Powder particle model parameter calibration method based on discrete element method

Abstract

The invention relates to the field of powder material manufacturing, and discloses a powder particle model parameter calibration method based on a discrete element method.The method comprises the steps that a static accumulation angle experiment is conducted, and the accumulation angle of micron-sized powder is obtained; performing particle size scaling according to a powder particle size distribution curve; discrete element intrinsic parameters are set according to powder characteristics and mold materials; setting to-be-calibrated parameters and ranges; performing PB design according to the to-be-calibrated parameters and the range; analyzing a test result and screening test parameters which have relatively obvious influence on the simplified and scaled powder accumulation angle; establishing a regression model and seeking an optimal combination by taking a relatively significant test parameter as an independent variable and taking an actually measured static accumulation angle as a target value; and finally determining the mineral particle model parameters based on the discrete element method through the established optimization combination verified by a numerical test. According to the method, the parameter calibration efficiency and the parameter calibration accuracy are improved, and an effective technical means is provided for key parameters of a powder particle numerical simulation experiment.

Description

Technical Field

[0001] This invention relates to the field of powder material manufacturing, and in particular to a method for calibrating powder particle model parameters based on the discrete element method. Background Technology

[0002] In recent years, the processing and preparation of powder materials has been widely used in various industries, especially in pharmaceuticals, metallurgy, chemicals, and advanced materials. The particle characteristics of powders have a crucial impact on their processing performance and the quality of the final product. With the continuous development of industrial production, the preparation process of powder materials is becoming increasingly complex. How to precisely control the particle size, morphology, flowability, and interactions of powders has become an important research direction for improving production efficiency, reducing costs, and enhancing product quality. To better understand and control the powder processing process, researchers are constantly proposing new methods and technologies.

[0003] The Discrete Element Method (DEM), as an effective numerical simulation tool, has been widely applied in the processing and preparation of powder materials. By simulating the interactions between particles, the DEM can accurately describe the motion, collision, and aggregation behaviors of particles, thereby helping researchers analyze and optimize processes such as powder flow, compaction, and mixing. The application of this method in powder engineering provides important theoretical basis for powder design and process optimization, greatly promoting the development of powder technology.

[0004] However, despite the promising future of the Discrete Element Method (DEM) in powder processing, accurate parameter calibration of the powder's particle characteristics is essential for accurately simulating powder behavior. The physical properties of powder particles, such as shape, size, density, and friction coefficient, directly affect the accuracy of the DEM simulation results. Therefore, parameter calibration is a necessary step to ensure the reliability of the simulation results. Only through calibration can parameters consistent with the actual material behavior be obtained, making the simulation process more realistic and providing more effective guidance for powder processing and preparation.

[0005] Therefore, it is necessary to provide a method to improve the calibration efficiency and accuracy of micron-sized powders. Summary of the Invention

[0006] The purpose of this invention is to propose a method for calibrating powder particle model parameters based on the discrete element method, thereby solving the technical problem of low calibration efficiency and accuracy of existing micron-sized powders.

[0007] Specifically, this invention provides a method for calibrating powder particle model parameters based on the discrete element method.

[0008] Specifically, the method includes the following steps: S1. Take micron-sized powder and conduct a static angle of repose experiment to obtain the angle of repose of the micron-sized powder; S2. Measure the intrinsic parameters of micron-sized powder particles and scale the particle size distribution; S3. Construct a constitutive model of powder particles based on the discrete element method, determine the contact range of the parameters to be calibrated, and encode them; S4. Following the Plackett-Burman design experiment sequence, set the contact parameters, conduct static packing angle simulation, and perform reliability analysis on the experimental results; S5. Screen the experimental variables that have a significant impact on the Plackett-Burman design, establish a regression model and seek the optimal combination; S6. Perform simulated packing angle verification on the obtained experimental parameters, and compare the results with the measured values ​​in step S1. If the error is less than the preset value, the parameter calibration of micron-sized powder particles is completed.

[0009] A storage device that stores instructions and data for implementing a parameter calibration method for a powder particle model based on the discrete element method.

[0010] A powder particle model parameter calibration device based on the discrete element method includes: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a powder particle model parameter calibration method based on the discrete element method.

[0011] The beneficial effects provided by this invention are: 1. This invention is based on the discrete element method to establish a powder particle model and simulate the packing behavior of powder particles; using the common and easily measurable static packing angle as the evaluation index and target, the parameters of powder particles are measured, overcoming the problems of low accuracy of model parameters, narrow applicability and large amount of calculation when there are many calibration parameters in the existing methods. 2. This invention is the first to propose using Plackett-Burman design to significantly screen micron-level powder parameters that require calibration; screening through experimental design reduces computational workload and improves screening efficiency. It overcomes the problems of conventional experiments requiring multiple iterations and cumbersome calculations. 3. This invention solves the key problem of difficulty in calibrating parameters of existing micron-level powder simulation models, and provides a reliable parameter model for powder numerical simulation research.

[0012] In summary, this invention provides an effective means for calibrating micron-level powder particle parameters. Applying this invention allows for simple and quick calibration of powder particle parameters, and offers advantages such as high accuracy, low computational load, and strong versatility. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a particle size distribution diagram of industrial alumina. Figure 3 The fitted curve between X3 and the static stacking angle; Figure 4 This is a schematic diagram of the hardware device used in this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0015] Before formally describing this invention, a general overview of the invention's solution is provided for ease of understanding. Furthermore, the relevant technical terms in this invention are uniformly defined as follows: 1. Plackett-Burman Experiment: This is a highly efficient screening experimental design method, mainly used in the early stages of an experiment when there are many potential influencing factors to be investigated, but resources (time, cost, materials, etc.) are limited, and only a small number of experiments can be conducted. Its core objective is to initially screen out the key main effect factors that have a significant impact on the response variable (outcome) with the fewest number of experiments.

[0016] 2. Angle of repose test: This is a test method used to measure the angle (i.e., angle of repose or angle of repose) between the inclined plane of a stable cone formed by granular materials (such as soil, sand, powder, etc.) in a natural accumulation state and the horizontal plane. This angle is a key parameter characterizing the flowability, frictional properties, and stability of bulk materials.

[0017] Please refer to Figure 1 The present invention provides a method for calibrating parameters of a powder particle model based on the discrete element method, comprising the following steps: S1. Take micron-sized powder and conduct a static angle of repose experiment to obtain the angle of repose of the micron-sized powder; It should be noted that the values ​​of D10, D50 and D90 were used as references when determining the particle size distribution characteristics of the micron-sized powder particles; where 5μm≤D10≤35μm; 30μm≤D50≤70μm; 70μm≤D90≤150μm.

[0018] It should be noted that in step S1, the angle of repose determination experiment uses a container made of known intrinsic parameters of the material. After the powder particles fall from a certain height above to complete the accumulation, the angle of repose is determined by graphic analysis software.

[0019] S2. Measure the intrinsic parameters of micron-sized powder particles and scale the particle size distribution; It should be noted that in step S2, among the intrinsic parameters obtained, the density of the powder particles is obtained by a true density meter; after the particle size distribution is determined by a laser particle size analyzer, a proportion of powder particles near the main peak of the particle size distribution is selected for magnification; the magnification factor of the selected powder particles is a preset range multiple. As an example, the present invention selects a proportion of powder particles near the main peak of the particle size distribution for magnification; the magnification factor of the selected powder particles is 2 to 15 times.

[0020] S3. Construct a constitutive model of powder particles based on the discrete element method, determine the contact range of the parameters to be calibrated, and encode them; It should be noted that step S3 specifically involves simplifying the powder particle morphology into a simple three-dimensional shape based on the microscopic morphology of the powder, determining the contact range of the calibration parameters, and setting at least one center point to number the upper limit, middle value, and lower limit of the parameter range as +1, 0, and -1, respectively, where the upper limit value is a preset multiple of the lower limit value. Specifically, in this invention, the upper limit value is 1-3 times the lower limit value.

[0021] S4. Following the Plackett-Burman design experiment sequence, set the contact parameters, conduct static packing angle simulation, and perform reliability analysis on the experimental results; It should be noted that step S4 specifically involves: using experimental design software, including Design-Expert and Jmp software, to conduct a simulation experiment on the angle of repose of micron-sized powders, and performing significance analysis and variance analysis on the experimental results to obtain the experimental results.

[0022] S5. Screen the experimental variables that have a significant impact on the Plackett-Burman design, establish a regression model and seek the optimal combination; It should be noted that if the p-value of the Plackett-Burman design model is lower than the preset value, and R... 2 and R 2 daj A p-value higher than the preset value indicates a good model fit. If the condition is not met, the experimental parameters need to be re-selected and the experiment repeated. If the condition is met, the models are sorted from smallest to largest p-value. As an example, if the p-value of the Plackett-Burman design model is less than 0.05, and R0.05... 2 and R 2 daj A value higher than 0.90 indicates a good model fit.

[0023] Furthermore, when analyzing the factors of the Plackett-Burman design, if there is exactly one parameter with p less than 0.05, the fixed variable method is used. With the other variables fixed, the factor is tested multiple times, and curve fitting is performed based on the different simulated static repose angles obtained. The R-squared value of the curve fitting is... 2 The value should not be lower than 0.90. If there are multiple parameters with p less than 0.05, response surface methodology should be used for analysis, and the static packing angle should still be used as the experimental index. Multiple parameters should be assigned range values, with the upper limit, middle limit, and lower limit numbered +1, 0, and -1, respectively, for further screening and determination.

[0024] S6. Perform simulated packing angle verification on the obtained experimental parameters, and compare the results with the measured values ​​in step S1. If the error is less than the preset value, the parameter calibration of micron-sized powder particles is completed.

[0025] After obtaining the optimal process parameters, the simulation experiment was repeated multiple times to obtain the static repacking angle and compare it with the actual static repacking angle. The angle difference was no more than 10%.

[0026] The present invention provides a specific embodiment as follows: In this embodiment, micron-sized alumina powder is used as the parameter calibration object. This powder is a raw material for electrolytic alumina and refractory materials. Multiple samples are taken from the same batch to calibrate the parameters of the micron-sized powder.

[0027] Step 1: Take an appropriate amount of industrial alumina powder and conduct a static angle of repose experiment. Repeat the experiment 5 times under different masses to obtain a total of 10 static angle of repose data. In this embodiment, the average static angle of repose is calculated to be 34.37°, which is the static angle of repose of the calibrated powder.

[0028] Step two: The density of the powder particles was obtained using a powder true density meter. Three measurements were performed for each group, and the average value was taken. The true density of this batch of industrial alumina powder was found to be 3240 kg / m³. 3 The particle size distribution of this batch of industrial alumina powder was determined using a laser particle size analyzer. Three measurements were taken, and the average value was calculated. The obtained particle size distribution was analyzed to obtain the particle size distribution characteristic D. 10 D 50 D 90 Value. Its particle size distribution results are as follows: Figure 2 As shown, the particle size of 89.50% of the powder near the main peak of the particle size distribution was magnified by a factor of 5. The selected particle size range was magnified from 32-140 μm to 160-740 μm.

[0029] By examining the microstructure of the powder particles under a scanning electron microscope, the morphology of the powder particles is simplified to a three-dimensional sphere. Considering that industrial alumina powder is a dry powder with a small particle size distribution and certain surface energy between particles, the Hertz-Mindlin with JKR model is selected as the collision and contact model between particles, and the Hertz-Mindlin standard model is selected as the model between particles and contact bodies.

[0030] Step three involves calibrating a total of seven parameters for this powder, listing and numbering them as follows: interparticle collision recovery coefficient X1, interparticle kinetic friction coefficient X3, particle surface energy X7, particle-ceramic plate collision recovery coefficient X4, particle-ceramic plate static friction X5, and particle-ceramic plate kinetic and static friction X6. These seven factors are independent variables, each with three levels: high, medium, and low, designated as -1, 0, and 1 respectively. The three levels were determined based on the database provided by the discrete element method software, references to similar powder literature, and preliminary experiments.

[0031] Table 1 shows the specific values ​​and ranges of each parameter required for calibration in this powder calibration experiment. The upper limit of any single parameter in the experimental design is limited to no more than three times the lower limit. The Design-Expert software was used, and experiments were conducted according to the Plackett-Burman design. A total of 13 experiments were performed, and the experimental factors and levels are shown in Table 2. Table 1 Parameters required for simulating the angle of repose of industrial alumina powder

[0032] Table 2 Numerical Experiment Factors and Levels

[0033] Step four: Using the Design-Expert software, experiments were conducted according to the Plackett-Burman design, totaling 13 experiments. The experimental combinations and corresponding simulated static packing angles are shown in Figure 3. After the experiments, significance analysis and variance analysis were performed on the results. The final parameter significance analysis table is shown in Table 4, and the significance and error of the overall design were evaluated.

[0034] Table 3 Plackett-Burman Experimental Protocol and Results

[0035] Table 4. Analysis of Experimental Variance

[0036] Statistical fitting of the model revealed that model R... 2A value of 0.9795 indicates that the model fits the data well and has strong predictive ability. Adjusted value (R²) 2 daj The p-value of 0.9437 indicates that the model can still effectively explain 94.37% of the variation after considering the number of variables, demonstrating both strong explanatory power and avoiding overfitting. Further analysis shows a signal-to-noise ratio of 12.756, indicating high quality experimental design and data, and that the model can accurately capture data changes, exhibiting high precision. According to Table 2.5, the analysis of variance shows that the p-value for the model term is 0.0035, which is less than 0.01, proving the model is significant and successful.

[0037] Analysis of the significance parameters revealed that only the X3 value was less than 0.01, while all other values ​​were greater than 0.05. This demonstrates that only the X3 value has a highly significant impact on the simulated static diagonal.

[0038] Step 5: For parameters with insignificant effects, the median value is used to ensure the reliability of the results. The specific parameter values ​​are as follows: X1=0.0325, X2=0.475, X4=0.325, X5=0.6, X6=0.175, and the JKR value is 0.03. To fully understand the impact of changes in the X3 value on the static stacking angle, the X3 value is gradually varied within the range of 0.1 to 0.4, and the corresponding changes in the static stacking angle are recorded. The relationship between the X3 value and the corresponding angle is shown in Table 5, and the fitting curve between X3 and the static stacking angle is shown in Table 5. Figure 3 As shown.

[0039] Table 5. Correspondence between values ​​and angles

[0040] The calculated value of X3, at 0.37, indicates that the simulated angle of repose matches the experimental angle of repose. This yields the specific parameters required for calibration.

[0041] Step six: Verify the simulated packing angle of the experimental parameters obtained from the simulation. The static packing angle obtained from the Fanghen packing experiment is 34.27°, which is only 0.29% different from the actual measured value of 34.37°, meeting the accuracy requirements of numerical simulation. That is, the preferred parameters of the powder model used in this experiment are those listed in Table 6, and the calibration method of micron-level powder particle model parameters based on the discrete element method is established.

[0042] Each embodiment of the present invention has its own emphasis, and parts not described in detail can be referred to the relevant descriptions of other embodiments. It should be emphasized that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the embodiments have been described in detail, those skilled in the art can still modify or make equivalent substitutions, and these modifications or substitutions will not change the core technical solutions of the present invention. The disclosed preferred embodiments are only used to illustrate the principles and practical applications of the present invention, and do not list all details, nor are they limited to specific implementation methods. Obviously, based on the content of this specification, the technical solutions can be modified and varied in many ways. Ultimately, the present invention is only limited by the claims and their equivalents.

[0043] Table 6 Optimal Parameter Combinations

[0044] Please see Figure 4 , Figure 4 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a powder particle model parameter calibration device 401 based on the discrete element method, a processor 402, and a storage device 403.

[0045] A powder particle model parameter calibration device 401 based on the discrete element method: The powder particle model parameter calibration device 401 based on the discrete element method implements the powder particle model parameter calibration method based on the discrete element method.

[0046] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the powder particle model parameter calibration method based on the discrete element method.

[0047] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the powder particle model parameter calibration method based on the discrete element method.

[0048] In summary, the beneficial effects of this invention are: 1. This invention is based on the discrete element method to establish a powder particle model and simulate the packing behavior of powder particles; using the common and easily measurable static packing angle as the evaluation index and target, the parameters of powder particles are measured, overcoming the problems of low accuracy of model parameters, narrow applicability and large amount of calculation when there are many calibration parameters in the existing methods. 2. This invention is the first to propose using Plackett-Burman design to significantly screen micron-level powder parameters that require calibration; screening through experimental design reduces computational workload and improves screening efficiency. It overcomes the problems of conventional experiments requiring multiple iterations and cumbersome calculations. 3. This invention solves the key problem of difficulty in calibrating parameters of existing micron-level powder simulation models, and provides a reliable parameter model for powder numerical simulation research.

[0049] In summary, this invention provides an effective means for calibrating micron-level powder particle parameters. Applying this invention allows for simple and quick calibration of powder particle parameters, and offers advantages such as high accuracy, low computational load, and strong versatility.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calibrating parameters of a powder particle model based on the discrete element method, characterized in that: The method includes the following steps: S1. Take micron-sized powder and conduct a static angle of repose experiment to obtain the angle of repose of the micron-sized powder; S2. Measure the intrinsic parameters of micron-sized powder particles and scale the particle size distribution; S3. Construct a constitutive model of powder particles based on the discrete element method, determine the contact range of the parameters to be calibrated, and encode them; S4. Following the Plackett-Burman design experiment sequence, set contact parameters, conduct static packing angle simulation, and perform reliability analysis on the experimental results; S5. Screen the experimental variables that have a significant impact on the Plackett-Burman design, establish a regression model and seek the optimal combination; S6. Perform simulated packing angle verification on the obtained experimental parameters, and compare the results with the measured values ​​in step S1. If the error is less than the preset value, the parameter calibration of micron-sized powder particles is completed.

2. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: When determining the particle size distribution characteristics of the calibrated micron-sized powder particles, the values ​​of D10, D50, and D90 are used as references, respectively; where 5μm≤D10≤35μm; 30μm≤D50≤70μm; and 70μm≤D90≤150μm.

3. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: In step S1, the angle of repose determination experiment uses a container made with known intrinsic parameters of the material. After the powder particles fall from a certain height above to complete the accumulation, the angle of repose is determined by graphic analysis software.

4. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: In step S2, among the obtained intrinsic parameters, the density of the powder particles is obtained by a true density meter; after the particle size distribution is determined by a laser particle size analyzer, powder particles near the main peak of the particle size distribution with a proportion not lower than a preset value are selected for magnification; the magnification factor of the selected powder particles is a preset range factor.

5. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: Step S3 specifically involves simplifying the powder particle morphology into a simple three-dimensional shape based on the microscopic morphology of the powder, determining the contact range of the calibration parameters, and setting at least one center point to number the upper limit, middle value, and lower limit of the parameter range as +1, 0, and -1, respectively, where the upper limit value is a preset range multiple of the lower limit value.

6. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: Step S4 specifically involves using experimental design software, including Design-Expert and Jmp software, to conduct a simulated angle of repose experiment for micron-sized powders, and performing significance analysis and variance analysis on the experimental results to obtain the experimental results.

7. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: If the p-value of the Plackett-Burman design model is lower than the preset value, and R 2 and R 2 daj If the value is higher than the preset value, it indicates that the model fits well. If the condition is not met, the experimental parameters need to be screened again and the experiment needs to be repeated. If the condition is met, the models are sorted in ascending order of p-value.

8. The method for calibrating powder particle model parameters based on the discrete element method as described in claim 1, characterized in that: When analyzing the factors of the Plackett-Burman design, if there is only one parameter with p less than the preset value, the fixed variable method is used; with the remaining variables fixed, the factor is taken multiple times, and curve fitting is performed based on the different simulated static repose angles obtained; where the R-squared value of the curve fitting is... 2 The value is not lower than the preset value; If there are multiple parameters with p less than the preset value, response surface methodology is used for analysis, and the static packing angle is still used as the experimental index. The multiple parameters are still numbered according to their range, with the upper limit, middle value, and lower limit numbered +1, 0, and -1, and then further filtered and determined.

9. A storage device, characterized in that: The storage device stores instructions and data to implement the powder particle model parameter calibration method based on the discrete element method as described in any one of claims 1 to 8.

10. A parameter calibration device for a powder particle model based on the discrete element method, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the powder particle model parameter calibration method based on the discrete element method as described in any one of claims 1 to 8.

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