[0036] In this embodiment, the parameter optimization device may be an electronic device equipped with a processor, the processor having a computer program for the paddle mixer structural parameter optimization method and the computer program being executable, such as a computer, smartphone, smart tablet, workstation, etc., which are not limited here.
[0037] like Figure 1 As shown, a method for optimizing the structural parameters of a paddle mixer includes steps S1 to S4.
[0038] S1. In 3D modeling software, a 3D model of a paddle mixer with variable structural parameters including the number of blade pairs, blade installation angle, and blade phase angle is created. An assembly model of the paddle mixer with the corresponding combination of structural parameters is generated according to the requirements of the response surface methodology.
[0039] This embodiment uses a twin-shaft paddle mixer as an example, and its structure is as follows: Figure 2 As shown, it mainly includes a mixing container (1), two parallel mixing shafts (3), and multiple blades (2, 4) installed on the mixing shafts. Two particle plants are provided diagonally above the mixing container (1) to generate cement particles and fine sand particles, respectively.
[0040] The mixing vessel and mixing shaft of a twin-shaft impeller mixer were designed. Based on key structural parameters such as the number of impeller pairs, impeller angle, and impeller phase angle, Solidworks software was used for modeling and assembly. Figures 3 to 5 The figures show different numbers of blades (N) and different blade angles. ) and different blade phase angles ( The structural form of the blades, such as the number of blade pairs (6, 7, 8, etc.). Figure 3 As shown), blade angles (35°\45°\55°, such as...) Figure 4 (as shown) and blade phase angles (30°\60°\90°, as shown) Figure 5 (As shown), and export the .stl model file. These parameters are the core variables for optimization in this invention.
[0041] The structural parameter combination model of the response surface methodology was modeled using Solidworks software, and the .stl model file was exported to provide a basic model for subsequent experiments.
[0042] S2. Based on the assembly model of the paddle mixer, a simulation model of the particle mixing process is constructed in discrete element simulation software.
[0043] Specifically, first, in discrete element simulation software (such as EDEM software), material property parameters (particulate materials such as fine sand, cement, geometric model materials, etc.) and interaction coefficients (such as contact parameter coefficients) are set, and particles are added.
[0044] Because EDEM simulates a large number of small particles, the calculation speed will drop sharply. Therefore, the fine sand particles and cement particles are enlarged during the simulation calculation. The material properties are shown in Table 1, and the contact parameter coefficients between the materials are shown in Table 2.
[0045] Table 1. Material Properties
[0046] Table 2. Contact Parameters
[0047] Next, the assembly model of the paddle mixer was imported into the discrete element simulation software. The geometric model of the mixer was set to steel plate material, and the rotation speed and direction of the two mixing shafts were configured according to the actual working conditions.
[0048] Then, two particle plants are set up diagonally above the mixer to generate cement particles and fine sand particles respectively. The particles are randomly generated in the particle plants and enter the mixing container under the action of gravity.
[0049] Define the material properties and contact parameters of the fine sand particles, cement particles, and the geometry of the mixer; set the two mixing shafts to rotate in the same direction and their corresponding speeds; select the Hertz-Mindlin (no slip) model of the simulated particles as the contact model. Because the mixture of fine sand and cement particles is a dry-mixed mortar, no hydration reaction will occur, meaning there is no bonding force.
[0050] Set the simulation time step (e.g., 20% of the Rayleigh time step), total simulation duration (e.g., 35s), and mesh cell size (e.g., 3 times the minimum particle radius) to complete the construction of the simulation model for the particle mixing process.
[0051] S3, Mesh the internal computational domain of the three-dimensional model of the paddle mixer, conduct simulation experiments based on the particle mixing process simulation model, and calculate the coefficient of variation and specific power corresponding to each set of parameter combinations.
[0052] When dividing the computational domain into meshes, the mesh size is 4 to 5 times the average particle size of the particles in the internal computational domain of the three-dimensional model of the paddle mixer.
[0053] In this embodiment, the computational domain is divided into 12×12×13; the mass information of particles in each grid is exported, and the coefficient of variation of particles in the computational domain is calculated as an evaluation index of mixing uniformity.
[0054] The coefficient of variation is a core indicator used to measure the dispersion of particle distribution. The smaller the value, the more uniform the mixing. The formula is: ; ; in, The coefficient of variation; This represents the standard deviation of the target particle mass percentage within each grid. For the first The overall average concentration of target particles in the sample; Q is the total number of samples; For the first The mass of the target particles in each sample; For the first The total mass of particles in each sample.
[0055] from Figure 6 It can be seen that increasing the number of blades N leads to a decrease in the final coefficient of variation of dry-mixed mortar, that is, an increase in the uniformity of particle mixing. This is because increasing the number of blades leads to a reduction in the gap between blades. When the blades rotate, more particles are subjected to the force applied by the blades, which increases the particle diffusion mixing mechanism and thus enhances the particle mixing effect.
[0056] from Figure 7 It can be seen that at the blade angle The final coefficient of variation of dry-mixed mortar is lowest when the blade angle is 45°, which means the uniformity of particle mixing is the highest. This is because the force applied to the particles by the blades during the mixing process is divided into axial force and circumferential force. When the blade angle is lower, the blades are arranged almost horizontally, resulting in low axial force. When the blade angle is higher, the blades are arranged almost vertically, resulting in low circumferential force. When the blade angle is 45°, the blades take into account both circumferential and axial forces, thus achieving the best mixing effect.
[0057] from Figure 8 It can be seen that, with the change in blade phase angle As the phase angle increases, the final coefficient of variation of dry-mixed mortar decreases, meaning the uniformity of particle mixing increases. This is because a lower blade phase angle results in more continuous particle propulsion, meaning the convective mixing of particles is stronger during the mixing process than at a higher phase angle. This manifests as a lower coefficient of variation and better mixing uniformity in the early stages of mixing. However, in the later stages, the lower phase angle leads to better convective mixing but poorer diffusion mixing, resulting in lower mixing uniformity.
[0058] The specific power is used to reflect the energy consumption required to mix a unit mass of particles. The smaller the value, the higher the energy efficiency. The formula is: ; in, Specific power; This refers to the power consumption generated throughout the entire hybrid cycle; This represents the total mass of the stirred particles.
[0059] from Figure 9 It can be seen that the specific power increases with the increase of the number of blades. The lower the blade angle, the lower the specific power. The higher the blade phase angle, the lower the specific power.
[0060] S4. Based on the simulation results, analyze the structural parameters, establish a quadratic polynomial prediction model of structural parameters, coefficient of variation, and specific power, and use the NSGA-II multi-objective genetic algorithm to find the optimal solution set.
[0061] The structure of the twin-shaft impeller mixer was optimized using response surface methodology experiments. Each combination of structural parameters in the response surface methodology was modeled using Solidworks software, and each factor was coded. The coding table is shown in Table 3. Then, the model of each combination of structural parameters was simulated, and the coefficient of variation and specific power of each group were summarized, as shown in Table 4.
[0062] Table 3. Coding of each factor
[0063] Table 4 Response Surface Methodology
[0064] The importance of various structural factors on the coefficient of variation and specific power of the twin-shaft mixer and the influence of the interaction between structural factors were summarized by ANOVA. Finally, the fitting prediction equations of structural factors on the coefficient of variation and specific power of the twin-shaft impeller mixer were fitted.
[0065] Specifically, the structural parameters and the coefficient of variation Specific power The formulas for the quadratic polynomial prediction model are as follows: ; ; in, The number of structural parameters; For constant terms; The coefficient of the first-order term represents the linear effect of a single structural parameter on the coefficient of variation. The coefficient of the quadratic term represents the nonlinear effect of a single structural parameter on the coefficient of variation. The interaction coefficients represent structural parameters. and The effect of coupling between them on the coefficient of variation; , Different structural parameters for the mixer; For constant terms; The coefficient for the first-order term represents the linear effect of a single structural parameter on the comparative power. The coefficients of the quadratic term represent the nonlinear influence of a single structural parameter on the comparative power. The interaction coefficients represent structural parameters. and The coupling effect between them affects the power.
[0066] As shown in Table 5, the ANOVA results, assuming three structural factors, show significant results for both the coefficient of variation and the specific power response surface model. In the coefficient of variation model, the number of blades and blade angle significantly affect the coefficient of variation, while the blade phase angle significantly affects it. Furthermore, the interactions between the number of blades and the phase angle, and between the blade angle and the phase angle, significantly affect the coefficient of variation. In the specific power model, the number of blades and blade angle significantly affect the specific power, while the blade phase angle significantly affects it.
[0067] Table 5 ANOVA Results
[0068] Corresponding number of blade pairs, Corresponding to the blade angle, Corresponding blade phase angle.
[0069] The prediction equation for the coefficient of variation is as follows: ; The prediction equation for specific power is as follows: ; The structural parameters of a biaxial impeller mixer are optimized using the NSGA-II genetic algorithm for multiple objectives. Specifically, the coefficient of variation and specific power predicted by the response surface methodology, along with the ranges of various factors, are used as boundary conditions. The optimal solution set is then obtained through solving these equations. The NSGA-II multi-objective genetic algorithm focuses on minimizing the coefficient of variation and the specific power, as defined in the following formula: ; in, It is a vector of decision variables composed of structural parameters, and satisfies the range constraints of each structural parameter. , These are the objective functions for minimizing the coefficient of variation and minimizing the specific power, respectively. This allows us to select the Pareto optimal solution set that satisfies the dominance relationship, and then combine the weights to obtain the optimal solution, expressed as: ; in, This is the optimal solution; , These are the weights for the coefficient of variation and the specific power, respectively.
[0070] like Figure 10 The optimal solution set and selected optimal solution obtained through multi-objective optimization of Pareto are shown. By setting the weights between the coefficient of variation and specific power, the corresponding optimal solution is obtained with 8 blades, a blade angle of 46.7°, and a blade phase angle of 81.3°, corresponding to a coefficient of variation of 0.0696 and a specific power of 15.46. Simulation verification based on the optimized structural combination yielded a coefficient of variation of 0.0706 and a power-to-weight ratio of 15.42. The relative error was 0.5%, confirming the reliability of the optimization. Compared with the previous structure, the uniformity was improved by 17.3% and the energy consumption was reduced by 1.7%.
[0071] In summary, compared with the prior art, the present invention has the following beneficial effects: This invention does not rely on a specific mixer model or fixed material ratio. Its parametric modeling and simulation process can be adapted to dry-mixed mortar systems with different sizes of mixing containers, different speed configurations, and different particle sizes. By adjusting material properties, particle plant settings, and simulation boundary conditions, this method can be directly transferred to the structural optimization of other dry particle mixing equipment, and has universality, systematicity, and engineering feasibility.
[0072] This invention is the first to deeply integrate the discrete element method, response surface modeling, and the NSGA-II multi-objective optimization algorithm, constructing a complete digital closed loop from geometric modeling to performance prediction and parameter optimization. By quantifying the coupling influence of the main and interaction effects of blade structural parameters on mixing uniformity and energy consumption, it breaks through the limitations of "single parameter adjustment" or "single objective orientation" in traditional optimization methods. The established quadratic polynomial prediction model can accurately reflect the nonlinear relationship between structural parameters and performance indicators, while the NSGA-II algorithm effectively solves the trade-off decision problem under multi-objective conflict. The final optimal solution set provides multiple feasible solutions for engineering design, supporting the flexible selection of optimal parameter combinations according to actual production needs.
[0073] This invention provides a scientific, reliable, and reproducible digital optimization path for the efficient and low-consumption design of dry-mixed mortar mixing equipment, significantly shortening the research and development cycle and improving equipment performance and energy utilization efficiency.
[0074] Example 2 like Figure 11 As shown, the second embodiment of the present invention also provides a device for optimizing the structural parameters of a paddle mixer, comprising: The 3D model building unit is used to build a 3D model of a paddle mixer in 3D modeling software, which includes variable structural parameters such as the number of blade pairs, blade installation angle, and blade phase angle. It also generates an assembly model of the paddle mixer with the corresponding combination of structural parameters according to the requirements of the response surface methodology. The simulation model building unit is used to build a particle mixing process simulation model in discrete element simulation software based on the assembly model of the paddle mixer. The simulation experiment unit is used to mesh the internal computational domain of the three-dimensional model of the paddle mixer, conduct simulation experiments based on the particle mixing process simulation model, and calculate the coefficient of variation and specific power corresponding to each combination of structural parameters. The simulation analysis unit is used to analyze structural parameters based on simulation results, establish a quadratic polynomial prediction model of structural parameters, coefficient of variation, and specific power, and use the NSGA-II multi-objective genetic algorithm to find the optimal solution set.
[0075] Example 3 The third embodiment of the present invention also provides a device for optimizing the structural parameters of a paddle mixer, which includes a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the paddle mixer structural parameter optimization method described above.
[0076] Example 4 The fourth embodiment of the present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by the processor of the device where the computer-readable storage medium is located, implement the paddle mixer structural parameter optimization method described above.
[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for optimizing the structural parameters of a paddle mixer, characterized in that, include: A 3D model of a paddle mixer with variable structural parameters, including the number of blade pairs, blade installation angle, and blade phase angle, is created in 3D modeling software. An assembly model of the paddle mixer with the corresponding combination of structural parameters is generated according to the requirements of the response surface methodology. Based on the assembly model of the paddle mixer, a simulation model of the particle mixing process is constructed in discrete element simulation software. The internal computational domain of the three-dimensional model of the paddle mixer is meshed, and a simulation experiment based on the particle mixing process simulation model is conducted to calculate the coefficient of variation and specific power corresponding to each combination of structural parameters. Based on the simulation results, the structural parameters were analyzed, and a quadratic polynomial prediction model of the structural parameters, coefficient of variation, and specific power was established. The optimal solution set was obtained by using the NSGA-II multi-objective genetic algorithm.
2. The method for optimizing the structural parameters of a paddle mixer according to claim 1, characterized in that... The three-dimensional model of the paddle mixer includes a mixing container, two parallel mixing shafts, and multiple blades installed on each mixing shaft. The number of blades on a single mixing shaft, the blade installation angle, and the blade phase angle are set as variable structural parameters.
3. The method for optimizing the structural parameters of a paddle mixer according to claim 2, characterized in that... The steps for constructing the simulation model of the particle mixing process are as follows: First, set the material property parameters and interaction coefficients in the discrete element simulation software and add particles; Next, the assembly model of the paddle mixer was imported into the discrete element simulation software. The geometric model of the mixer was set to steel plate material, and the rotation speed and direction of the two mixing shafts were configured according to the actual working conditions. Two particle plants are set up diagonally above the mixer to generate cement particles and fine sand particles respectively. The particles are randomly generated in the particle plants and enter the mixing container under the action of gravity. Define the material properties and contact parameters of fine sand particles, cement particles, and the geometry of the mixer. Set the two mixing shafts to rotate in the same direction and their corresponding speeds. Set the simulation time step, total simulation duration, and mesh cell size to complete the construction of the simulation model of the particle mixing process.
4. The method for optimizing the structural parameters of a paddle mixer according to claim 3, characterized in that... When meshing the internal computational domain of the three-dimensional model of the paddle mixer, the mesh size is 4 to 5 times the average particle size.
5. The method for optimizing the structural parameters of a paddle mixer according to claim 3, characterized in that... The coefficient of variation is a core indicator used to measure the dispersion of particle distribution. The smaller the value, the more uniform the mixing. The formula is: ； ； in, The coefficient of variation; This represents the standard deviation of the target particle mass percentage within each grid. For the first The overall average concentration of target particles in the sample; Q is the total number of samples; For the first The mass of the target particles in each sample; For the first The total mass of particles in each sample.
6. The method for optimizing the structural parameters of a paddle mixer according to claim 5, characterized in that... The specific power is used to reflect the energy consumption required to mix a unit mass of particles. The smaller the value, the higher the energy efficiency. The formula is: ； in, Specific power; This refers to the power consumption generated throughout the entire hybrid cycle; This represents the total mass of the stirred particles.
7. The method for optimizing the structural parameters of a paddle mixer according to claim 6, characterized in that... The structural parameters and coefficient of variation Specific power The formulas for the quadratic polynomial prediction model are as follows: ； ； in, The number of structural parameters; , Index for the number of structural parameters; For constant terms; The coefficient of the first-order term represents the linear effect of a single structural parameter on the coefficient of variation. The coefficient of the quadratic term represents the nonlinear effect of a single structural parameter on the coefficient of variation. The interaction coefficients represent structural parameters. and The effect of coupling between them on the coefficient of variation; , Different structural parameters for the mixer; For constant terms; The coefficient for the first-order term represents the linear effect of a single structural parameter on the comparative power. The coefficients of the quadratic term represent the nonlinear influence of a single structural parameter on the comparative power. The interaction coefficients represent structural parameters. and The coupling effect between them affects the power.
8. The method for optimizing the structural parameters of a paddle mixer according to claim 7, characterized in that... The NSGA-II multi-objective genetic algorithm focuses on minimizing the mutation coefficient and minimizing the specific power, as shown in the following formula: ； in, It is a vector of decision variables composed of structural parameters, and satisfies the range constraints of each structural parameter. , These are the objective functions for minimizing the coefficient of variation and minimizing the specific power, respectively. This allows us to select the Pareto optimal solution set that satisfies the dominance relationship, and then combine the weights to obtain the optimal solution, expressed as: ； in, This is the optimal solution; , These are the weights for the coefficient of variation and the specific power, respectively.
9. A device for optimizing the structural parameters of a paddle mixer, characterized in that, include: The 3D model building unit is used to build a 3D model of a paddle mixer in 3D modeling software, which includes variable structural parameters such as the number of blade pairs, blade installation angle, and blade phase angle. It also generates an assembly model of the paddle mixer with the corresponding combination of structural parameters according to the requirements of the response surface methodology. The simulation model building unit is used to build a particle mixing process simulation model in discrete element simulation software based on the assembly model of the paddle mixer. The simulation experiment unit is used to mesh the internal computational domain of the three-dimensional model of the paddle mixer, conduct simulation experiments based on the particle mixing process simulation model, and calculate the coefficient of variation and specific power corresponding to each combination of structural parameters. The simulation analysis unit is used to analyze structural parameters based on simulation results, establish a quadratic polynomial prediction model of structural parameters, coefficient of variation, and specific power, and use the NSGA-II multi-objective genetic algorithm to find the optimal solution set.
10. A device for optimizing the structural parameters of a paddle mixer, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that can be executed by the processor to implement a method for optimizing the structural parameters of a paddle mixer as described in any one of claims 1-8.