Sunflower stalk discrete element model construction method

Abstract

The application provides a sunflower straw discrete element model construction method, comprising the following steps: separating and uniformly cutting the straw skin and the core of the solid sunflower straw to obtain straw skin samples and core samples; mixing the straw skin samples and the core samples according to a preset ratio to obtain straw skin core mixture, and performing a physical angle of repose test on the straw skin core mixture to obtain an average value of the angle of repose; performing a simulation angle of repose test based on a pre-designed straw skin sample model, a core sample model and an angle of repose test device model to obtain a simulation angle of repose and a relative error value between the simulation angle of repose and the average value of the physical angle of repose; determining an optimal discrete element contact parameter combination according to the simulation angle of repose and the relative error value; and establishing a sunflower straw discrete element model based on the optimal discrete element contact parameter combination. The method can improve the accuracy of the sunflower straw discrete element model.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation technology for grain processing, specifically relating to a method for constructing a discrete element model of sunflower straw. Background Technology

[0002] The sunflower stalk discrete element model (DEM) is a computational model based on the discrete element method (DEM) that can be used to simulate and analyze the physical behavior of sunflower stalks, seeds, or other granular materials. By simulating the processing of sunflower stalks in agricultural machinery (such as cutting, compressing, and bundling), it helps in designing more efficient mechanical structures and operating parameters.

[0003] Currently, when constructing discrete element models of sunflower stalks, the entire sunflower stalk is used as the experimental material to calibrate the discrete element contact parameters. However, different parts of the sunflower stalk have different tissue structures, mechanical properties, and contact behaviors. Therefore, the discrete element models of sunflower stalks constructed in this way cannot accurately simulate the physical behavior of sunflower stalks during agricultural machinery operations. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for constructing a discrete element model of sunflower straw.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for constructing a discrete element model of sunflower stalks includes:

[0007] The stalks and pith of the sunflower were separated and cut evenly to obtain stalk samples and pith samples. The stalk samples and pith samples were mixed in a preset ratio to obtain a stalk-pith mixture. The physical angle of repose of the stalk-pith mixture was tested to obtain the average value of the physical angle of repose.

[0008] Based on a pre-designed straw sample model, pith sample model, and angle of repose test device model, a simulated angle of repose test was conducted to obtain the simulated angle of repose and the relative error between the simulated angle of repose and the average value of the physical angle of repose. The optimal discrete element contact parameter combination was determined based on the simulated angle of repose and the relative error value.

[0009] A discrete element model of sunflower straw is established based on the optimal discrete element contact parameter combination.

[0010] Optionally, a simulated angle of repose test is conducted based on a pre-designed straw sample model, pith sample model, and angle of repose test device model to obtain the simulated angle of repose and the relative error between the simulated angle of repose and the average value of the physical angle of repose, including:

[0011] Within the range of discrete element contact parameters, determine the initial value and step size of each discrete element contact parameter and conduct the steepest climbing test.

[0012] In the steepest climbing test, the generation process of the angle of repose was simulated by using straw sample model, pith sample model and angle of repose test device model to obtain the simulated angle of repose and relative error value corresponding to different discrete element parameter combinations.

[0013] Optionally, the optimal combination of discrete element contact parameters is determined based on the simulated repose angle and relative error value, including:

[0014] The optimal discrete element contact parameter combination is the combination of discrete element contact parameters corresponding to the minimum error value among the relative error values; or...

[0015] The value of the discrete element contact parameter combination corresponding to the minimum relative error value in the steepest climb test results is taken as the zero level value, the value of the discrete element contact parameter combination corresponding to the first relative error value is taken as the high level value, and the value of the discrete element contact parameter combination corresponding to the second relative error value is taken as the low level value. A Box-Behnken test is then conducted to establish a second-order angle-of-pack regression model. Here, the first relative error is the larger of the two relative errors adjacent to the minimum relative error value in the Box-Behnken test results, and the second error is the smaller of the two relative errors adjacent to the minimum relative error value in the Box-Behnken test results.

[0016] Using the average physical packing angle as the objective function and the intervals between high and low levels of the discrete element contact parameter combination as constraints, the discrete element contact parameters in the second-order packing angle regression model are solved to obtain the optimal discrete element contact parameter combination.

[0017] Optionally, before conducting the steepest climb test, the following may also be included:

[0018] The average physical angle of repose is used as the target value, and each discrete element parameter is used as a variable to conduct Plackett-Burman experiments to determine the target discrete element contact parameter. The target discrete element contact parameter is the parameter among the discrete element contact parameters whose contribution to the angle of repose is greater than a preset threshold.

[0019] Within the selected range of discrete element contact parameters, determine the initial values ​​and step sizes of each discrete element contact parameter to conduct the steepest climbing test, including:

[0020] Within the selected range of target discrete element contact parameters, the initial values ​​and step sizes of each target discrete element contact parameter are determined to conduct the steepest climbing test.

[0021] Optionally, the average physical packing angle is used as the target value, and Plackett-Burman experiments are conducted with each discrete element parameter as a variable to determine the target discrete element contact parameters, including:

[0022] Different combinations of discrete element contact parameters are generated based on the selection range of each discrete element contact parameter.

[0023] Simulated angle of repose experiments were conducted under different combinations of discrete element contact parameters to obtain different combinations of discrete element contact parameters and corresponding simulated angles of repose.

[0024] Based on the results of the simulated angle of repose test, an angle of repose regression model was established. The significance of the angle of repose regression model was analyzed to determine the contribution rate of each discrete element contact parameter to the angle of repose.

[0025] Discrete element contact parameters whose contribution rate values ​​are greater than a preset threshold are used as target discrete element contact parameters.

[0026] Optionally, a sunflower straw discrete element model is established based on the optimal discrete element contact parameter combination, including:

[0027] Based on the actual size of the straw bark and the actual size of the pith core, complete three-dimensional models of the straw bark and pith core were established respectively.

[0028] Multi-spherical straw granules and core granules were designed based on complete three-dimensional models of straw bark, complete three-dimensional models of pith, and optimal discrete element contact parameter combinations.

[0029] By combining straw husk particles and pith particles into complete straw particles using a meta-model particle modeling approach, a discrete element model of sunflower straw is obtained.

[0030] Optionally, it also includes:

[0031] Based on the dimensions of the actual straw husk, the actual pith, and the actual angle of packing test apparatus in the physical angle of packing test, a three-dimensional model of the straw husk, a three-dimensional model of the pith, and a model of the angle of packing test apparatus were designed respectively. The straw husk and pith three-dimensional models were then filled with particles to obtain straw husk sample models and pith sample models.

[0032] The sunflower straw discrete element model construction method provided by this invention has the following beneficial effects:

[0033] Since the husk and pith of straw are the main components of straw, and the two have different physical and mechanical properties, this invention separates the husk and pith of actual sunflower straw and conducts a physical angle of repose test on the mixture of husk and pith. Simulated angle of repose tests are then conducted on husk and pith sample models to determine the optimal discrete element contact parameters. This allows for a more realistic reflection of the behavior of the husk and pith under stress in both physical and simulated angle of repose tests, reflecting the differences in their characteristics and obtaining more accurate discrete element contact parameters. Consequently, the constructed discrete element model of sunflower straw can more accurately simulate the real behavior of sunflower straw in actual agricultural machinery operations, such as cutting, compressing, and baling. Attached Figure Description

[0034] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A schematic flowchart illustrating a method for constructing a discrete element model of sunflower stalks, provided in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of an experimental material provided in an embodiment of the present invention. Figure 2 Figure (a) shows a straw sample. Figure 2 Figure (b) shows the core sample;

[0037] Figure 3 This is a schematic diagram of an angle of repose testing device provided in an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of a three-dimensional model provided in an embodiment of the present invention. Figure 4 Figure (a) shows a three-dimensional model of the straw. Figure 4 Figure (b) shows a three-dimensional model of the core.

[0039] Figure 5 This is a schematic diagram of a model of an angle of accumulation test device provided in an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of a sample model provided in an embodiment of the present invention. Figure 6 Figure (a) shows a straw sample model. Figure 6 Figure (b) shows a core sample model;

[0041] Figure 7 This is a schematic diagram comparing the stacking states of materials provided in an embodiment of the present invention. Figure 7Figure (a) shows the stacking state of the straw pith and core mixture in the physical test. Figure 7 Figure (b) shows the stacking state of the straw pith mixture in the simulation test;

[0042] Figure 8 This is a comparison chart of compression tests provided in an embodiment of the present invention. Figure 8 Figure (a) in the diagram is a schematic diagram of a physical compression test. Figure 8 Figure (b) in the diagram is a schematic diagram of the simulated compression test;

[0043] Figure 9 A pressure-time curve provided for an embodiment of the present invention;

[0044] Figure 10 This is another schematic diagram of a three-dimensional model provided in an embodiment of the present invention. Figure 10 Figure (a) shows a complete three-dimensional model of the straw. Figure 10 Figure (b) shows a complete three-dimensional model of the core.

[0045] Figure 11 This invention provides a simulation model of straw bark and pith core. Figure 11 Figure (a) shows a simulation model of straw. Figure 11 Figure (b) shows the core simulation model;

[0046] Figure 12 This invention provides a straw simulation model. Figure 12 Figure (a) in the figure is a front view of the complete straw simulation model. Figure 12 Figure (b) in the figure is an isometric view of the complete straw simulation model;

[0047] Figure 13 This is a schematic diagram illustrating the generation process of a simulated angle of accumulation, provided as an embodiment of the present invention. Detailed Implementation

[0048] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0049] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the technical solution of this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0050] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "connected" or "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, which will not be elaborated further here.

[0051] First, some of the terms involved in this invention will be explained.

[0052] Discrete Element Method (DEM) is a numerical simulation method used to solve and analyze the dynamics of complex discrete particle assembly systems. It simulates the interactions between bulk materials and between bulk materials and structural components by establishing a parameterized model of the solid particle system, setting the properties of the particle system, and simulating the interactions between bulk materials and bulk materials and structural components within the particle system.

[0053] With the rapid development of information science and technology, the discrete element method (DEM) has been widely applied in agricultural and pastoral engineering. By using EDEM software to simulate and analyze the interaction behavior between particles and between particles and geometric components, DEM simulation can effectively evaluate the operational performance of agricultural and pastoral equipment, providing a reference for subsequent research on bundling mechanisms and physical experiments.

[0054] EDEM discrete element simulation software offers advantages such as improved work efficiency and shortened development cycles during product development. EDEM software features modeling, solving, and post-processing capabilities. It allows for visual adjustments to imported geometric component models and performs detection, feedback, and optimization based on interactions between particles and between particles and geometric components. Furthermore, it can be coupled with kinematics and dynamics software such as ADAMS, ANSYS, and FLUENT to solve multi-state coupled physics problems. EDEM software primarily consists of three functional modules: a pre-processing module (Creator), a solving module (Simulator), and a post-processing module (Analyst).

[0055] Example 1

[0056] Based on this, embodiments of the present invention provide a method for constructing a discrete element model of sunflower straw, such as... Figure 1 As shown, the method includes the following steps:

[0057] S1. Separate and uniformly cut the stalks and pith of the sunflower stalks to obtain stalk samples and pith samples. Mix the stalk samples and pith samples according to a preset ratio to obtain a stalk-pith mixture. Perform a physical angle of repose test on the stalk-pith mixture to obtain the average value of the physical angle of repose.

[0058] For example, sunflower stalks with a moisture content of approximately 20% were selected as the test material. The stalk husk and pith were separated and uniformly cut to obtain stalk husk samples and pith samples, as shown below. Figure 2 As shown, Figure 2 Figure (a) shows a straw sample. Figure 2 Figure (b) shows the core sample.

[0059] Then, the straw husk sample and the pith sample are mixed in a preset ratio to obtain a straw husk and pith mixture. The preset ratio can be determined according to the composition ratio of straw husk and pith in sunflower straw, for example, 7:1.

[0060] Furthermore, a physical angle of repose test was conducted on the straw pith and core mixture using an angle of repose testing device to obtain the average value of the physical angle of repose.

[0061] Figure 3 This is a schematic diagram of an angle of repose testing device provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes a microcomputer-controlled electronic universal testing machine 1, a lifting cylinder 2, and a tray 3. This angle-of-accumulation testing device can be used to perform physical angle-of-accumulation tests.

[0062] Specifically, the lower edge of the lifting cylinder 2 is first brought into contact with the tray 3 using a universal testing machine 1, ensuring that the lifting cylinder 2 and tray 3 are concentric. After the straw and pith samples are mixed evenly, the straw and pith mixture is added into the lifting cylinder 2 through the top opening. During the test, the lifting cylinder 2 is loaded at a constant speed of 10 mm / min. After the straw and pith mixture has completely detached from the lifting cylinder 2 and come to rest, a front view image of the straw and pith mixture is taken along the plane of the base. Then, the coordinate data of the outline of the piled material is fitted using an image digitization tool. The test is repeated N times (N is a positive integer greater than or equal to 1), and the angle of repose obtained each time is recorded. The average value of the physical angle of repose is calculated.

[0063] S2. Based on the pre-designed straw sample model, pith sample model, and angle of repose test device model, a simulated angle of repose test is conducted to obtain the simulated angle of repose and the relative error value between the average value of the simulated angle of repose and the physical angle of repose. The optimal discrete element contact parameter combination is determined based on the simulated angle of repose and the relative error value.

[0064] For example, three-dimensional models of the straw bark and pith can first be created using 3D modeling software (such as SolidWorks), such as... Figure 4 As shown, Figure 4 Figure (a) shows a three-dimensional model of the straw. Figure 4 Figure (b) shows the three-dimensional model of the pith. To improve the rationality and accuracy of the natural angle of packing test of the sunflower straw and pith mixture, the dimensions of the three-dimensional models of the straw and pith are consistent with the actual objects.

[0065] Simultaneously, a model of the angle of repose test apparatus can be generated using EDEM software. The lifting cylinder type is set to solid, the material to steel, and the diameter and height to be the same as the actual dimensions, with both the top and bottom surfaces of the cylinder set to open. The tray type is set to solid, the material to steel, and the tray diameter to be equal to the actual diameter, with the lower end face of the lifting cylinder coinciding with the tray. Figure 5 As shown, Figure 5 This is a schematic diagram of a stacking angle test device model provided in an embodiment of the present invention, which includes a lifting cylinder 51 and a tray 52.

[0066] Furthermore, particles were filled into the outer contours of the three-dimensional models of the straw and pith respectively to obtain the straw sample model and the pith sample model, as follows: Figure 6 As shown, Figure 6 Figure (a) shows a straw sample model. Figure 6 Figure (b) shows a core sample model.

[0067] For example, in the straw and pith particle material panel of the drawing software, select single spherical particles and name them "jie pi particle" and "sui xinparticle" respectively. Then, import the straw and pith 3D model into the EDEM software, select the number of mesh elements and set the smoothing value to generate multi-spherical straw and pith particles. Fill the particles into the straw and pith 3D model to obtain the straw sample model and the pith sample model.

[0068] Furthermore, based on the designed straw sample model, pith sample model, and angle of repose test device model, a simulated angle of repose test was conducted to obtain the simulated angle of repose and the relative error between the simulated angle of repose and the average value of the physical angle of repose.

[0069] Before conducting the simulated angle of repose test, a contact model is first selected to simulate various physical phenomena of particulate materials when subjected to external forces, such as elastic deformation, plastic deformation, friction, wear, breakage and adhesion.

[0070] Considering that sunflower stalks have a certain moisture content and are a sticky material, and that the surface energy of the HM-JKR (Hertz-Mindlin with JKR) model can better simulate the cohesive force between particles, the HM-JKR model was selected as the contact model in this embodiment of the invention.

[0071] Based on the selected contact model, test material, and material of the angle of repose test device, the discrete element contact parameters in this embodiment of the invention include: straw-straw recovery coefficient, straw-straw static friction coefficient, straw-straw rolling friction coefficient, straw-core recovery coefficient, straw-core static friction coefficient, straw-core rolling friction coefficient, straw-Q235 steel static friction coefficient, straw-Q235 steel rolling friction coefficient, straw JKR surface energy, core JKR surface energy, and straw-core JKR surface energy.

[0072] It should be understood that discrete element contact parameters can help discrete element models more accurately simulate the physical behavior of particulate materials in the real world, such as collisions, friction, and adhesion between particles. Accurate discrete element contact parameters can make simulation results closer to reality. Therefore, process S2 is the process of calibrating discrete element contact parameters through simulated angle of repose tests.

[0073] Furthermore, fixed intrinsic parameters were set for the straw bark sample model, the pith core sample model, and the angle of repose test device model, respectively.

[0074] The intrinsic parameters of the straw sample model include Poisson's ratio, straw density, and straw elastic modulus; the intrinsic parameters of the core sample model include Poisson's ratio, core density, and core elastic modulus; and the intrinsic parameters of the angle of repose test device model include Poisson's ratio, steel density, and steel elastic modulus. Table 1 provides an example of one set of intrinsic parameter values ​​provided by an embodiment of the present invention.

[0075] Table 1 Examples of intrinsic parameters

[0076] parameter numerical values <![CDATA[Poisson's ratio v1 of straw skin]]> 0.3 <![CDATA[Straw skin density ρ sp / kg·m -3 > 408.56 <![CDATA[Elastic modulus E1 of straw skin / MPa]]> 1120.84 <![CDATA[Core Poisson's ratio v2]]> 0.4 <![CDATA[Core density ρ ss / kg·m -3 > 74.06 <![CDATA[Elastic modulus E2 of the core / MPa]]> 4.50 <![CDATA[Poisson's ratio v3 of steel]]> 0.25 <![CDATA[Steel density ρ3 / kg·m -3 > 7850 <![CDATA[Elastic modulus E3 of steel / MPa]]> 79000

[0077] It should be understood that the densities of straw and pith in Table 1 are the average particle densities when the moisture content is about 20%, and the elastic moduli of straw and pith are the average elastic moduli when the moisture content is about 20%.

[0078] Then, different combinations of discrete element contact parameters were designed according to the selection range of discrete element contact parameters. Under different combinations of discrete element contact parameters, the generation process of the angle of repose was simulated by straw sample model, pith sample model and angle of repose test device model. Several sets of test result data were obtained, including discrete element contact parameter combinations, corresponding simulated angle of repose and relative error values.

[0079] The relative error value is the relative error between the average value of the simulated stacking angle and the physical stacking angle. Furthermore, the discrete element contact parameter combination corresponding to the minimum error value among the relative error values ​​can be taken as the optimal discrete element contact parameter combination.

[0080] Alternatively, to reduce the number of tests, the simulated angle of repose and relative error value can be obtained through the steepest slope test.

[0081] It should be understood that in the steepest climbing test, the initial value and step size of each discrete element contact parameter are determined within the selected range of discrete element contact parameters. Starting from the initial value, the step size is increased at equal intervals to obtain different combinations of discrete element contact parameters. Then, the generation process of the angle of repose is simulated by the straw sample model, the pith sample model, and the angle of repose test device model to obtain the simulated angle of repose and relative error.

[0082] Furthermore, to improve the accuracy of the optimal discrete element contact parameter combination, a Box-Behnken test can be conducted after the steepest climb test to determine the optimal discrete element contact parameter combination.

[0083] Specifically, the value of the discrete element contact parameter combination corresponding to the minimum relative error value in the steepest climbing test results is taken as the zero level value, the value of the discrete element contact parameter combination corresponding to the first relative error value is taken as the high level value, and the value of the discrete element contact parameter combination corresponding to the second relative error value is taken as the low level value. A Box-Behnken test is then conducted to establish a second-order angle of repose regression model. The average value of the physical angle of repose is taken as the objective function, and the intervals of the high and low level values ​​of the discrete element contact parameter combination are taken as constraints. The discrete element contact parameters in the second-order angle of repose regression model are solved to obtain the optimal discrete element contact parameter combination.

[0084] The first relative error is the larger of the two relative errors adjacent to the minimum relative error, and the second error is the smaller of the two relative errors adjacent to the minimum relative error.

[0085] For example, the Box-Behnken test results include different combinations of discrete element parameters and corresponding simulated packing angles. Based on the Box-Behnken test results, a quadratic response surface regression analysis is performed to establish a quadratic response surface regression model of the packing angle and each discrete element contact parameter. Then, the average value of the physical packing angle is used as the objective function, and the intervals of high and low levels of the discrete element parameter combination are used as constraints. The optimization module of the Design-Expert software is used to optimize the parameters in the second-order packing angle regression model to obtain the optimal combination of discrete element contact parameters.

[0086] Optionally, to further reduce the number of tests and improve test efficiency, a Plackett-Burman test can be conducted before the steepest climb test to select discrete element contact parameters that have a significant impact on the angle of repose for the steepest climb test.

[0087] Specifically, first, based on the selection range of discrete element contact parameters, different combinations of discrete element contact parameters are determined to conduct simulated pile angle tests to obtain the pile angles corresponding to different combinations of discrete element contact parameters. Then, the significance of the pile angles is analyzed using Design Expert software to obtain the contribution rate of each discrete element contact parameter. Discrete element contact parameters with a contribution rate greater than a preset threshold (e.g., 18%) are selected as target discrete element contact parameters.

[0088] The contribution rates can also be sorted, and the top three discrete element contact parameters with the highest contribution rates can be selected as the target discrete element contact parameters.

[0089] Furthermore, steepest ramp tests and Box-Behnken tests were conducted on the target discrete element contact parameters to determine the optimal parameter combination. It should be understood that in the steepest ramp test of the target discrete element contact parameters, the level values ​​of the target discrete element contact parameters were increased in equal steps according to the standard effect values ​​of the Plackett-Burman test, while the zero-level values ​​of other discrete element contact parameters were used.

[0090] Optionally, to verify the rationality and effectiveness of the optimal combination of parameters, after determining the optimal discrete element contact parameter combination, an angle of repose verification test and a compression verification test can be conducted.

[0091] For example, under the experimental conditions of the optimal discrete element contact parameter combination, three simulation tests of the straw-pith repacking angle were conducted to obtain the average value of the simulated repacking angle. The average value of the simulated repacking angle was compared with the average value of the physical repacking angle, and the relative error was 9.41%, indicating that the optimal discrete element contact parameter combination is well matched with the actual contact parameters.

[0092] Figure 7 This is a schematic diagram comparing the stacking states of materials provided in an embodiment of the present invention. Figure 7 Figure (a) shows the stacking state of the straw pith and core mixture in the physical test. Figure 7 Figure (b) shows the stacking state of the straw pith core mixture in the simulation test.

[0093] Depend on Figure 7 It can be seen that the simulated angle of accumulation test and the physical angle of accumulation test have a high degree of matching.

[0094] Then, a radial physical compression test of sunflower stalks was conducted.

[0095] For example, a sunflower stalk sample with a length of 50 mm was selected and placed horizontally in the center of the lower chassis of the SDJF-30KN universal testing machine. The starting preload force was less than 5 N, and the test loading speed was 10 mm / min. The test was stopped when the sunflower stalk sample was obviously crushed. The test data was recorded, and each test was repeated 3 times.

[0096] Then, a simulated compression test was conducted.

[0097] For example, a radial compression simulation test of sunflower stalks was conducted using EDEM software. Based on previous repeated simulation tests, the radii of the single-sphere particles in the straw and pith were set to 0.5 mm and 1 mm, respectively. The three-dimensional models of the straw and pith were quickly filled, and the coordinate values ​​(X, Y, and Z) of each single-sphere particle in the straw and pith were obtained through particle replacement. A "Meta-Particle" meta-model of sunflower stalks was added to establish a simulation model of the sunflower stalk compression sample. This simulation model contained 8892 single-sphere straw particles and 2139 single-sphere pith particles. Three-dimensional models of the upper pressure head (moving component) and lower chassis (fixed component) in IGS format were inserted into the geometry component interface. A sunflower stalk sample particle template was inserted, and its particle factory was established, setting the generation rate of straw particles to 1 particle / second, with a total of 1 particle. To be consistent with the physical compression test, the descent speed of the upper pressure head was set to 0.17 mm / s, and the start time of movement was set to 1 second. The HM-JKR contact model was used in the experiment. The significant contact parameters of each component of sunflower straw are the optimal combination of contact parameters obtained from the Box-Behnken test, and the other parameters are the average values ​​obtained from the above physical measurement tests.

[0098] Figure 8 This is a comparison chart of compression tests provided in an embodiment of the present invention. Figure 8 Figure (a) in the diagram is a schematic diagram of a physical compression test. Figure 8 Figure (b) in the figure is a schematic diagram of the simulated compression test.

[0099] Data from physical compression tests and simulated compression tests of sunflower stalks were processed, and Origin software was used to generate curves showing the change in radial pressure of sunflower stalks over time. Figure 9 As shown.

[0100] Depend on Figure 9 It can be seen that in the physical compression test of sunflower stalks, the maximum pressure value when the internal structure of the sunflower stalks is damaged is 164.01 N, while in the simulated compression test of sunflower stalks, the maximum pressure value when the sunflower stalks are damaged is 151.47 N, and the relative error of the maximum pressure is 7.65%. The compression verification test shows that the discrete element HM-JKR contact model of sunflower stalks is appropriate and effective.

[0101] In summary, the results of the angle of repose test and compression test show that the discrete element contact parameters are in good agreement with the actual contact parameters.

[0102] S3: Establish a discrete element model of sunflower straw based on the optimal discrete element contact parameter combination.

[0103] Specifically, complete 3D models of straw and pith are established based on the actual dimensions of the straw and pith. Multi-spherical straw particles and pith particles are designed based on the complete 3D models of straw and pith and the optimal discrete element contact parameters. The straw and pith particles are combined into complete straw particles through meta-model particle modeling to obtain the sunflower straw discrete element model.

[0104] For example, based on the physical bundling test bench and the structural dimensions of actual sunflower stalks, three-dimensional models of complete stalk husks and complete pith were created using SolidWorks software, such as... Figure 10 As shown, Figure 10 Figure (a) shows a complete three-dimensional model of the straw. Figure 10 Figure (b) shows the complete 3D model of the pith. In the EDEM software preprocessing module, the 3D models of the complete straw and pith are imported, and the particles are named "jiepi" and "suixin" respectively. Then, the calibrated discrete element contact parameters (i.e., optimal discrete element parameters) are set for the system properties of each particle, and the number of mesh elements and smoothing values ​​for the straw and pith particles are set. Multi-spherical straw and pith particles are generated through particle filling. Figure 11 As shown, Figure 11 Figure (a) shows a simulation model of straw. Figure 11 Figure (b) shows the core simulation model; then, using the "meta-model" particle modeling method, the straw and core particles are combined into a complete straw particle. A "Meta-Particle" element, named "jie gan," is added to the particle material interface. In the "jie gan" meta-model interface, "jie pi" and "sui xin" particles are added sequentially, and their relative coordinates are adjusted so that the straw particles and core particles are coaxial and have coplanar end faces on the same side, forming a complete sunflower straw simulation model. Figure 12 As shown, Figure 12 Figure (a) in the figure is a front view of the complete straw simulation model. Figure 12 Figure (b) is an isometric view of the complete straw simulation model.

[0105] In the above embodiments, since the straw and pith are the main components of straw and have different physical and mechanical properties, this invention separates the straw and pith of the actual sunflower straw and conducts a physical angle of packing test on the mixture of straw and pith. Simulated angle of packing tests are then conducted based on straw sample models and pith sample models to determine the optimal discrete element contact parameters. This allows for a more realistic reflection of the behavior of the straw and pith under stress in both physical and simulated angle of packing tests, reflecting the differences in their characteristics and obtaining more accurate discrete element contact parameters. Consequently, the constructed sunflower straw discrete element model can more accurately simulate the actual behavior of sunflower straw in actual agricultural machinery operations, such as cutting, compressing, and baling.

[0106] Example 2

[0107] The process of establishing a discrete element model of sunflower straw can be roughly divided into sunflower straw discrete element parameter calibration and sunflower straw discrete element modeling.

[0108] The parameter calibration process is also the process of finding the optimal values ​​of the contact parameters in the model. The contact parameters in this invention refer to the parameters required for contact and mechanical behavior to occur between straw particles and between straw particles and geometric components, including the static friction coefficient, rolling friction coefficient, recovery coefficient, and parameters of the contact model.

[0109] The following section describes the process of calibrating the discrete element parameters of sunflower straw.

[0110] First, a physical angle of repose test is conducted, as follows:

[0111] Sample selection: Sunflower stalks with a moisture content of about 20% were selected as the test material. The stalk peel and pith were separated to obtain stalk peel samples and pith samples.

[0112] Physical angle of repose test: The physical angle of repose test was conducted on the straw husk sample and the pith sample. It should be understood that the angle of repose test can directly reflect the friction and flow characteristics between the particles of the bulk material. In this embodiment of the invention, based on the structural characteristics and flow characteristics of sunflower straw, the cylindrical lifting method was selected to conduct the angle of repose test of the straw husk-pith mixture. The angle of repose test was conducted on an electronic universal testing machine. The collected angle of repose images were processed using Origin software to obtain the actual experimental straw husk-pith repose angle value.

[0113] Before the test, the lower edge of the lifting cylinder is basically attached to the tray using the control system of the universal testing machine, and the lifting cylinder and the tray are concentric. After the straw and pith samples are mixed evenly, the straw and pith mixture is added into the lifting cylinder through the opening on the top of the lifting cylinder.

[0114] Based on previous experiments and considering the composition ratio of husk and pith in sunflower stalks, the ratio of husk samples to pith samples was approximately 7:1. Therefore, the design of the angle of repose test included 560 husk samples and 80 pith samples. During the experiment, the lifting cylinder was loaded at a uniform speed of 10 mm / min. After the husk-pith mixture samples were completely detached from the lifting cylinder and came to rest, a frontal view image of the husk-pith mixture was taken along the plane of the tray. Then, the image digitization tool in Origin software was used to fit the coordinate data of the outline of the piled material.

[0115] The experiment was repeated 10 times, and the average angle of accumulation was 28.15°.

[0116] Then, a simulated angle of repose test was conducted, as follows:

[0117] The first step is to select a contact model: Since the HM-JKR contact model is a cohesive particle contact model based on JKR theory, it is suitable for simulating materials where particles become sticky and agglomerated, such as moist soil and crops. Therefore, the HM-JKR contact model can be selected as the contact model for calibrating the sunflower straw discrete element model.

[0118] Based on the literature's method for calculating the normal elastic force of the JKR contact model, let the adhesion force between the straw and the pith be W. s The calculation formula is as follows:

[0119] W s =γ1+γ2-γ3.

[0120] Among them, W s γ1 represents the bonding force between the straw husk and the pith, expressed in J / m²; γ2 represents the surface energy of the straw husk; γ3 represents the surface energy of the straw-pith combination.

[0121] The formula for calculating the normal elastic force between the straw bark and the pith is:

[0122]

[0123] In the formula, F JKR E represents the normal elastic force, measured in N (Newtons). * The equivalent elastic modulus is expressed in Pa; α is the radius of the contact surface, expressed in meters; R * This is the equivalent contact radius, in meters (m).

[0124] The formula for calculating the normal overlap is:

[0125]

[0126] δ is the normal overlap, in meters (m).

[0127] in,

[0128]

[0129] v1 and v2 are Poisson's ratios of straw and pith, respectively; R1 and E2 are elastic moduli of straw and pith, respectively; R1 and R2 are particle radii of straw and pith, respectively, in meters.

[0130] When the surface energy γ3 of the straw-pith contact model is 0, the normal elastic force of the JKR contact model is equivalent to the normal elastic force of the Hertz-Mindlin contact model, that is:

[0131]

[0132] Even if the particles are not in diametrical contact, the JKR contact model still provides cohesion. The expression for the maximum gap with non-zero cohesion between particles is as follows:

[0133]

[0134] Where, δ c α represents the maximum normal interparticle spacing when there is non-zero cohesive force between particles, expressed in meters (m). c The maximum tangential gap is the distance between particles when there is non-zero cohesive force, expressed in meters (m).

[0135] When the particles are not disintegrated and the gaps are smaller than a certain value, the cohesive force is at its maximum.

[0136]

[0137] When using the JKR contact model to simulate particles with high water content, the particle surface energy and contact angle determine the magnitude of the force required for particle separation.

[0138]

[0139] Where is the contact angle between particles.

[0140] The second step is to establish a calibration model:

[0141] Optionally, geometric models of the straw and pith are created separately using the 3D modeling software SolidWorks. To make the straw and pith models more realistic and facilitate rapid particle filling, while also considering the computational time for computer simulation, particle filling is performed based on the outer contours of the 3D models of the straw and pith. Single-sphere particles are selected in the particle material panel for both the straw and pith, and the 3D models of the straw and pith are imported into EDEM software sequentially. The number of mesh elements is set to the default value of 30, and the smoothing value is set to 4. Multi-spherical straw and pith particles are generated, with 250 and 151 particles respectively.

[0142] The carrying tube and pallet are generated by EDEM software. The carrying tube type is set to solid, the material is steel, the diameter and height of the carrying tube are the same as the actual size, and the top and bottom of the carrying tube are set to open. The pallet type is selected as solid, the material is steel, the diameter of the pallet is equal to the actual diameter, and the bottom end face of the carrying tube coincides with the pallet.

[0143] Step 3: EDEM software calibration and simulation settings, the process is as follows:

[0144] Based on historical measurements of intrinsic parameters of sunflower stalk materials and relevant literature, the intrinsic parameters of sunflower stalk peel, pith, and steel required for the simulation experiment were set. To ensure that the simulated angle of repose test conditions of sunflower stalk-pith are consistent with those of the physical angle of repose test, the upward lifting speed of the lifting cylinder in the EDEM component panel was set to 0.17 mm / s, and the start time of movement was set to 1.2 s. Virtual particle factories for stalk peel and pith were added above the lifting cylinder and named "jie pi factory" and "sui xin factory" respectively. The particle factories were set to generate 560 stalk peel samples and 80 pith samples per second. According to the formula for calculating the free fall velocity of an object, the particle descent velocity along the negative Z-axis was calculated and set to 900 mm / s. The particle generation process of the angle of repose is as follows. Figure 13 As shown.

[0145] Step 4: Conduct the Plackett-Burman test

[0146] The Plackett-Burman experiment identifies variables that significantly affect the target value by examining the relationship between the target response and various experimental variables. In other words, the Plackett-Burman experiment can screen out parameters that have a significant impact on the experimental indicators.

[0147] First, the experimental parameters and parameter levels are determined by combining the simulation parameter selection ranges of some references.

[0148] Table 2 is an example of experimental parameters and parameter levels provided in the embodiments of the present invention.

[0149] Table 2 Plackett-Burman Experimental Parameters and Levels

[0150]

[0151] The Plackett-Burman experimental design method in Design Expert 13 software was used. The angle of packing of sunflower straw-pith mixture was used as the response value. The parameters in Table 2 were used as experimental variables to conduct significance tests. The parameters with the most significant impact on the experimental indicators were screened. Three central groups were set up for the screening test, and a total of 15 groups of experiments were conducted. The experimental scheme and experimental results are shown in Table 3.

[0152] Table 3 Plackett-Burman Experimental Design and Results

[0153] Serial Number <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> <![CDATA[X 11 ]]> Angle of accumulation θ / (°) 1 0.4 0.66 0.43 0.12 0.6 0.42 0.41 0.22 1 0.6 0.4 24.27 2 0.1 0.66 0.43 0.12 0.68 0.48 0.41 0.22 0.6 0.2 0.8 28.55 3 0.1 0.66 0.33 0.21 0.68 0.42 0.41 0.32 1 0.2 0.4 26.9 4 0.4 0.57 0.43 0.21 0.68 0.42 0.33 0.22 1 0.2 0.8 28.15 5 0.1 0.57 0.33 0.21 0.6 0.48 0.41 0.22 1 0.6 0.8 26.48 6 0.4 0.57 0.43 0.21 0.6 0.48 0.41 0.32 0.6 0.2 0.4 23.37 7 0.1 0.57 0.43 0.12 0.68 0.48 0.33 0.32 1 0.6 0.4 27.16 8 0.4 0.57 0.33 0.12 0.68 0.42 0.41 0.32 0.6 0.6 0.8 21.28 9 0.1 0.57 0.33 0.12 0.6 0.42 0.33 0.22 0.6 0.2 0.4 20.05 10 0.25 0.615 0.38 0.165 0.64 0.45 0.37 0.27 0.8 0.4 0.6 24.51 11 0.25 0.615 0.38 0.165 0.64 0.45 0.37 0.27 0.8 0.4 0.6 25.32 12 0.4 0.66 0.33 0.21 0.68 0.48 0.33 0.22 0.6 0.6 0.4 27.5 13 0.4 0.66 0.33 0.12 0.6 0.48 0.33 0.32 1 0.2 0.8 28.76 14 0.1 0.66 0.43 0.21 0.6 0.42 0.33 0.32 0.6 0.6 0.8 24.66 15 0.25 0.615 0.38 0.165 0.64 0.45 0.37 0.27 0.8 0.4 0.6 24.9

[0154] Based on the Plackett-Burman test results in Table 3, the regression model for the angle of repose θ is as follows:

[0155] θ=25.46-0.039X1+1.18X2+0.433X3+0.583X4+0.996X5+

[0156] 1.38X6-0.453X7-0.239X8+1.36X9-0.369X 10 +0.719X 11 .

[0157] The significance of the regression model for the angle of buildup was analyzed using Design Expert software, and the results are shown in Table 4.

[0158] Table 4. Significance Analysis Results of the Plackett-Burman Experiment

[0159] Source of variance Standardization effect sum of squares Contribution rate / % F value p-value <![CDATA[X1]]> -0.078 0.02 0.02 0.04 0.8578 <![CDATA[X2]]> 2.358 16.69 18.09 34.48 0.0099** <![CDATA[X3]]> 0.865 2.24 2.43 4.64 0.1203 <![CDATA[X4]]> 1.165 4.07 4.41 8.41 0.0625 <![CDATA[X5]]> 1.992 11.90 12.9 24.59 0.0157* <![CDATA[X6]]> 2.752 22.72 24.63 46.94 0.0064** <![CDATA[X7]]> -0.905 2.46 2.66 5.08 0.1096 <![CDATA[X8]]> -0.478 0.69 0.74 1.42 0.3193 <![CDATA[X9]]> 2.718 22.17 24.03 45.81 0.0066** <![CDATA[X 10 ]]> -0.738 1.64 1.77 3.38 0.1633 <![CDATA[X 11 ]]> 1.438 6.21 6.73 12.83 0.0372*

[0160] Where ** indicates that the model term is highly significant (P < 0.01), and * indicates that the model term is significant (P < 0.05).

[0161] As shown in Table 4, the repose angle regression model has P < 0.05 and a determination coefficient R0.05. 2 =0.9843, indicating that the main effect model is significant, and that the fitted angle of packing regression model is consistent with the actual situation and can represent X1~X 11 The degree of influence of each variable on the response value. Table 4 shows that the order of contribution rate of each variable to the angle of repose of the straw-pith mixture is: X6 > X9 > X2 > X5 > X 11 >X4>X7>X3>X 10 >X8>X1, among which the variables that are most significant to the angle of repose are the straw-straw static friction coefficient X2, the straw-pith rolling friction coefficient X6, and the straw JKR surface energy X9.

[0162] The fifth step is to conduct the steepest climb test, as follows:

[0163] The Steepest Ascent Method is an experimental design method for response surface optimization, used to quickly find regions that are close to optimal conditions.

[0164] The steepest climbing test was conducted on variables X2, X6, and X9, which have a significant impact on the angle of accumulation, to determine the optimal value of the significance level of these variables.

[0165] Specifically, based on the standard effect value of the Plackett-Burman experiment, the levels of variables X2, X6, and X9 were increased in equal increments, with the average value of the angle of repose from the physical experiment (28.15°) as the target value. All other variables were taken from the zero level value of the Plackett-Burman experiment. Based on the relative error between the angle of repose from the steepest climb experiment and the angle of repose from the physical experiment, the level value of the variable corresponding to the group with the smallest relative error in the angle of repose was used as the level value of the optimal solution for the Box-Behnken experiment.

[0166] The formula for calculating the relative error of the angle of accumulation is:

[0167]

[0168] Where η is the relative error of the angle of repose; θ1 is the angle of repose in the physical test; and θ2 is the angle of repose in the steepest climbing test.

[0169] Table 5 shows an example of a steepest climbing test scheme and results provided by an embodiment of the present invention.

[0170] Table 5. Test Scheme and Results for the Steepest Climb

[0171]

[0172] Step 6: Conduct Box-Behnken experiments and analyses, as follows:

[0173] Box-Behnken experiments are a commonly used experimental design method in Response Surface Methodology (RSM) to fit a quadratic response surface model and optimize process parameters in the later stages of the experiment to obtain the best experimental results.

[0174] Specifically, as shown in Table 5, when X2, X6, and X9 gradually increase, the angle of repose of the sunflower stalk-pith mixture gradually increases, and its relative error first decreases and then increases. Since the relative error of the angle of repose in the fifth group of experiments in Table 5 is the smallest, the value of the variable of the steepest climb test in the fifth group is selected as the zero level value. The experimental data of the sixth and fourth groups are used as the high (1) and low (-1) level values, respectively. The static friction coefficient of straw-straw X2, the rolling friction coefficient of straw-pith X6, and the JKR surface energy of straw X9 are used as experimental variables, and the angle of repose of the sunflower stalk-pith mixture is used as the response value. The Box-Behnken test is conducted using Design Expert software to seek the optimal parameter combination. The experimental variables and levels are shown in Table 6, and the experimental design and experimental results are shown in Table 7.

[0175] Table 6. Box-Behnken Experiment Variables and Levels

[0176]

[0177] Table 7 Box-Behnken Experimental Design and Results

[0178]

[0179] Furthermore, a quadratic response surface regression analysis was performed on the experimental results in Table 7 to establish a quadratic response surface regression model for the angle of repose θ and the static friction coefficient X2 between straw and straw, the rolling friction coefficient X6 between straw and pith, and the JKR surface energy X9 of straw:

[0180]

[0181] Then, an analysis of variance was performed on the packing angle η of the Box-Behnken test results, and the results are shown in Table 8.

[0182] Table 8. Analysis of Variance for the Box-Behnken Experiment Regression Model

[0183] Source of variance sum of squares Degrees of freedom Mean Square F P Model 68.38 9 7.60 19.34 0.0004** <![CDATA[X2]]> 7.16 1 7.16 18.23 0.0037** <![CDATA[X6]]> 56.39 1 56.39 143.51 ＜0.0001** <![CDATA[X9]]> 1.44 1 1.44 3.66 0.0475* <![CDATA[X2X6]]> 0.0036 1 0.0036 0.0092 0.9264 <![CDATA[X2X9]]> 0.0812 1 0.0812 0.2067 0.6631 <![CDATA[X6X9]]> 0.3600 1 0.3600 0.9161 0.3704 <![CDATA[X2 2 ]]> 0.4345 1 0.4345 1.11 0.3279 <![CDATA[X6 2 ]]> 1.64 1 1.64 4.17 0.0805 <![CDATA[X9 2 ]]> 0.5961 1 0.5961 1.52 0.2579 residual 2.75 7 0.39 Mismatch 2.00 3 0.67 3.58 0.1248 error 0.7462 4 0.1866 sum 71.13 16

[0184] As shown in the table, the quadratic regression model of the angle of accumulation is highly significant (P < 0.01), and the coefficient of determination R0.01 is [missing value]. 2 =0.9613, the lack-of-fit term was not significant (P>0.05), and the coefficient of variation was 2.28%, indicating that the angle-of-pack regression model fits well and can correctly express the relationship between η and X2, X6, and X9. Among them, the p-values ​​of the linear terms X2 <0.01, X6 <0.0001, and X9 <0.05, indicating that X2 and X6 are extremely significant, X9 is significant, and the order of significance is: X6>X2>X2. The other terms are not significant.

[0185] Furthermore, response surface optimization experiments were conducted:

[0186] Specifically, based on the Box-Behnken experiment results and the quadratic regression model, using the angle of repose obtained from the physical angle of repose experiment (28.15°) as the target value, the optimization module of the Design-Expert software was used to solve for the optimal values ​​of the experimental variables X2, X6, and X9. The objective function and constraints were set as follows:

[0187] targetθ = 28.15°

[0188]

[0189] The optimal parameter combination was finally obtained: X2 = 0.643, X6 = 0.461 and X9 = 0.607. This optimal parameter combination was used to conduct a verification test on the sunflower straw contact parameters.

[0190] The seventh step is to conduct experimental verification.

[0191] The experimental verification is divided into the angle of repose test and the compression test:

[0192] (1) Angle of accumulation test

[0193] Specifically, under the experimental conditions of optimal parameter combination, three simulation experiments of the angle of repose of sunflower stalks and pith were conducted. The average simulated angle of repose was 30.8°, and the relative error between the simulated experimental angle of repose and the physical experimental angle of repose was 9.41%. For example, the experimental comparison... Figure 7 As shown, Figure 7 Figure (a) shows the packing state of the mixture in the physical test. Figure 7 Figure (b) shows the simulated mixing and stacking state. The results of the comparative test show that the physical test and the simulation test have a good match in terms of the angle of repose.

[0194] (2) Compression test

[0195] To further verify the rationality and effectiveness of the selected contact model and the measured contact parameters of each component of sunflower stalks, a radial compression physical test of sunflower stalks was conducted using an SDJF-30KN universal testing machine. A 50mm long sunflower stalk sample was selected and placed horizontally in the center of the lower base. The initial preload was less than 5N, and the loading speed was 10mm / min. The test was stopped when the sunflower stalk sample was clearly crushed. The test data were recorded, and each test was repeated three times.

[0196] Then, a radial compression simulation test of sunflower stalks was conducted using EDEM software. Specifically, the radii of the single-sphere particles in the stalk and pith were set to 0.5 mm and 1 mm, respectively. The 3D models of the stalk and pith were quickly filled, and the coordinate values ​​(X, Y, and Z) of each single-sphere particle in the stalk and pith were obtained through particle replacement. A "Meta-Particle" meta-model of sunflower stalks was added to establish a simulation model of the sunflower stalk compression sample. This simulation model contained 8892 single-sphere particles in the stalk and 2139 single-sphere particles in the pith. IGS format 3D models of the upper pressure head (moving component) and lower chassis (fixed component) were inserted into the geometry component interface. A sunflower stalk sample particle template was inserted, and its particle factory was established, with the generation rate of straw particles set to 1 particle / second. To ensure consistency between the simulated compression test and the physical compression test, the descent speed of the upper pressure head was set to 0.17 mm / s, and the start time of movement was set to 1 second. The HM-JKR contact model was used in the experiment. The significant contact parameters of each component of sunflower straw were the optimal parameter combination obtained from the Box-Behnken experiment, and the other contact parameters were the average values ​​obtained from the above physical measurement experiments.

[0197] In the compression test, the relative error between the maximum pressure value of sunflower stalk destruction in the simulated compression test and the maximum pressure value of sunflower stalk destruction in the physical compression test was 7.65%, indicating that the discrete element JKR contact model of sunflower stalk was appropriate and effective.

[0198] In summary, the results of the sunflower stalk stacking angle and compression verification tests show that the contact model and contact parameters are in good agreement with the actual situation.

[0199] The above embodiment describes the process of discrete element parameter calibration for sunflower stalks. The following section introduces the process of discrete element modeling for sunflower stalks.

[0200] To simulate the actual structural composition of sunflower stalks, a complete discrete element model of sunflower stalks consisting of sunflower stalk peel and pith was established.

[0201] Specifically, based on the physical bundling test bench and the structural dimensions of the actual sunflower stalks, a complete 3D model of the straw and pith was established using SolidWorks software. Then, the 3D models of the straw and pith were imported into the particle material interface, and the particles were named "jie pi" and "sui xin" respectively. The calibrated discrete element contact parameters were then set for each particle system attribute, along with the number of mesh elements and smoothing values ​​for the straw and pith particles. Multi-spherical straw and pith particles were generated through particle filling. Considering the large amount of complete straw used in the sunflower stalk bundling simulation experiment, and taking into account the computer's computing power, the number of mesh elements and smoothing values ​​for the straw and pith particles were set to default values ​​of 30 and 5, respectively, generating multi-spherical straw and pith particles containing 620 and 46 single-sphere particles, respectively.

[0202] To combine the husk and pith particles into a complete stalk particle, considering the differences in their material properties, a discrete element model of sunflower stalks was established using the "meta-model" particle modeling method. A "Meta-Particle" element, named "jie gan," was added to the particle material interface. Then, "jie pi" and "sui xin" particles were added sequentially to the "jie gan" meta-model interface. Their relative coordinates were adjusted so that the husk particles and pith particles were coaxial and had their end faces on the same side coplanar, thus forming a complete sunflower stalk simulation model.

[0203] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for constructing a discrete element model of sunflower stalks, characterized in that, include: The stalks and pith of the sunflower were separated and cut evenly to obtain stalk samples and pith samples. The stalk samples and pith samples were mixed in a preset ratio to obtain a stalk-pith mixture. The physical angle of repose of the stalk-pith mixture was tested to obtain the average value of the physical angle of repose. Based on a pre-designed straw sample model, pith sample model, and angle of repose test device model, a simulated angle of repose test was conducted to obtain the simulated angle of repose and the relative error between the simulated angle of repose and the average value of the physical angle of repose. The optimal discrete element contact parameter combination was determined based on the simulated angle of repose and the relative error value. A discrete element model of sunflower straw is established based on the optimal discrete element contact parameter combination. Using the average physical angle of repose as the target value, and each discrete element contact parameter as a variable, a Plackett-Burman experiment is conducted to determine the target discrete element contact parameter. The target discrete element contact parameter is the parameter among the discrete element contact parameters whose contribution to the angle of repose is greater than a preset threshold. The process of using the average physical angle of repose as the target value and conducting Plackett-Burman experiments to determine the target discrete element contact parameter includes: generating different combinations of discrete element contact parameters based on the selection range of each discrete element contact parameter; conducting simulated angle of repose experiments under different combinations of discrete element contact parameters to obtain different combinations of discrete element contact parameters and corresponding simulated angles of repose; establishing an angle of repose regression model based on the simulated angle of repose experiment results; performing significance analysis on the angle of repose regression model to determine the contribution rate of each discrete element contact parameter to the angle of repose; and selecting discrete element contact parameters with contribution rates greater than a preset threshold as target discrete element contact parameters. Within the range of discrete element contact parameters, determine the initial value and step size of each discrete element contact parameter and conduct the steepest climbing test. The discrete element contact parameters include: straw-straw recovery coefficient, straw-straw static friction coefficient, straw-straw rolling friction coefficient, straw-core recovery coefficient, straw-core static friction coefficient, straw-core rolling friction coefficient, straw-Q235 steel static friction coefficient, straw-Q235 steel rolling friction coefficient, straw JKR surface energy, core JKR surface energy, and straw-core JKR surface energy. The target discrete element contact parameters include: straw-straw static friction coefficient, straw-pith rolling friction coefficient, and straw JKR surface energy.

2. The method for constructing a discrete element model of sunflower stalks according to claim 1, characterized in that, Simulated angle of repose tests were conducted based on pre-designed straw sample models, pith sample models, and angle of repose testing device models. The simulated angle of repose and the relative error between the simulated angle of repose and the average value of the physical angle of repose were obtained, including: In the steepest climbing test, the generation process of the angle of repose was simulated by using straw sample model, pith sample model and angle of repose test device model to obtain the simulated angle of repose and relative error value corresponding to different discrete element parameter combinations.

3. The method for constructing a discrete element model of sunflower stalks according to claim 2, characterized in that, The optimal combination of discrete element contact parameters is determined based on the simulated repose angle and relative error value, including: The optimal discrete element contact parameter combination is the combination of discrete element contact parameters corresponding to the minimum error value among the relative error values; or... The value of the discrete element contact parameter combination corresponding to the minimum relative error value in the steepest climb test results is taken as the zero level value, the value of the discrete element contact parameter combination corresponding to the first relative error value is taken as the high level value, and the value of the discrete element contact parameter combination corresponding to the second relative error value is taken as the low level value for Box-Behnken tests to establish a second-order angle of packing regression model; wherein, the first relative error is the larger of the two relative errors adjacent to the minimum relative error value in the Box-Behnken test results, and the second error is the smaller of the two relative errors adjacent to the minimum relative error value in the Box-Behnken test results; Using the average physical packing angle as the objective function and the intervals between high and low levels of the discrete element contact parameter combination as constraints, the discrete element contact parameters in the second-order packing angle regression model are solved to obtain the optimal discrete element contact parameter combination.

4. The method for constructing a discrete element model of sunflower stalks according to claim 3, characterized in that, Within the selected range of discrete element contact parameters, determine the initial values ​​and step sizes of each discrete element contact parameter to conduct the steepest climbing test, including: Within the selected range of target discrete element contact parameters, the initial values ​​and step sizes of each target discrete element contact parameter are determined to conduct the steepest climbing test.

5. The method for constructing a discrete element model of sunflower stalks according to any one of claims 1 to 3, characterized in that, A discrete element model of sunflower straw is established based on the optimal discrete element contact parameter combination, including: Establish complete three-dimensional models of straw bark and pith core based on the actual straw bark size and pith core size, respectively. Multi-spherical straw granules and core granules were designed based on complete three-dimensional models of straw bark, complete three-dimensional models of pith, and optimal discrete element contact parameter combinations. By combining straw husk particles and pith particles into complete straw particles using a meta-model particle modeling approach, a discrete element model of sunflower straw is obtained.

6. The method for constructing a discrete element model of sunflower stalks according to any one of claims 1 to 3, characterized in that, Also includes: Based on the dimensions of the actual straw husk, the actual pith, and the actual angle of packing test apparatus in the physical angle of packing test, a three-dimensional model of the straw husk, a three-dimensional model of the pith, and a model of the angle of packing test apparatus were designed respectively. The straw husk and pith three-dimensional models were then filled with particles to obtain straw husk sample models and pith sample models.

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