CN113029873A - Non-circular seed particle model parameter calibration method and device and storage medium - Google Patents
Non-circular seed particle model parameter calibration method and device and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device and a storage medium for calibrating parameters of a non-circular seed particle model, wherein the method comprises the steps of introducing basic parameters of seeds and contact measurement parameters of the seeds; constructing a non-circular seed particle model according to the basic parameters of the seeds; carrying out orthogonal experimental treatment on the non-circular seed particle model according to the seed contact measurement parameters to obtain influence parameter sequencing information, and carrying out optimization treatment on the non-circular seed particle model; and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model. According to the method, the influence parameter sequencing information is determined through the non-circular seed particle model, and then the non-circular seed particle model is optimized through the influence parameter sequencing information, so that non-circular seed particle model parameters for calibrating non-circular seeds are obtained, and the accuracy of the optimized non-circular seed particle model is improved.
Description
Technical Field
The invention mainly relates to the technical field of seed data processing, in particular to a method and a device for calibrating parameters of a non-circular seed particle model and a storage medium.
Background
The application of Discrete Element Method (DEM) in agricultural equipment is becoming more and more widespread, and the research of the dynamic problem of the mitochondria by applying the discrete element method has become a development trend. In the working process of the seed metering device, the acting force between seeds and between the seeds and the seed metering device is very complex, and the seed metering device can be researched through discrete element software based on a discrete element method. In order to improve the precision of the discrete element simulation test, a discrete element model of a material needs to be accurately established and physical property parameters of a simulation model need to be accurately defined in discrete element software; the physical parameters mainly comprise intrinsic parameters and contact parameters, the intrinsic parameters are characteristic parameters of the object, are usually fixed values and are irrelevant to external factors such as Poisson's ratio, shear modulus, density, friction coefficient and the like, and can be directly measured through a bench test; the contact parameters comprise an repose angle and a friction angle, are physical parameters when two objects are in contact, have a relation with the two contacted objects, and can be obtained through virtual simulation test calibration.
At present, in the numerical simulation research of movement behaviors of non-round seed particles at the present stage, the particle parameters which are easy to measure are generally obtained by adopting the conventional measuring means, for example, the particle density, the hardness and the particle size can be respectively measured by a pycnometer, a pressing-in method and a laser particle size analyzer, but the contact parameters of the seed particles are difficult to obtain by the conventional measuring means through the conventional physical experiment, and even if the contact parameters are measured by a complicated experiment, the measurement precision has great deviation, thereby influencing the accuracy of the numerical simulation result.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a method and a device for calibrating parameters of a non-circular seed particle model and a storage medium.
The technical scheme for solving the technical problems is as follows: a non-circular seed particle model parameter calibration method comprises the following steps:
introducing basic seed parameters and contact measurement parameters, wherein the basic seed parameters and the contact measurement parameters are obtained by physical experiment measurement in advance;
constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
carrying out regression analysis on the non-circular seed particle model through the seed contact measurement parameters to obtain influence parameter sequencing information of the non-circular seed particle model;
optimizing the non-circular seed particle model according to the influence parameter sequencing information to obtain optimized model parameters;
and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
Another technical solution of the present invention for solving the above technical problems is as follows: a non-circular seed particle model parameter calibration device comprises:
the device comprises an importing module, a data processing module and a data processing module, wherein the importing module is used for importing basic seed parameters and seed contact measurement parameters, and the basic seed parameters and the seed contact measurement parameters are obtained by measuring through a physical experiment in advance;
the construction module is used for constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
the processing module is used for carrying out regression analysis on the non-circular seed particle model through the seed contact measurement parameters to obtain the influence parameter sequencing information of the non-circular seed particle model;
optimizing the non-circular seed particle model according to the influence parameter sequencing information to obtain optimized model parameters;
and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
Another technical solution of the present invention for solving the above technical problems is as follows: a non-round seed particle model parameter calibration device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and when the computer program is executed by the processor, the non-round seed particle model parameter calibration method is realized.
Another technical solution of the present invention for solving the above technical problems is as follows: a computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the non-round seed particle model parameter calibration method as described above.
The invention has the beneficial effects that: the method comprises the steps of establishing a non-circular seed particle model based on a discrete element method, determining influence parameter sequencing information through the non-circular seed particle model, and further optimizing the non-circular seed particle model through seed contact measurement parameters and the influence parameter sequencing information, so that non-circular seed particle model parameters for calibrating non-circular seeds are obtained, and the optimized non-circular seed particle model improves calibration accuracy.
Drawings
FIG. 1 is a flow chart of a method for calibrating parameters of a non-circular seed particle model according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a non-circular seed particle model parameter calibration apparatus according to an embodiment of the present invention;
FIG. 3 is a flowchart of the overall method for calibrating parameters of a non-circular seed particle model according to an embodiment of the present invention;
FIG. 4 is a diagram of a homemade experimental facility for measuring the angle of repose α according to an embodiment of the present invention;
FIG. 5 is a self-made experimental apparatus for measuring a friction angle γ according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a non-circular seed discrete meta-particle model provided in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a numerical simulation experiment for determining an angle of repose α according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a numerical simulation experiment for determining the friction angle γ according to an embodiment of the present invention;
FIG. 9 is a flow chart of EDEM discrete element software provided by an embodiment of the present invention;
fig. 10 is a schematic diagram of a Hertz-minilin (no slip) contact model according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a method for calibrating parameters of a non-circular seed particle model according to an embodiment of the present invention.
Example 1: as shown in fig. 1, a method for calibrating parameters of a non-circular seed particle model includes the following steps:
s1, introducing basic parameters of seeds and contact measurement parameters of the seeds, wherein the basic parameters of the seeds and the contact measurement parameters of the seeds are obtained by measurement in advance through a physical experiment;
s2, constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
s3, carrying out regression analysis on the non-circular seed particle model through the seed contact measurement parameters to obtain the influence parameter sequencing information of the non-circular seed particle model;
s4, optimizing the non-circular seed particle model according to the influence parameter sorting information to obtain optimized model parameters;
and S5, obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
In the above embodiment, the non-circular seed particle model is established based on the discrete element method, the influence parameter ordering information is determined by the non-circular seed particle model, and then the non-circular seed particle model is optimized by the influence parameter ordering information, so that non-circular seed particle model parameters for calibrating the non-circular seeds are obtained, and the optimized non-circular seed particle model improves the calibration precision.
On the basis of example 1, example 2: the basic parameters of the seeds comprise density, particle Poisson ratio, granularity, shear modulus, inter-particle elastic recovery coefficient, inter-particle static friction coefficient and inter-particle dynamic friction coefficient, and the parameters for measuring the seed contact comprise an angle of repose alpha and an angle of friction gamma.
It should be understood that the basic parameters of the seeds comprise physical characteristic parameters and formulated parameters, the physical characteristic parameters comprise the water content, the thousand seed mass, the bud length, the density, the granularity, the triaxial arithmetic mean grain diameter and the hardness of the non-round seeds, and the physical characteristic parameters of the non-round seeds are obtained by measuring through physical experiments; the formulated parameters comprise the particle Poisson ratio, the shear modulus, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient of the non-circular seeds; and constructing the physical characteristic parameters and the formulated parameters based on a discrete element method to obtain a non-circular seed particle model.
As shown in fig. 4-5, the physical experiment measurement mentioned in S1, specifically, the measurement experiment of the angle of repose α and the angle of friction γ of the seed contact measurement parameters was performed by using a self-made experimental apparatus made of a known calibration material. Determining the angle of repose alpha by using a side wall collapse method, namely filling a certain volume of seeds in a geometric container, such as a square container, and suddenly removing a side baffle after standing, so that the material in the container collapses from the side wall surface to form the angle of repose alpha; as shown in fig. 7-8, the self-made sliding friction angle measuring device places the seeds on the experiment flat plate, lifts one end of the experiment flat plate at a uniform speed through the lifting mechanism, slowly tilts the experiment flat plate, and when the seeds start to slide, the tilt angle of the flat plate is the sliding friction angle of the seeds on the experiment flat plate, and the digital display inclinometer is used for measuring the angle γ. And (3) measuring the repose angle alpha and the friction angle gamma, comparing the coincidence condition of the results of the physical experiment and the numerical simulation experiment, and preliminarily judging whether the established model parameter constitutive model is reliable or not.
The non-round seeds to be measured in the experiment are selected and sampled, and then are measured, preferably, a halogen moisture meter is used for measuring the moisture content of the seeds, so that the seeds are ensured to be under the condition of the moisture content within a certain range. Wherein, the density of the seeds can be preferably measured by a pycnometer, the granularity distribution is preferably measured by a laser granularity analyzer, and the hardness is tested based on an indentation method; in the determination experiments of the angle of repose alpha and the angle of friction gamma, the contact between particles and the contact model of the particles and the experimental device are Hertz-Mindlin (no slip) models, wherein the angle of repose alpha and the angle of friction gamma determined by physical experiments or numerical simulation experiments are average values.
It should be understood that in the determination experiment of the angle of repose α and the angle of friction γ, the model of the contact between the particles and the experimental apparatus is the Hertz-minidin (no slip) model, wherein the angle of repose α and the angle of friction γ determined by the physical experiment or the numerical simulation experiment are the average values of the measurement. As shown in fig. 10, in the Hertz-minidlin (no slip) model, the particle stress calculation formula includes: normal force F in contact modeln(N) is the normal overlap amount δnThe function of (a), whose expression is,
in the formula E*-equivalent young's modulus, Pa;
R*-equivalent radius, mm;
δnnormal overlap, mm.
Equivalent Young's modulus E*With equivalent radius R*Are respectively defined as the following components in percentage by weight,
in the formula Ei、Ej-young's modulus, Pa;
vi、vj-a poisson's ratio;
Ri、Rjcontact sphere radius, mm.
In addition, damping force
N, obtained by the following formula,
in the formula
-equivalent mass, kg;
coefficient of restitution related parameter beta, and normal stiffness SnThe expression of (N/m) is as follows,
wherein e-coefficient of restitution.
Tangential force Ft(N) by the tangential overlap amount δt(mm), and tangential stiffness St(N/m), determination
Wherein the content of the first and second substances,
in the formula G*-equivalent shear modulus, Pa.
In addition, the solution formula for tangential damping is,
in the formula
-tangential component of relative velocity, m/s.
Coulomb friction force musFnA tangential force is defined, whereinsIs the coefficient of static friction.
In the EDEM simulation, the effect of rolling friction is very important and must be considered. A torque is typically applied to the contact surface to characterize rolling friction.
In the formula ofr-a rolling friction coefficient;
Ri-distance of centroid of particle i to contact point, mm;
Through the formula analysis, the mechanics solution based on the discrete element method is complex, the related parameters are more, and the influence of the friction coefficient of the particles on the simulation result is larger.
Fig. 6 is a schematic diagram of a non-circular seed discrete meta-particle model according to an embodiment of the present invention.
As shown in FIG. 6, in practical operation, non-round seeds are selected from the variety of the rice commonly used for precision sowing, namely Pebaofeng (super hybrid rice), and the germination is accelerated until more than 90% of the seeds are broken in chest and exposed to white. The length of the seed bud is controlled to be less than 1mm according to the requirement of precision seeding of rice. Selecting 100 rice buds at random, measuring the sizes of the rice buds in the length direction, the width direction and the height direction by using a vernier caliper with the precision of 0.01mm, counting to obtain the three-axis size of the rice buds conforming to normal distribution, and reducing the number of spherical particles as much as possible on the premise of ensuring small model error during modeling. Based on the shape and size of the bud seeds, the model can be simplified to approximate to an oblate ellipsoid, the shape of the bud seeds is formed by stacking spherical particles, the number of the spherical particles is 49, and the triaxial size of the model is 9.00mm multiplied by 2.68mm multiplied by 2.0mm, and rice seed buds are constructed.
The numerical simulation experiment through the non-circular seed particle model mentioned in S3 is specifically to perform a repose angle α and a friction angle γ measurement simulation experiment on a device (experimental device) having the same geometric parameters by using EDEM discrete element software, wherein the computational domain grid size can be set to 2mm in consideration of particle size distribution characteristics and computer simulation computational efficiency, the iteration method is Euler method, and the time step is 1.0 × 10-5s (the time step is related to the particle diameter of the particles and is 1.0X 10-5s, the time step is 18.6343% of the rayleigh time step, satisfying the computation reference that the fixed time step is generally lower than 40% of the rayleigh time step); the total simulation time was 15s (the total simulation time was selected according to the actual experiment and was such that all particles reached a steady state without change).
Preferably, for the physical experiment to determine the angle of repose α and the angle of friction γ, angle measurement software is used to determine, for example, data taken by a high-speed camera system is taken and averaged. And (3) determining the repose angle alpha and the friction angle gamma after the numerical simulation experiment by using an angle measuring tool carried by EDEM discrete element software in the same method, and comparing the results of the physical experiment and the numerical test.
Fig. 9 is a flowchart of the EDEM discrete meta-software provided by the embodiment of the present invention.
As shown in fig. 9, the operation steps in the EDEM discrete meta-software are as follows:
s2.1: creating a model:
designing global model parameters: selecting a measurement unit, inputting a model name and description thereof, and selecting a contact module; setting gravity and defining materials; defining interactions between materials;
definition of particles: creating a new particle type, and defining the surface and the attribute of the particle;
defining the geometry: introducing a geometric body, setting moving plane parameters, and creating the geometric body as a particle factory;
set simulation area and create particle factory: and (4) creating a particle factory and setting initial parameters of the particle factory.
S2.2: simulation operation: setting time options and setting network options.
S2.3: and (3) analysis results: configuring a display mode, coloring elements, creating a grid unit group and exporting data play.
On the basis of example 2, example 3: the process of performing orthogonal experimental treatment on the non-circular seed particle model according to the seed contact correction parameters to obtain the influence parameter sequencing information of the non-circular seed particle model comprises the following steps:
selecting a particle Poisson ratio, an inter-particle elastic recovery coefficient, an inter-particle static friction coefficient and an inter-particle dynamic friction coefficient from the non-circular seed particle model;
taking the particle Poisson ratio, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient as independent variables, taking the repose angle alpha and the friction angle gamma in the seed contact measurement parameters as evaluation indexes, respectively selecting n numerical values from each independent variable, wherein n is a positive integer, carrying out orthogonal experiment processing on all the selected numerical values and the evaluation indexes, and obtaining influence parameter sorting information according to orthogonal processing results.
Specifically, the preset number n is 3-5.
Specifically, the orthogonal experimental treatment is an orthogonal experimental treatment performed by discrete element software. And selecting 4 influencing factors of the Poisson ratio of the non-round seed particles, the elastic recovery coefficient of the interaction of the particles, the static friction coefficient and the dynamic friction coefficient as independent variables according to the evaluation indexes of the repose angle alpha and the friction angle gamma in the seed contact correction parameters, preferably 3-5 numerical values of each independent variable, performing an orthogonal numerical test, performing regression analysis on orthogonal results, and judging the primary and secondary sequence of the 4 influencing factors on the test indexes.
The specific preferred scheme is as follows: the Poisson ratio of the particles, the elastic recovery coefficient of the interaction between the particles, the static friction coefficient and the dynamic friction coefficient are 4 influencing factors which are independent variables, each independent variable takes 3 level values of low, medium and high, the 3 level values are selected according to the parameters of the particles with similar properties of the existing data, and because different particle properties are different, 3 levels of low, medium and high are selected according to the existing reference data to perform regression analysis (in order to improve the precision, the difference between each level can be reduced, and more groups of orthogonal tests are performed), wherein the influencing factors and the levels of the numerical tests are shown in a table 1, and the table 1 is the selection of the numerical test factors and the level values.
TABLE 1
Preferably, an orthogonal numerical test is designed by applying a test Design software Design expert, the specific scheme is a 4-factor 3 horizontal orthogonal test, 9 sets of tests are counted, the combination of the orthogonal numerical tests and the response values of the repose angle alpha and the friction angle gamma are shown in table 2, and the table 2 is an orthogonal numerical test table.
TABLE 2
Preferably, regression analysis is performed on the orthogonal results, a factor level effect graph is prepared, the primary and secondary sequence of the 4 influence factors on the test indexes is judged, the influence factor regression analysis table in table 3 is obtained, and the influence factor regression analysis in table 3 is obtained.
TABLE 3
Assuming a case, according to the results in table 3 above, if the dynamic friction coefficient has the least influence on the evaluation index, the regression analysis can be performed by using 3 influencing factors, such as the particle poisson ratio, the elastic recovery coefficient and the static friction coefficient, as a combined model, and the regression result analysis table is shown in table 4, if the variance is lower than 0.05 for the P values of the 3 influencing factors, such as the particle poisson ratio, the elastic recovery coefficient and the static friction coefficient, it is shown that the combined model has a very significant influence on the evaluation index repose angle α and the friction angle γ.
TABLE 4
In the embodiment, the orthogonal test method adopted by the invention takes the material parameters (the Poisson ratio of the particles, the elastic recovery coefficient of the interaction between the particles, the static friction coefficient and the dynamic friction coefficient 4 influencing factors as independent variables) which are difficult to test the non-circular seed particles as independent variables, and carries out orthogonal numerical test on the stacking behavior of the non-circular seed particles through discrete element software, so that the test workload is relatively low, the calculation speed is high, the reliability of the numerical test result is high, and the problems of high resource consumption, low precision of the measured result and the like caused by manual physical experiments are solved.
On the basis of example 3, example 4: the process of optimizing the non-circular seed particle model according to the influence parameter sorting information to obtain the optimized model parameters comprises the following steps:
and taking the seed contact measurement parameters as target values, establishing a regression model through the influence parameter sequencing information and the non-circular seed particle model based on an optimization algorithm, and obtaining optimization model parameters through the regression model.
On the basis of example 3, example 5: the process of obtaining non-circular seed particle model parameters for calibrating non-circular seeds according to the optimized model parameters and the non-circular seed particle model comprises the following steps:
optimizing the non-circular seed particle model through the optimized model parameters, performing a numerical simulation experiment on the optimized non-circular seed particle model to obtain an optimized repose angle alpha and a friction angle gamma, comparing the optimized repose angle alpha and friction angle gamma with the repose angle alpha and friction angle gamma of the seed contact measurement parameters respectively, if the difference values are smaller than the preset difference value, and if the difference values are smaller than the preset difference value, meeting the precision requirement, and obtaining non-circular seed particle model parameters for calibrating non-circular seeds through the optimized non-circular seed particle model.
It should be understood that the preset difference includes an angle of repose preset difference and a friction angle preset difference.
As shown in fig. 3, the following describes the overall implementation steps:
s101, introducing basic seed parameters and contact measurement parameters, wherein the basic seed parameters comprise density, particle Poisson ratio, particle size, shear modulus, inter-particle elastic recovery coefficient, inter-particle static friction coefficient and inter-particle dynamic friction coefficient, and the contact measurement parameters comprise an angle of repose alpha and an angle of friction gamma which are obtained through physical experiment measurement;
s102, constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
s103, selecting a particle Poisson ratio, an inter-particle elastic recovery coefficient, an inter-particle static friction coefficient and an inter-particle dynamic friction coefficient from the non-circular seed particle model;
s104, taking the particle Poisson ratio, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient as independent variables, taking the repose angle alpha and the friction angle gamma in the seed contact correction parameter as evaluation indexes, and respectively selecting n numerical values from each independent variable according to a preset numerical value n;
s105: performing orthogonal experimental treatment on all selected numerical values and the evaluation indexes, and obtaining influence parameter sequencing information according to orthogonal treatment results;
s106, taking the seed contact measurement parameters as target values, establishing a regression model through the influence parameter sequencing and the non-circular seed particle model based on an optimization algorithm, and obtaining optimization model parameters through the regression model;
s107, optimizing the non-circular seed particle model through the optimized model parameters;
s108, carrying out a numerical simulation experiment through the optimized non-circular seed particle model to obtain an optimized repose angle alpha and an optimized friction angle gamma, respectively comparing the optimized repose angle alpha and the optimized friction angle gamma with the repose angle alpha and the friction angle gamma of the seed contact measurement parameters, and if the difference values are smaller than a preset difference value, meeting the precision requirement and executing S109;
s109, obtaining non-circular seed particle model parameters for calibrating non-circular seeds through the optimized non-circular seed particle model;
and S1010, finishing calibration.
In the above embodiment, if the accuracy requirement is not satisfied, the data is discarded, and the orthogonal experiment processing is performed again.
Fig. 2 is a functional block diagram of a non-circular seed particle model parameter calibration apparatus according to an embodiment of the present invention.
Example 6: as shown in fig. 2, a non-circular seed particle model parameter calibration apparatus includes:
the device comprises an importing module, a data processing module and a data processing module, wherein the importing module is used for importing basic seed parameters and seed contact measurement parameters, and the basic seed parameters and the seed contact measurement parameters are obtained by measuring through a physical experiment in advance;
the construction module is used for constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
the processing module is used for carrying out regression analysis on the non-circular seed particle model through the seed contact measurement parameters to obtain the influence parameter sequencing information of the non-circular seed particle model;
optimizing the non-circular seed particle model according to the influence parameter sequencing information to obtain optimized model parameters;
and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
Example 7 on the basis of example 6: the basic parameters of the seeds comprise density, particle Poisson ratio, granularity, shear modulus, inter-particle elastic recovery coefficient, inter-particle static friction coefficient and inter-particle dynamic friction coefficient, and the parameters for measuring the seed contact comprise an angle of repose alpha and an angle of friction gamma.
On the basis of example 6, example 8: the process of performing orthogonal experimental treatment on the non-circular seed particle model according to the seed contact correction parameters in the treatment module to obtain the influence parameter sequencing information of the non-circular seed particle model comprises the following steps:
selecting a particle Poisson ratio, an inter-particle elastic recovery coefficient, an inter-particle static friction coefficient and an inter-particle dynamic friction coefficient from the non-circular seed particle model;
taking the particle Poisson ratio, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient as independent variables, taking the repose angle alpha and the friction angle gamma in the seed contact measurement parameters as evaluation indexes, respectively selecting n numerical values from each independent variable, wherein n is a positive integer, carrying out orthogonal experiment processing on all the selected numerical values and the evaluation indexes, and obtaining influence parameter sorting information according to orthogonal processing results.
Example 9: a non-round seed particle model parameter calibration device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and when the computer program is executed by the processor, the non-round seed particle model parameter calibration method is realized.
Example 10: a computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the non-round seed particle model parameter calibration method as described above.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A non-circular seed particle model parameter calibration method is characterized by comprising the following steps:
introducing basic seed parameters and contact measurement parameters, wherein the basic seed parameters and the contact measurement parameters are obtained by physical experiment measurement in advance;
constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
carrying out orthogonal experimental treatment on the non-circular seed particle model according to the seed contact measurement parameters to obtain influence parameter sequencing information of the non-circular seed particle model;
optimizing the non-circular seed particle model according to the influence parameter sequencing information to obtain optimized model parameters;
and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
2. The method for calibrating the parameters of the non-circular seed particle model according to claim 1, wherein the basic parameters of the seed include density, Poisson ratio of the particles, particle size, shear modulus, elastic recovery coefficient between the particles, static friction coefficient between the particles and kinetic friction coefficient between the particles, and the contact measurement parameters of the seed include angle of repose α and angle of friction γ.
3. The method for calibrating parameters of a non-circular seed particle model according to claim 2, wherein the step of performing orthogonal experimental processing on the non-circular seed particle model according to the seed contact measurement parameters to obtain the order information of the influence parameters of the non-circular seed particle model comprises:
selecting a particle Poisson ratio, an inter-particle elastic recovery coefficient, an inter-particle static friction coefficient and an inter-particle dynamic friction coefficient from the non-circular seed particle model;
taking the particle Poisson ratio, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient as independent variables, taking the repose angle alpha and the friction angle gamma in the seed contact measurement parameters as evaluation indexes, respectively selecting n numerical values from each independent variable, wherein n is a positive integer, carrying out orthogonal experiment processing on all the selected numerical values and the evaluation indexes, and obtaining influence parameter sorting information according to orthogonal processing results.
4. The method for calibrating parameters of a non-circular seed particle model according to claim 3, wherein the step of optimizing the non-circular seed particle model according to the influence parameter ranking information to obtain optimized model parameters comprises:
and taking the seed contact measurement parameters as target values, establishing a regression model through the influence parameter sequencing information and the non-circular seed particle model based on an optimization algorithm, and obtaining optimization model parameters through the regression model.
5. The method for calibrating non-circular seed particle model parameters of claim 4, wherein said obtaining non-circular seed particle model parameters for calibrating non-circular seeds according to said optimized model parameters and said non-circular seed particle model comprises:
optimizing the non-circular seed particle model through the optimized model parameters, performing a numerical simulation experiment on the optimized non-circular seed particle model to obtain an optimized repose angle alpha and a friction angle gamma, comparing the optimized repose angle alpha and friction angle gamma with the repose angle alpha and friction angle gamma of the seed contact measurement parameters respectively, meeting the precision requirement if the difference values are smaller than a preset difference value, and obtaining non-circular seed particle model parameters for calibrating non-circular seeds through the optimized non-circular seed particle model.
6. A non-circular seed particle model parameter calibration device is characterized by comprising:
the device comprises an importing module, a data processing module and a data processing module, wherein the importing module is used for importing basic seed parameters and seed contact measurement parameters, and the basic seed parameters and the seed contact measurement parameters are obtained by measuring through a physical experiment in advance;
the construction module is used for constructing a non-circular seed particle model according to the seed basic parameters and based on a discrete element method;
the processing module is used for carrying out regression analysis on the non-circular seed particle model through the seed contact measurement parameters to obtain the influence parameter sequencing information of the non-circular seed particle model;
optimizing the non-circular seed particle model according to the influence parameter sequencing information to obtain optimized model parameters;
and obtaining non-circular seed particle model parameters for calibrating the non-circular seeds according to the optimized model parameters and the non-circular seed particle model.
7. The non-round seed particle model parameter calibration device of claim 6, wherein the seed basic parameters comprise density, particle poisson's ratio, particle size, shear modulus, inter-particle elastic recovery coefficient, inter-particle static friction coefficient and inter-particle kinetic friction coefficient, and the seed contact determination parameters comprise angle of repose α and angle of friction γ.
8. The non-circular seed particle model parameter calibration device according to claim 7, wherein the process of performing the orthogonal experimental processing on the non-circular seed particle model according to the seed contact correction parameter in the processing module to obtain the influence parameter ranking information of the non-circular seed particle model comprises:
selecting a particle Poisson ratio, an inter-particle elastic recovery coefficient, an inter-particle static friction coefficient and an inter-particle dynamic friction coefficient from the non-circular seed particle model;
taking the particle Poisson ratio, the inter-particle elastic recovery coefficient, the inter-particle static friction coefficient and the inter-particle dynamic friction coefficient as independent variables, taking the repose angle alpha and the friction angle gamma in the seed contact measurement parameters as evaluation indexes, respectively selecting n numerical values from each independent variable, wherein n is a positive integer, carrying out orthogonal experiment processing on all the selected numerical values and the evaluation indexes, and obtaining influence parameter sorting information according to orthogonal processing results.
9. An apparatus for calibrating parameters of a non-round seed particle model, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for calibrating parameters of a non-round seed particle model according to any one of claims 1 to 5.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the method for non-circular seed particle model parameter calibration according to any one of claims 1 to 5.
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