Targeted calibration and verification method for discrete element parameters of corn seeds

Abstract

The invention discloses a corn seed discrete element parameter targeted calibration and verification method, and relates to the field of agricultural engineering, and the method comprises the following steps: selecting different corn variety particles as research objects, and measuring direct measurement parameters of corn seed particles through actual experiments; analyzing the influence of a static friction coefficient between corn seed particles, a rolling friction coefficient between the particles and a rolling friction coefficient between the particles and a boundary on the behavior of a seed particle population through a volume density test and a self-flow screening test; through a Box-behnke experiment, the sensitivity relationship between the macrophysical phenomenon and the static friction coefficient between corn seed particles, the rolling friction coefficient between the particles and the rolling friction coefficient between the particles and the boundary is explored; and obtaining a non-direct measurement parameter targeted calibration method of the corn seed particles according to the sensitivity relationship, and verifying the calibrated parameters by using a cylinder lifting test and a shear box test. The problem that an existing calibration method is large in workload and has certain difficulty is solved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural engineering, and in particular to a method for targeted calibration and verification of discrete element parameters of corn seeds. Background Art

[0002] Corn is China's largest grain crop. During the process of corn planting, seed harvesting and storage, there is always contact with related mechanical parts. In the past, most of the methods used were experimental and statistical analysis methods to study the contact between seed particles and related mechanical parts. However, this method is time-consuming and labor-intensive, and the cycle is long. The results obtained are often only for a given situation, and it is difficult to obtain information such as the force between soil particles and seed particles, particle displacement and particle velocity. The use of the discrete element method (Discrete Element Method) (Cundall.1979) to analyze the contact between particles and mechanical parts can solve the above problems. At present, the discrete element method has become a general method for analyzing granular materials and has been widely used in the field of agricultural engineering. Among them, appropriate discrete element model parameters are necessary for accurately predicting the movement of corn seed particles and the interaction process between seeds and mechanical parts. There are three main methods for determining the mechanical parameters of granular material particles: Treating the particle as a solid, the mechanical parameters of a single particle are obtained through elastic-plastic theory analysis and single particle tests; The mechanical parameters of the particles were obtained through contact mechanics analysis and trial and error. Macroscopic mechanical tests, such as angle of repose, triaxial test, biaxial test and direct shear test, are used to obtain the macroscopic mechanical parameters of the particle group. Then, the relationship between the macroscopic parameters and the microscopic particle parameters is established to obtain the mechanical parameters of the particles.

[0003] Due to their irregular shape, corn seeds are difficult to measure directly, such as the coefficient of static friction between seed particles, the coefficient of rolling friction between seed particles, and the coefficient of rolling friction between seed particles and working parts. Parameters that are difficult to measure directly (i.e., parameters that cannot be directly measured) are generally obtained through a third method: parameter calibration. However, this method involves inverse parameter determination and is subject to the multi-solution problem. When more than two parameters are calibrated, more than one parameter combination can meet the requirements. While the response surface methodology can be used to identify the optimal parameter combination, this method generates multiple parameter combinations, and it is uncertain which combination is optimal. Thomas et al. explored this multi-solution problem and proposed using a stacking box test (taking into account the stacking angles of the upper and lower boxes, the discharge volume, and the mass change rate between 1 and 2 seconds) to determine the correct parameter combination. However, this calibration method is labor-intensive and increases the difficulty of parameter calibration. Summary of the Invention

[0004] In response to the above-mentioned deficiencies in the prior art, the present invention provides a targeted calibration and verification method for discrete element parameters of corn seeds, which solves the problem that the existing calibration methods are labor-intensive and difficult.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a method for targeted calibration and verification of discrete element parameters of corn seeds, comprising the following steps:

[0006] S1: Select different corn varieties as research objects, and measure the direct measurement parameters of corn seed particles through actual experiments;

[0007] S2: Through bulk density tests and gravity sieving tests, the effects of the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries on the group behavior of seed particles were analyzed;

[0008] S3: Through Box-Behnke experiments, explore the sensitivity of macroscopic physical phenomena to the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries;

[0009] S4: A targeted calibration method for non-directly measured parameters of corn seed particles is obtained based on the sensitivity relationship, and the calibrated parameters are verified using a cylinder lifting test and a shear box test to complete the targeted calibration and verification of discrete element parameters of corn seeds.

[0010] Furthermore, the directly measured parameters of the corn seed particles in S1 include moisture content, static friction coefficient between the seed and the boundary, collision recovery coefficient, elastic modulus and particle density;

[0011] The moisture content is measured by a normal pressure constant temperature drying method;

[0012] The static friction coefficient between the seed and the boundary is measured using the inclined plane method:

[0013]

[0014] in, is the static friction coefficient between the seed and the boundary, To obtain the value of the inclinometer at the moment when the organic glass slides by high-speed photography;

[0015] The collision recovery coefficient includes the inter-species collision recovery coefficient and the collision recovery coefficient between seeds and boundaries;

[0016] The interspecies collision recovery coefficient Using a simple pendulum test to measure:

[0017]

[0018] in, is the initial height of the seed particle 1, is the initial height of the seed particle 1, is the height of seed particle 2 after collision;

[0019] The collision restitution coefficient between the seed and the boundary Using a drop test to measure:

[0020]

[0021] in, is the rebound height of the particle, is the initial height of the particle;

[0022] The elastic modulus for:

[0023]

[0024]

[0025]

[0026] in, is the deformation of the material, is the corresponding test force, is the Poisson's ratio of the material, is the coefficient, and are the major and minor curvature radii of the material surface in contact with the upper plate, is the width of the seed, is the thickness of the seed;

[0027] The particle density Measured using the pycnometer method:

[0028]

[0029] in, is the density of distilled water at temperature t℃, For the quality of corn seeds, is the mass of the empty volumetric flask, is the mass of the volumetric flask filled with distilled water, is the mass of a volumetric flask containing corn seed particles and filled with distilled water.

[0030] Furthermore, the HM-new rebound contact model is used in S2 to calculate the contact force between corn seed particles and between particles and boundaries, and the formula is:

[0031]

[0032]

[0033] in, is the normal force, is the tangential force, is the equivalent Young's modulus, is the equivalent radius, is the total tangential overlap of the component ball k during one contact process, is the restitution coefficient, , is the equivalent mass, is the normal relative velocity component of the sphere at the contact point, For particles pointing from arrive The unit vector of , is the equivalent shear modulus, is the total tangential overlap of the spheres in a single contact process, is the tangential relative velocity component of the sphere at the contact point, is the static friction coefficient, is the unit tangent vector;

[0034] Then the net force and net moment acting on the particle are:

[0035]

[0036]

[0037] in, is the net force acting on the particle, is the net moment acting on the particle, is the number of spheres, is the number of contact points, is the number of pseudo contact points generated by the multi-sphere particle approximation, For contact points The position vector of For particles The position vector of the center of mass, is the rolling friction coefficient, For particles The unit angular velocity of .

[0038] Furthermore, the macroscopic physical phenomena in S3 include discharge time, dynamic angle of repose and screening rate;

[0039] The unloading time is determined by a stacking box test to determine the duration of the seed falling process;

[0040] The dynamic repose angle is determined by measuring the tilt angle of the seed population through a drum rotation test;

[0041] The sieve penetration rate is determined by a gravity sieving test to determine the mass ratio of seeds passing through the sieve holes.

[0042] Furthermore, the non-direct measurement parameter targeted calibration method of corn seed particles in S4 is:

[0043] The rolling friction coefficient between particles was calibrated using the unloading time. The static friction coefficient between particles was set to 0.2, the rolling friction coefficient between particles and the boundary was set to 0.03, and five levels of rolling friction coefficient from 0 to 0.12 were selected for linear fitting.

[0044] The static friction coefficient between particles was calibrated using the dynamic repose angle, the rolling friction coefficient between particles and the boundary was set to 0.03, the calibrated rolling friction coefficient between particles was input, and five levels of static friction coefficient from 0 to 0.36 were selected for linear fitting;

[0045] Gravity screening is used to calibrate the rolling friction coefficient between particles and boundaries.

[0046] Furthermore, the calibrated parameters are verified using a cylinder lift test and a shear box test in S4:

[0047] The cylinder lifting test measures the seed stacking angle by comparing simulation with actual conditions and verifies the accuracy of the calibration parameters.

[0048] The shear box test measures the seed shear angle by comparing simulation with actual results, verifying the accuracy of the calibration parameters;

[0049] The stacking angle and shear angle are both quantified by image recognition methods.

[0050] The beneficial effects of the present invention are:

[0051] The present invention has obtained that along with the increase of inter-particle static friction coefficient and inter-particle rolling friction coefficient on bulk density by analyzing the influence of inter-particle static friction coefficient and inter-particle rolling friction coefficient on bulk density, bulk density reduces gradually.Reason is due to the increase of inter-particle static friction coefficient and rolling friction coefficient, hinders the mutual movement between particles, causes porosity to increase, and bulk density diminishes.Along with the increase of particle and boundary rolling friction coefficient, seed sieving rate also has larger reduction thereupon, and reason is the increase of particle and boundary rolling friction coefficient, causes the resistance of seed through sieve aperture to increase, causes sieving rate to descend.Therefore inter-particle static friction coefficient, inter-particle rolling friction coefficient and particle and boundary rolling friction coefficient need to be accurately calibrated.

[0052] This study uses Box-Behnken experiments to investigate the sensitivity of inter-particle static friction, inter-particle rolling friction, and particle-boundary rolling friction to discharge time, dynamic angle of repose, and sieving rate. The results show that discharge time is only sensitive to the inter-particle rolling friction coefficient, while the dynamic angle of repose is sensitive to both inter-particle static and inter-particle rolling friction. Sieving rate is sensitive to all three: inter-particle static friction, inter-particle rolling friction, and particle-boundary rolling friction.

[0053] Based on sensitivity relationships, this paper develops a targeted calibration method for corn seed parameters. First, a single-factor test of discharge time directly calibrates the inter-particle rolling friction coefficient. Furthermore, a single-factor test of dynamic angle of repose calibrates the inter-particle static friction coefficient. Furthermore, a single-factor test of sieve penetration calibrates the particle-boundary rolling friction. The calibrated parameters are validated using cylinder lift and shear box tests. Comparison of actual test results with simulations verifies the validity and accuracy of the parameters, demonstrating the feasibility of the parameter calibration method. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a targeted calibration and verification method for discrete element parameters of corn seeds.

[0055] Figure 2 This is a comparison chart of the effects of interspecific static friction coefficient and rolling friction coefficient on bulk density of three corn varieties.

[0056] Figure 3 This is the response surface diagram of various parameters of Liangyu 99 corn seed particles and unloading time.

[0057] Figure 4 This is the response surface diagram of the parameters of Liangyu 99 corn seed particles and the dynamic angle of repose.

[0058] Figure 5 This is the response surface diagram of various parameters of Liangyu 99 corn seed particles and sieving rate.

[0059] Figure 6 This is a comparison chart of the single-factor test results of unloading time and interspecies dynamic friction coefficient.

[0060] Figure 7 This is a comparison chart of the single-factor test results of the dynamic angle of repose and the interspecies static friction coefficient.

[0061] Figure 8 This is a comparison chart of the single-factor test results of screening rate and seed and boundary rolling friction coefficient.

[0062] Figure 9 This is a comparison chart of the cylinder lifting simulation and actual test.

[0063] Figure 10Comparison chart of shear angle simulation and actual test. DETAILED DESCRIPTION

[0064] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0065] like Figure 1 As shown, a method for targeted calibration and verification of discrete element parameters of corn seeds includes the following steps:

[0066] S1: Select different corn varieties as research objects, and measure the direct measurement parameters of corn seed particles through actual experiments;

[0067] S2: Through bulk density tests and gravity sieving tests, the effects of the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries on the group behavior of seed particles were analyzed;

[0068] S3: Through Box-Behnke experiments, explore the sensitivity of macroscopic physical phenomena to the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries;

[0069] S4: A targeted calibration method for non-directly measured parameters of corn seed particles is obtained based on the sensitivity relationship, and the calibrated parameters are verified using a cylinder lifting test and a shear box test to complete the targeted calibration and verification of discrete element parameters of corn seeds.

[0070] In this example, the main varieties of corn grown in China's three major corn-producing areas (Liangyu 99, Xianyu 335, and Zhengda 999) were selected as research objects, and some physical parameters of corn seeds (i.e., moisture content, collision recovery coefficient, elastic modulus, particle density, and static friction coefficient between seed particles and plexiglass) were directly measured through actual experiments.

[0071] The directly measured parameters of the corn seed particles in S1 include moisture content, static friction coefficient between the seed and the boundary, collision recovery coefficient, elastic modulus and particle density;

[0072] The moisture content is measured by a normal pressure constant temperature drying method;

[0073] The static friction coefficient between the seed and the boundary is measured using the inclined plane method:

[0074]

[0075] in, is the static friction coefficient between the seed and the boundary, To obtain the value of the inclinometer at the moment when the organic glass slides by high-speed photography;

[0076] The collision recovery coefficient includes the inter-species collision recovery coefficient and the collision recovery coefficient between seeds and boundaries;

[0077] The interspecies collision recovery coefficient The simple pendulum test is used to measure the heights of the two particles at rest before and after their first collision. The calculation formula for the coefficient of restitution of the collision between the particles can be deduced as follows:

[0078]

[0079] in, is the initial height of the seed particle 1, is the initial height of the seed particle 1, is the height of seed particle 2 after collision;

[0080] The collision restitution coefficient between the seed and the boundary Using a drop test to measure:

[0081]

[0082] in, is the rebound height of the particle, is the initial height of the particle;

[0083] The elastic modulus of corn seeds was measured according to the ASAE S368.4 DEC2000 (R2008) standard. This experiment used spherical corn seeds. The slope of the force-displacement curve corresponding to the deformation phase is the elastic modulus of the corn seed. The elastic modulus of the seed can be calculated using the following formula:

[0084] The elastic modulus for:

[0085]

[0086]

[0087]

[0088] in, is the deformation of the material, is the corresponding test force, is the Poisson's ratio of the material, is the coefficient, and are the major and minor curvature radii of the material surface in contact with the upper plate, is the width of the seed, is the thickness of the seed;

[0089] The particle density Measured using the pycnometer method:

[0090]

[0091] in, is the density of distilled water at temperature t℃, For the quality of corn seeds, is the mass of the empty volumetric flask, is the mass of the volumetric flask filled with distilled water, is the mass of a volumetric flask containing corn seed particles and filled with distilled water.

[0092] Due to the irregular shape of corn seed particles, the rolling friction coefficients between particles and plexiglass, between particles themselves, and the static friction coefficient between particles cannot be directly measured. This study used three varieties of corn seeds (Liangyu 99, Xianyu 335, and Zhengda 999) as the research objects. By exploring the impact of these three parameters on the macroscopic physical phenomena of the particle population, the necessity of accurate parameter calibration was analyzed. Then, based on parameter sensitivity analysis, a solution for accurate parameter calibration was determined.

[0093] When constructing a corn seed particle model using the multi-ball method, the effects of multiple contacts must be considered. Therefore, the Hertz-Mindlin (no-slip) contact model and the HM-new rebound contact model are used to calculate the contact forces between particles and between particles and the wall, analyzing the impact of multiple contacts on particle motion. When using the HM-new rebound contact model, the contact forces between the constituent balls in a particle at the contact point can generally be divided into normal and tangential forces, expressed as follows:

[0094] In S2, the HM-new rebound contact model is used to calculate the contact force between corn seed particles and between particles and boundaries. The formula is:

[0095]

[0096]

[0097] in, is the normal force, is the tangential force, is the equivalent Young's modulus, , 、 、 and Particles and Young's modulus and Poisson's ratio, is the equivalent radius, , and The components are balls and The radius, is the total tangential overlap of the component ball k during one contact process, is the restitution coefficient, , , is the equivalent mass, , and is the mass of the sphere, is the normal relative velocity component of the sphere at the contact point, For particles pointing from arrive The unit vector of , and The components are balls Center of ball and contact point The position vector of , is the equivalent shear modulus, is the total tangential overlap of the spheres in a single contact process, is the tangential relative velocity component of the sphere at the contact point, is the static friction coefficient, is the unit tangent vector;

[0098] Then the net force and net moment acting on the particle are:

[0099]

[0100]

[0101] in, is the net force acting on the particle, is the net moment acting on the particle, is the number of spheres, is the number of contact points, is the number of pseudo contact points generated by the multi-sphere particle approximation, For contact points The position vector of For particles The position vector of the center of mass, is the rolling friction coefficient, For particles The unit angular velocity of .

[0102] The present invention analyzes the necessity of accurate calibration of the inter-species static friction coefficient, inter-species rolling friction coefficient, and rolling friction coefficient between seeds and organic glass through bulk density and "self-flow screening" simulation analysis. If the parameters have no significant effect on macroscopic physical phenomena, there is no need to obtain them through calibration.

[0103] The bulk density simulation analysis was performed using EDEM. The container size and shape was a cylindrical container with a bottom radius of 40 mm and a height of 150 mm. First, a circular particle plant with a radius of 10 mm was set on the upper part of the funnel, which generated 0.7 kg of seed particles at a speed of 0.3 kg / s. Then, after the particles stabilized, the scraper was moved at a speed of 1 m / s to scrape off the excess seed particles on the upper part of the container. Finally, the bulk density was calculated based on the remaining seed mass in the container and the internal volume of the container. When exploring the influence of non-directly measured parameters on bulk density, the other two parameters were set to 0.03, and the experimental coefficients were set to 0, 0.03, 0.06, 0.09, 0.15, 0.18, and 0.21. Single-factor experiments were conducted in 7 groups, and each experiment was repeated 5 times.

[0104] Gravity screening simulation analysis was performed using EDEM software. The screen surface was set at a 24-degree inclination angle, the screen apertures were 9 mm circular, and the screen plate was made of plexiglass. The experimental method involved first creating a 60 mm × 100 mm × 280 mm rectangular area within a storage box as a seed factory. Then, after the simulation began, 0.2 kg of corn seed particles were generated within the seed factory and accumulated at the bottom of the box. The plate was removed at a speed of 1 m / s, causing the seeds to move downward along the screen surface under gravity. Finally, after the seeds stabilized, the mass of seeds that passed through the screen apertures was calculated, and the seed penetration rate was calculated by dividing the mass of seeds that passed through the screen apertures by 0.2 kg. The rolling friction coefficient between the corn seeds and the boundary was set to 0, 0.06, 0.12, 0.18, and 0.24. Five replicates were performed for each test.

[0105] Because more than two parameters need to be calibrated, there is more than one set of parameters that meet the test requirements. To address this problem, the present invention explores the sensitivity of macroscopic physical phenomena to particle parameters and establishes a sensitive relationship network between particle parameters and macroscopic physical phenomena. The goal is to find the uniqueness of the sensitive relationship between macroscopic physical phenomena and particle parameters. In other words, a macroscopic physical phenomenon is only sensitive to one parameter. In this case, a single-factor test can be used for accurate parameter calibration.

[0106] Therefore, the unloading time, dynamic angle of repose and screening rate were taken as the objects of investigation, and the box-behnken test was carried out using Design-expert. The rolling and static friction coefficients between particles and the rolling friction coefficient between particles and boundaries were taken at three levels (as shown in Table 1) to study the sensitive relationship between macroscopic physical phenomena and particle parameters.

[0107] Table 1 Response surface factor level table

[0108]

[0109] The macroscopic physical phenomena in S3 include discharge time, dynamic repose angle and screening rate;

[0110] The unloading time is determined by a stacking box test to determine the duration of the seed falling process;

[0111] First, a 320 mm × 64 mm × 25 mm rectangular area was set up in the upper chamber of the stacking box as a seed factory. After the simulation began, 1.4 kg of seeds were generated in the seed factory at a rate of 1 kg / s and gradually accumulated in the upper chamber of the stacking box. After the seeds stabilized, the extraction plate was removed at a rate of 1 m / s, and the seed particles flowed by gravity into the lower chamber of the stacking box. After the seeds stabilized, the time it took for the seeds to fall was recorded as the unloading time. Each experiment was repeated three times.

[0112] The dynamic repose angle is determined by measuring the tilt angle of the seed population through a drum rotation test;

[0113] First, a cylindrical area with an inner diameter of 140 mm and a thickness of 60 mm was set up inside the drum as a seed pellet plant. After the simulation began, 0.94 kg of seeds were generated within the pellet plant at a rate of 1 kg / s. Once the seeds stabilized, they were rotated at a speed of 0.5 rad / s. During this rotation, the angle between the seed pile and the horizontal direction was defined as the dynamic repose angle. Image recognition was used to determine the dynamic repose angle. Three replicates were performed for each set of experiments.

[0114] The sieve penetration rate is determined by a gravity sieving test to determine the mass ratio of seeds passing through the sieve holes;

[0115] The sieve surface was set at a 24-degree inclination, with 9 mm circular holes. The sieve plate was made of plexiglass. The experimental procedure involved placing 0.2 kg of seeds in the load box. The insert was then quickly withdrawn, causing the seeds to roll on the sieve plate. Once the seeds stabilized, the mass of seeds that passed through the sieve plate was calculated and divided by 0.2 kg to calculate the sieve penetration rate. Each experiment was repeated five times.

[0116] Based on the above sensitivity analysis, a parameter calibration scheme was determined: first, the unloading time was used to calibrate the rolling friction coefficient between particles, then the dynamic repose angle was used to calibrate the static friction coefficient between particles, and finally, the rolling friction coefficient between particles and boundaries was calibrated through "grass-flow screening".

[0117] The non-direct parameter targeted calibration method of corn seed particles in S4 is as follows:

[0118] The rolling friction coefficient between particles was calibrated using the unloading time. The static friction coefficient between particles was set to 0.2, the rolling friction coefficient between particles and the boundary was set to 0.03, and five levels of rolling friction coefficient from 0 to 0.12 were selected for linear fitting.

[0119] The static friction coefficient between particles was calibrated using the dynamic repose angle, the rolling friction coefficient between particles and the boundary was set to 0.03, the calibrated rolling friction coefficient between particles was input, and five levels of static friction coefficient from 0 to 0.36 were selected for linear fitting;

[0120] Gravity screening is used to calibrate the rolling friction coefficient between particles and boundaries.

[0121] Since "self-flow screening" is sensitive to the rolling friction coefficient between particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries, after the rolling friction coefficient between particles and the static friction coefficient between particles are calibrated, the rolling friction coefficient between particles and boundaries can be calibrated using the "self-flow screening" single-factor test.

[0122] In S4, the calibrated parameters are verified using a cylinder lift test and a shear box test:

[0123] The cylinder lifting test measures the seed stacking angle by comparing simulation with actual conditions and verifies the accuracy of the calibration parameters.

[0124] In the cylinder lifting test, the materials of the cylinder and the tray are plexiglass. The radius of the cylinder is 60mm, the height is 200mm, and the thickness is 5mm. A thin wire is used to connect the cylinder to the motor. After connection, the stability of the cylinder must be ensured during movement. The rising speed of the cylinder is set to 5mm / s.

[0125] The experimental process is as follows: first, connect the cylinder to the motor smoothly, making sure that the bottom of the cylinder is parallel to the tray, add 360g of seeds into the cylinder, and then place the cylinder upright on the tray so that the seeds inside cannot leak out and the seeds inside should be evenly distributed. Due to the small mass of the seeds, static electricity will affect the experimental results, so it is necessary to touch the entire experimental device with a metal object to remove static electricity and reduce experimental errors. Then start the motor to make the cylinder move upward at a speed of 0.5mm / s. When the seeds completely fall out of the cylinder and gather into a pile, turn off the motor. The angle formed at this time is the stacking angle, and the size of the stacking angle is obtained using image recognition methods. Each set of experiments is repeated five times.

[0126] In the cylinder lift simulation analysis, a cylinder with an inner diameter of 60 mm and a height of 200 mm was set up, along with a tray with an inner diameter of 400 mm. A particle factory with a diameter of 60 mm and a thickness of 10 mm was placed on top of the cylinder to produce 360 g of seeds. The cylinder's movement speed was set at 5 mm / s. The simulation was stopped when the seeds completely fell out of the cylinder and piled up. The angle formed at this point was the stacking angle, and the stacking angle was determined using image recognition. Each experiment was repeated five times.

[0127] The shear box test measures the seed shear angle by comparing simulation with actual results, verifying the accuracy of the calibration parameters;

[0128] The stacking angle and shear angle are both quantified by image recognition methods.

[0129] In the shear box test, the surrounding panels were made of plexiglass, the inner chamber dimensions were 200 mm × 118 mm × 65 mm, and the seed mass used in the experiment was 813.5 g.

[0130] The experimental procedure involves first placing the device on a plexiglass plate so that its front end is 170 mm from the edge of the bottom plexiglass plate. Then, a baffle is placed to seal the sides of the device. Corn seeds are poured into the cavity, evenly distributed. The baffle is then quickly withdrawn, causing the seeds to pour out. When the seeds stabilize, the angle formed by the accumulation of seeds on the bottom plexiglass plate is the shear angle. Image recognition methods are used to determine the accumulation angle. Each experiment is repeated five times.

[0131] In the simulation analysis of the shear box experiment, the chamber dimensions were set to 200 mm × 118 mm × 65 mm, the baffle dimensions were 250 mm × 250 mm × 10 mm, and the base plate dimensions were 288 mm × 280 mm × 10 mm. The edges of the shear box were aligned with the edges of the base plate. The pellet plant dimensions were set to 200 mm × 65 mm × 15 mm, the generated seed mass was 813.5 g, and the baffle withdrawal speed was 1 m / s. The simulation was stopped when the seeds poured out of the shear box and stabilized. The angle formed at this point was the shear angle, and the stacking angle was determined using image recognition methods. Each experiment was repeated five times.

[0132] In one embodiment of the present invention, Figure 2 Figure 1 shows the effect of the interspecific static friction coefficient and rolling friction coefficient on bulk density for three corn varieties, where (a) is the inter-particle static friction coefficient, (b) is the inter-particle rolling friction coefficient, and (c) is the particle-boundary rolling friction coefficient. It can be seen that as the inter-particle static friction coefficient and rolling friction coefficient increase, the bulk density gradually decreases. This is because the increase in the inter-particle static friction coefficient and rolling friction coefficient hinders the mutual movement of particles, resulting in an increase in porosity and a decrease in bulk density. Therefore, the inter-particle static friction coefficient and rolling friction coefficient have a significant impact on the movement of particle groups and need to be accurately calibrated. As the rolling friction between the seed and the boundary increases, the seed's sieving rate decreases. This is because the increase in the rolling friction coefficient between the particle and the boundary increases the resistance of the seed to passing through the sieve aperture, resulting in a decrease in sieving rate. Therefore, it can be concluded that the rolling friction between the particle and the boundary has a significant impact on the movement of the particle group and needs to be accurately calibrated.

[0133] The sensitive relationship between macroscopic physical phenomena and particle parameters, taking Liangyu 99 as an example, the Box-Behnken experimental scheme and results are shown in Table 2.

[0134] Table 2 Liangyu 99 Box-behnken experimental plan and results

[0135]

[0136] Table 3 shows the variance regression analysis of the interspecific static friction coefficient, interspecific rolling friction coefficient, and rolling friction coefficient of seeds and boundaries on the unloading time of the three corn varieties. It can be seen from the data in the table that the regression model P <0.0001, the difference reached an extremely significant level, and the level of lack of fit was not significant (P>0.05), indicating that the equation fit is good. The above conclusions show that it is feasible to use this mathematical model to characterize the degree of influence of each factor on the response value. It can be seen from the table that the unloading time is only sensitive to the interspecific rolling friction coefficient and is not sensitive to the other two factors. In addition, the response surface diagram between the factors ( Figure 3 ) It can be seen that the change in the coefficient of static friction between seeds and the coefficient of rolling friction between seeds and boundaries does not significantly change the value of the unloading time.

[0137] Table 3. Test of sensitivity of Liangyu 99 unloading time

[0138]

[0139] Table 4 shows the effects of interspecific static friction coefficient, interspecific rolling friction coefficient, and rolling friction coefficient of seeds and boundaries on the dynamic angle of repose. As can be seen from the data in the table, the regression model P <0.0001, the difference reached an extremely significant level, and the level of lack of fit term was not significant (P>0.05), indicating that the equation fit was good. The above conclusions show that it is feasible to use this mathematical model to characterize the degree of influence of each factor on the response value. As can be seen from the table, the dynamic angle of repose of seeds is sensitive to interspecific static friction and rolling friction coefficient, but not to another factor. In addition, the response surface diagram between the factors ( Figure 4 ) It can be seen that increasing the rolling friction coefficient of the seed and the boundary does not significantly change the dynamic repose angle.

[0140] Table 4. Sensitivity test of the dynamic angle of rest of Liangyu 99

[0141]

[0142] Table 5 shows the effects of interspecific static friction coefficient, interspecific rolling friction coefficient, and rolling friction coefficient of seeds and borders on sieve penetration rate. The data in the table show that the regression model has a P < 0.0001, indicating an extremely significant difference. The lack-of-fit term is not significant (P > 0.05), indicating a good fit. The above conclusions indicate that it is feasible to use this mathematical model to characterize the degree of influence of various factors on the response value. The response surface plot ( Figure 5 ) It can be seen that the screening rate is sensitive to the inter-species static friction coefficient, inter-species rolling friction coefficient, and the rolling friction coefficient between seeds and boundaries.

[0143] Table 5 Sensitivity test of Liangyu 99 sieve penetration rate

[0144]

[0145] In summary, the unloading time is only sensitive to the inter-particle rolling friction coefficient, and is insensitive to the other two parameters; the dynamic repose angle is sensitive to the inter-particle static friction coefficient and the inter-particle rolling friction coefficient, but is insensitive to the rolling friction coefficient between the particle and the boundary; and the screening rate is sensitive to all three parameters. Based on the sensitive relationship between macroscopic physical phenomena and particle parameters, a parameter targeted calibration method was determined: first, the inter-particle rolling friction coefficient was calibrated using the unloading time. Then, based on the calibrated inter-particle rolling friction coefficient, the inter-particle static friction coefficient was calibrated using the dynamic repose angle. Finally, the rolling friction coefficient between the particle and the boundary was calibrated using "self-flow screening."

[0146] Figure 6 The figure shows the relationship between the unloading time of three varieties of corn seeds in the stacking box and the rolling friction coefficient between particles. The results show that as the rolling friction coefficient between particles increases, the flow time gradually becomes longer.

[0147] Taking Liangyu 99 seeds as an example, the relationship between the unloading time and the interspecies kinetic friction coefficient through linear fitting is:

[0148]

[0149] Substituting the unloading time of Liangyu 99 (3.23667 s) from the actual experiment into the equation for y yields the value of x (0.023), which is the inter-particle rolling friction coefficient of Liangyu 99. Similarly, the inter-particle rolling friction coefficients of Xianyu 335 and Zhengda 999 corn seeds are 0.021 and 0.035, respectively.

[0150] After determining the static friction coefficient between three varieties of corn seeds, since the dynamic angle of repose is sensitive to the static friction coefficient and rolling friction coefficient between particles, a single-factor experiment can be used to calibrate the static friction coefficient between particles through the dynamic angle of repose. Figure 7 The relationship between the dynamic angle of repose and the static friction coefficient between particles of three varieties of corn seeds in the stacking box is shown in the figure. The results show that as the static friction coefficient between particles increases, the dynamic angle of repose also gradually increases. Taking Liangyu 99 seeds as an example, the relationship between unloading time and dynamic friction between seeds is obtained through linear fitting:

[0151]

[0152] Substituting the dynamic angle of repose of Liangyu 99 (31.3654) from the actual experiment into the equation for y yields the value of x (0.211837), which is the inter-particle static friction coefficient of Liangyu 99. Similarly, the inter-particle static friction coefficients of Xianyu 335 and Zhengda 999 corn seeds are 0.260 and 0.281, respectively.

[0153] After determining the static friction coefficient and rolling friction coefficient between the three varieties of corn seeds, since the screening rate is sensitive to the three parameters of the static friction coefficient, rolling friction coefficient between particles and the rolling friction coefficient between particles and boundaries, the rolling friction coefficient between particles and boundaries can be directly calibrated through the influence of the rolling friction coefficient between particles and boundaries on the screening rate. Figure 8 The relationship between the sieving rate and the rolling coefficient between the grains and the boundary of three varieties of corn seeds in a stacking box is shown in the figure. The results show that as the rolling friction coefficient between the grains increases, the sieving rate gradually decreases. Taking Liangyu 99 seeds as an example, the relationship between the sieving rate and the dynamic friction between the grains and the boundary through linear fitting is:

[0154]

[0155] Substituting the experimental Liangyu 99 sieve penetration rate of 0.45475 into the equation for y yields the value of x (0.016691), which is the coefficient of rolling friction between the grains and the boundary of Liangyu 99. Similarly, the inter-particle rolling friction coefficients of Xianyu 335 and Zhengda 999 corn seeds are 0.014 and 0.019, respectively.

[0156] The present invention verifies the accuracy of the parameter calibration process through cylinder lifting and shear box tests. The simulation and test results of the three varieties are as follows Figure 9 、 10 As shown in the figure, the relative error between the results of the Yuantong lifting simulation and the actual test is within 6%, and the relative error between the results of the shear angle simulation and the actual test is within 3%, which proves the feasibility and effectiveness of the parameter calibration method proposed in the present invention.

[0157] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the invention.

Claims

1. A method for targeted calibration and verification of discrete element parameters of corn seeds, characterized in that: The following steps are involved: S1: Select different corn varieties as research objects, and measure the direct measurement parameters of corn seed particles through actual experiments; S2: Through bulk density tests and gravity sieving tests, the effects of the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries on the group behavior of seed particles were analyzed; S3: Through Box-Behnke experiments, explore the sensitivity of macroscopic physical phenomena to the static friction coefficient between corn seed particles, the rolling friction coefficient between particles, and the rolling friction coefficient between particles and boundaries; S4: A targeted calibration method for non-directly measured parameters of corn seed particles is obtained based on the sensitivity relationship, and the calibrated parameters are verified using a cylinder lifting test and a shear box test to complete the targeted calibration and verification of discrete element parameters of corn seeds.

2. The method for targeted calibration and verification of discrete element parameters of corn seeds according to claim 1, characterized in that: The directly measured parameters of the corn seed particles in S1 include moisture content, static friction coefficient between the seed and the boundary, collision recovery coefficient, elastic modulus and particle density; The moisture content is measured by a normal pressure constant temperature drying method; The static friction coefficient between the seed and the boundary is measured using the inclined plane method: ； in, is the static friction coefficient between the seed and the boundary, To obtain the value of the inclinometer at the moment when the organic glass slides by high-speed photography; The collision recovery coefficient includes the inter-species collision recovery coefficient and the collision recovery coefficient between seeds and boundaries; The interspecies collision recovery coefficient Using a simple pendulum test to measure: ； in, is the initial height of the seed particle 1, is the initial height of the seed particle 1, is the height of seed particle 2 after collision; The collision restitution coefficient between the seed and the boundary Using a drop test to measure: ； in, is the rebound height of the particle, is the initial height of the particle; The elastic modulus for: ； ； ； in, is the deformation of the material, is the corresponding test force, is the Poisson's ratio of the material, is the coefficient, and are the major and minor curvature radii of the material surface in contact with the upper plate, is the width of the seed, is the thickness of the seed; The particle density Measured using the pycnometer method: ； in, is the density of distilled water at temperature t℃, For the quality of corn seeds, is the mass of the empty volumetric flask, is the mass of the volumetric flask filled with distilled water, is the mass of a volumetric flask containing corn seed particles and filled with distilled water.

3. The method for targeted calibration and verification of discrete element parameters of corn seeds according to claim 1, characterized in that: In S2, the HM-new rebound contact model is used to calculate the contact force between corn seed particles and between particles and boundaries. The formula is: ； ； in, is the normal force, is the tangential force, is the equivalent Young's modulus, is the equivalent radius, is the total tangential overlap of the component ball k during one contact process, is the restitution coefficient, , is the equivalent mass, is the normal relative velocity component of the sphere at the contact point, For particles pointing from arrive The unit vector of , is the equivalent shear modulus, is the total tangential overlap of the spheres in a single contact process, is the tangential relative velocity component of the sphere at the contact point, is the static friction coefficient, is the unit tangent vector; Then the net force and net moment acting on the particle are: ； ； in, is the net force acting on the particle, is the net moment acting on the particle, is the number of spheres, is the number of contact points, is the number of pseudo contact points generated by the multi-sphere particle approximation, For contact points The position vector of For particles The position vector of the center of mass, is the rolling friction coefficient, For particles The unit angular velocity of .

4. The method for targeted calibration and verification of discrete element parameters of corn seeds according to claim 1, characterized in that: The macroscopic physical phenomena in S3 include discharge time, dynamic repose angle and screening rate; The unloading time is determined by a stacking box test to determine the duration of the seed falling process; The dynamic repose angle is determined by measuring the tilt angle of the seed population through a drum rotation test; The sieve penetration rate is determined by a gravity sieving test to determine the mass ratio of seeds passing through the sieve holes.

5. The method for targeted calibration and verification of discrete element parameters of corn seeds according to claim 1, characterized in that: The non-direct parameter targeted calibration method of corn seed particles in S4 is as follows: The rolling friction coefficient between particles was calibrated using the unloading time. The static friction coefficient between particles was set to 0.2, the rolling friction coefficient between particles and the boundary was set to 0.03, and five levels of rolling friction coefficient from 0 to 0.12 were selected for linear fitting. The static friction coefficient between particles was calibrated using the dynamic repose angle, the rolling friction coefficient between particles and the boundary was set to 0.03, the calibrated rolling friction coefficient between particles was input, and five levels of static friction coefficient from 0 to 0.36 were selected for linear fitting; Gravity screening is used to calibrate the rolling friction coefficient between particles and boundaries.

6. The method for targeted calibration and verification of discrete element parameters of corn seeds according to claim 1, characterized in that: In S4, the calibrated parameters are verified using a cylinder lift test and a shear box test: The cylinder lifting test measures the seed stacking angle by comparing simulation with actual conditions and verifies the accuracy of the calibration parameters. The shear box test measures the seed shear angle by comparing simulation with actual results, verifying the accuracy of the calibration parameters; The stacking angle and shear angle are both quantified by image recognition methods.

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