95 results about "Discrete element simulation" patented technology

The invention discloses a dangerous rock falling motion feature analysis method based on discrete element simulation, and belongs to the technical field of engineering geology. Comprising the steps that dangerous rock data are collected through multiple means and preprocessed, and a three-dimensional visual dangerous rock mass initial model is constructed; based on the three-dimensional visual dangerous rock mass initial model, constructing a discrete element model, defining contact types and parameters among joint units by adopting a Hertz-mindlin contact theory, and determining normal contact force; based on the discrete element model and the normal contact force, a discrete element simulation technology is adopted, multi-physical field factors are considered to simulate dangerous rock falling movement, a discrete element model simulation result is obtained, a dynamic updating method is adopted for joint failure judgment, and the discrete element model is verified through an indoor physical model test and field monitoring data. And designing and implementing a prevention project according to a simulation result, thereby effectively preventing dangerous rock falling disasters and ensuring project safety.

The invention belongs to the technical field of discrete element simulation, and particularly relates to a digital twinborn discrete element model construction method based on CT (Computed Tomography) scanning, which comprises the following three steps of: in a CT scanning stage, acquiring a high-resolution cross-sectional image by optimizing scanning parameters, and carrying out image noise reduction and feature enhancement processing; in the structural analysis stage, a segmentation model is constructed based on a deep convolutional neural network, and automatic annotation of a scanned image and intelligent prediction of a three-dimensional space topological relation are realized; in the discrete element modeling stage, the reconstructed three-dimensional grid model is converted into a discrete element model with real physical attributes through parameter calibration, and discrete element simulation is achieved. According to the method, the non-destructive detection advantage of industrial CT and the feature extraction capacity of deep learning are combined, microstructure characterization is converted into macroscopic performance prediction, and a reliable simulation analysis method is provided for performance optimization of the composite material.

The invention relates to the technical field of underwater propeller modeling, and discloses an underwater propeller annular propeller modeling method based on analog simulation. The method comprises the following steps: constructing an initial blade fluid domain grid model by using a discrete element simulation algorithm based on three-dimensional geometric parameters and material attribute data of an annular propeller; after multiphase flow coupling boundary conditions are applied to the initial model, fluid dynamic load distribution data are obtained through a transient flow field solver; generating a blade deformation characteristic value according to the load data, and if the blade deformation characteristic value exceeds a preset tolerance interval, activating a dynamic grid reconstruction instruction; fluid-structure interaction interface pressure pulsation data is extracted based on the instruction, a stress concentration coefficient is calculated in combination with material attributes, and an abnormal signal is generated when a safety threshold value is exceeded; and when the signal is triggered, the vortex field tracking module is called to update the fluid domain grid model.

The invention discloses a discrete element contact parameter calibration device and method based on a dynamic repose angle, the device comprises a platform on which a motor and a motor control panel are fixedly arranged, the motor control panel is connected with a PC, the motor control panel is connected with the motor, and an output shaft of the motor is fixedly connected with a driving belt wheel; a first bearing seat and a second bearing seat are installed on the top face of the platform, the first bearing seat is rotationally connected with a first rotating shaft, the first rotating shaft is fixedly connected with a driven belt wheel connected with the driving belt wheel through a belt, and the two ends of the first rotating shaft are fixedly connected with driving wheels respectively. The second bearing seat is rotationally connected with a second rotating shaft; two ends of the second rotating shaft are fixedly connected with supporting wheels respectively; the cylinder is in contact with the surfaces of the driving wheel and the supporting wheel; a high-speed camera connected with a PC (Personal Computer) is fixedly arranged on the top surface of the platform, and discrete element analogue simulation software is installed in the PC. According to the invention, the dynamic angle of repose of the material in different flowing states can be measured in real time, and high-precision calibration of contact parameters among particles is facilitated through a discrete element model.

Provided are a method and apparatus for calibrating contact parameters of electrode materials, and a computer storage medium. The method includes the following operations. A discrete element simulation test is performed on an electrode material based on a calibration model and calibration ranges of contact parameters of the electrode material when an axial force-displacement curve of a target pellet of the electrode material meets an adaption condition of the contact model; then a simulated axial pressure-axial strain relationship of the electrode material is acquired based on a target discrete element simulation parameter of the electrode material; whether the contact parameters are calibrated successfully is judged based on this relationship; and when the contact parameters are calibrated successfully, calibration results of the contact parameters are determined.

The invention discloses a discrete element simulation method for slope unstable seepage, effectively solves the problem of nonlinear flow simulation distortion caused by excessive simplification of a traditional seepage model, and improves the calculation precision of transient processes such as rainstorm infiltration. The dynamic porosity feedback mechanism can reflect the influence of the internal structure change of the slope on the seepage field in real time, and more reliable pore water pressure prediction data is provided for landslide early warning. The establishment of a multi-scale coupling framework provides a new technical approach for unstable seepage analysis under complex geological conditions, a physical information neural network effectively avoids the overfitting risk of a pure data driven model, and an improved multi-objective optimization algorithm can quickly position an optimal parameter combination under complex constraint conditions. A dynamic updating mechanism can automatically adjust model parameters according to slope state changes, and the timeliness and accuracy of landslide early warning under the unstable seepage condition are remarkably improved.

The invention discloses a viscous fine particle-based MICP rock fracture restoration three-dimensional discrete element simulation method, which comprises the following steps: carrying out a direct shear test on a rock sample, obtaining stress data and particle size data of the rock sample in the test process, and establishing a relation model; establishing an initial model of fine particle cementation, setting a scaling coefficient and creating boundary and contact conditions; physical attributes and geometric attributes are given to each sphere, the process of repairing rock fractures by calcium carbonate is simulated under the gravity effect, and a discrete sphere set within the boundary range is stored as a first model and placed into a particle attribute giving module; simulating calcium carbonate precipitation; gradually loading extrusion force, detecting the stability of the model by utilizing a balance force function, storing the model as a model II, and putting the model II into a shear loading module; and evaluating a reinforcement effect and judging a failure mode. According to the method, the biological rock fracture repairing effect and the numerical simulation of the damage state can be quickly and flexibly predicted at low cost.

The invention discloses a mechanism and data fusion driven wheat and corn harvesting and threshing grain damage rate evolution prediction method. The method comprises the following steps: 1, acquiring response data of wheat or corn grain damage rate along with time change under different threshing working conditions based on a bench test; 2, on the basis of a discrete element simulation analysis result, constructing a wheat and corn threshing grain damage rate physical model to obtain response data of the grain damage rate changing along with time under different threshing working conditions; 3, constructing a wheat corn harvesting and threshing grain damage rate prediction model training data set; 4, establishing a wheat corn harvesting and threshing grain damage rate evolution prediction network model driven by mechanism and data fusion; 5, a loss function of the prediction model is established and trained, and an optimal grain damage rate evolution prediction model is obtained and used for predicting the grain damage rate of wheat and corn harvesting and threshing. According to the method, the evolution trend of the grain damage rate in the wheat and corn harvesting and threshing process can be accurately obtained in advance, and a basis is provided for wheat or corn harvesting and threshing self-adaptive damage suppression regulation and control.

The invention discloses a road patrol image disease three-dimensional size and maintenance material estimation method and device, and the method comprises the steps: collecting road patrol image data and laser radar three-dimensional point cloud data, carrying out the visual angle correction, target recognition and feature extraction of the image data, carrying out the screening, cutting, meshing and graying of the three-dimensional point cloud data, and carrying out the detection of the three-dimensional size and maintenance material. Constructing a pavement disease image and a pavement three-dimensional disease data set through feature matching, training an adversarial generative network model, inputting the pavement disease image, generating a corresponding pavement disease image with elevation information, and calculating the three-dimensional size of the pavement disease. And performing three-dimensional reconstruction on the pavement disease image with the elevation information, and estimating the use amount of the asphalt mixture by adopting discrete element simulation. According to the method, the three-dimensional size and the maintenance material of the pavement disease can be estimated by using the efficiently acquired road patrol image data, and support is provided for maintenance strategy formulation and cost estimation.

The invention discloses a rock-fill concrete three-dimensional simulation system based on digital twinborn and discrete elements, relates to the technical field of digital simulation processing, and solves the problems that an existing three-dimensional simulation method neglects the real form and contact relation of a rock-fill framework, cannot describe the real microstructure of rock-fill concrete, and cannot describe the real microstructure of rock-fill concrete. And the deviation between the simulation prediction result and the real mechanical behavior is too large. The three-dimensional simulation system comprises an acquisition processing module, a twin construction module, a discrete control module, a contact calibration module, a simulation interaction module and an evaluation decision module. The method comprises the following steps: constructing a digital model of a rock-fill skeleton by combining a real form and a contact relationship of the rock-fill skeleton through a digital twinning technology and a discrete element simulation principle, and tracking and calculating a moving state of particles in a rock-fill concrete numerical model according to a Newton's law of motion and alternate iteration of interaction among the particles; therefore, the real microstructure of the rock-fill concrete is visually described, and the degree of deviation between a simulation prediction result and a real mechanical behavior is reduced.

The invention discloses a coal mine gangue solid filling deformation bearing regulation and control method. The method comprises the following steps that deformation bearing indexes of a filling body are determined according to different filling control targets; the method comprises the following steps: acquiring a field gangue filling material, and acquiring physical and mechanical characteristics of gangue and an influence rule of different particle size gradation, lithology and compactness on deformation characteristics of a gangue filling body through an experimental means; selecting one kind of lithologic gangue, and determining discrete element simulation microcosmic parameters by adopting a trial-and-error method; the size of a large-scale numerical model and the gangue particle size range are determined by considering the actual working face size, the gangue lateral confinement compression stress deformation characteristics under the engineering scale are simulated, and the stress-strain relation is fitted according to the stress deformation characteristics. According to the on-site gangue compaction degree and the characteristics of the memory effect of gangue, the fitting curve is translated leftwards to obtain a gangue stress-strain fitting curve under the tamping effect of the filling support, numerical simulation under the engineering condition is conducted through a translated fitting formula, and gangue filling parameters are regulated and controlled according to the simulation result. Experiments, scale effect correction and numerical simulation are organically combined, test operation is easy and convenient, the bearing performance of the filling body is accurately regulated and controlled, the method is suitable for various complex mining scenes, regulation and control efficiency is high, adaptability is high, and reliable technical support is provided for green filling coal mining.

The application discloses a 3D printing core preparation method coupled with discrete element simulation and mechanical experiment, and the method comprises six steps of printing a core, soaking the core, constructing a model, first-time calibration of the model, second-time calibration of the model and formula fitting. Compared with the prior art, the 3D printing core preparation method coupled with discrete element simulation and mechanical experiment has the advantages that the preparation of 3D printing cores with different strengths is realized, the rock shortage in underground energy storage engineering rock mechanics experiments is avoided, and the requirements of rock mechanics experiments in underground energy storage engineering are met.

The invention belongs to the technical field of lithium batteries, and relates to a material particle size grading optimization method and system based on a genetic algorithm and discrete element simulation. The method comprises the following steps: carrying out particle size distribution test on a material to be optimized to obtain a particle size distribution result as an optimizable particle size range; carrying out three-component peak separation on the obtained particle size distribution result, and approximately representing the particle size distribution of the material through three-component spherical particles; optimizing the stacking density of the three-component spherical particles in an optimizable particle size range by utilizing a genetic algorithm, and enabling the optimized stacking density of the three-component spherical particles to be approximate to the compaction density of the three-component spherical particles; obtaining physical property data of the material; and carrying out simulation verification on the compaction density of the three-component spherical particles obtained by optimization by using a discrete element model according to physical property data of a material. According to the method, optimization design is carried out on the particle size of the material based on discrete element analog simulation and a genetic optimization algorithm, and the large and small particle gradation of the high-compaction-density material can be rapidly screened.

The invention relates to the technical field of lithium batteries, and particularly discloses a parameter optimization method of a mixed process and computer equipment. The method comprises the following steps: constructing a discrete element simulation model by mixing equipment based on the actual composition of a battery dry powder material, and configuring each simulation parameter; based on the discrete element simulation model and the simulation parameters, position coordinates and component types of all particles in the mixing process are obtained through simulation; on the basis of the position coordinates and the component types of all the particles, an overall mixing index is obtained; constructing a data set based on the combination of each target simulation parameter configured in multiple simulation processes and the obtained overall mixing index, and training a reduced-order model based on the data set; and connecting the trained reduced-order model to an optimizer to determine an optimal value combination of each target simulation parameter, and applying the optimal value combination of each target simulation parameter to a mixing process of the electrode dry powder. Therefore, the technical limitation of mixed quality evaluation is overcome, and the parameter optimization efficiency is improved.

The invention provides a method and a system for analyzing mechanical characteristics of a dirty ballast under a dynamic-melting effect, and relates to the technical field of mechanical characteristics of ballasts.The method comprises the specific steps of collecting mechanical characteristic parameters and influence parameters of a dirty ballast sample, training an adversarial network according to the mechanical characteristic parameters and the influence parameters, obtaining simulation parameters, constructing a discrete element simulation model according to the sample parameters, and calculating the mechanical characteristics of the dirty ballast. The method comprises the following steps of: calibrating by using simulation parameters, inputting influence parameters of a to-be-analyzed dirty ballast into a calibrated model to obtain mechanical property parameters of the to-be-analyzed dirty ballast, acquiring key data such as stress standard deviation, sequentially generating a deformation coefficient, a strength damage coefficient and a bearing coefficient through data processing and correlation analysis, and comparing the bearing coefficient with a preset threshold value to obtain a to-be-analyzed dirty ballast model. Therefore, the bearing capacity of the dirty ballast to be analyzed is accurately evaluated. According to the method, multi-factor coupling is comprehensively considered, internal complex changes are accurately quantified, the bearing capacity of the dirty ballast structure under actual complex working conditions is accurately evaluated, and evaluation deviation is reduced.

The application provides a kind of microspherical aluminum powder contact parameter calibration method for energetic materials, and the basic principle of the method is to combine discrete element simulation-experiment, compare simulation angle of repose and experimental angle of repose error, take different calibration order for different error, and gradually complete the contact parameter calibration of microspherical aluminum powder for energetic materials.The application first proposes that for microspherical aluminum powder, the inter-particle collision restitution coefficient, sliding friction coefficient, rolling friction coefficient and adsorption energy of the four contact parameters need to be calibrated, and the application first clearly defines the appropriate roughening range of aluminum powder when modeling, the calibration order under different conditions and the contact parameter calibration range, the application reduces the workload of microspherical aluminum powder contact parameter calibration, and promotes the efficient preparation of energetic materials.

The invention discloses a three-dimensional random field-based rockfill material space variability discrete element simulation method. The method specifically comprises the following steps of: 1, constructing a triaxial test discrete element model and determining related parameters; step 2, generating a three-dimensional random field based on Karhunen-Loeve expansion; and step 3, random interpolation and assignment of particle contact parameters. Aiming at the technical defect that the real mechanical behavior of the rockfill material cannot be accurately reflected due to the fact that homogeneous hypothesis, two-dimensional modeling and continuous medium analysis are generally adopted in the prior art, the method comprises the following steps: constructing a three-dimensional random field of the rockfill material and introducing a discrete element particle system; more reasonable and more practical description of rockfill material space variability, macroscopic mechanical characteristics and mesoscopic mechanism deformation is realized.

The invention discloses an investigation and evaluation method for railway engineering crossing a high and steep bank slope area, and aims to solve the problems of incomplete investigation and inaccurate evaluation of the high and steep bank slope area. According to the method, a systematized process is formed by constructing five logic progressive stages: firstly, carrying out regional geology and line collaborative analysis, and macroscopically avoiding an unfavorable geological area; secondly, preliminarily identifying an unfavorable geologic body by using a remote sensing technology, and optimizing a line trend; a multi-precision unmanned aerial vehicle is adopted for investigation, and fine three-dimensional information of the dangerous terrain is safely and efficiently obtained; then, comprehensive investigation is carried out under the guidance, and the system obtains physical mechanics and structural characteristics of the rock-soil body; and finally, based on detailed investigation data, carrying out stability comprehensive evaluation by fusing qualitative analysis, discrete element simulation and / or three-dimensional numerical simulation so as to output optimization suggestions of the abutment and the pile foundation. According to the method, the discrete technology is integrated into a standard process, so that the systematicness and scientificity of investigation and evaluation and the reliability of engineering decision are remarkably improved.

The application provides a kind of based on the broken energy consumption quantification evaluation method of blasting pile size distribution, it is related to the technical field of mining engineering, including the following steps: the size distribution data acquisition and energy consumption collection of blasting pile;Discrete element simulation and energy consumption-size distribution relationship modeling;Model energy consumption correction and actual energy consumption mapping;Target size distribution determination and characteristic curve construction;Size distribution curve form difference quantification;Four-level quantification evaluation system construction.The application generates size sample according to the size distribution of on-site blasting pile, discrete element simulation and multivariate nonlinear regression modeling, combined with the correction factor matrix calibrated by field data, so that the deviation between model energy consumption and actual energy consumption is controlled within 5%, and high-precision quantification of crushing energy consumption is realized.

According to the multi-objective optimization method for the working parameters of the sprocket type residual film recycling machine, the sprocket type residual film recycling machine is used as a research object, and the angular velocity of a picking device, the angular velocity of a film and impurity separating device and the working angle of the film and impurity separating device are set as key variables to be optimized. In order to obtain the optimal residual film impurity rate and picking-up rate, discrete element simulation is performed through an EDEM, a back propagation neural network is trained by using simulation data, and a prediction model of the residual film impurity rate and picking-up rate is established. A prediction result of the neural network is optimized by adopting a non-dominated multi-objective genetic algorithm, and optimal working parameters are given in a Pareto frontier form. When the angular speed of the picking device is 58.80 rad / s, the angular speed of the film and impurity separating device is 40 rad / s, and the angle of the film and impurity separating device is 35.51, the impurity content of the residual film is 24.26%, and the picking rate of the residual film is 83.67%. And working parameters are selected in the Pareto front, so that the impurity rate of the residual film can be effectively reduced on the basis of keeping a relatively high residual film picking-up rate.

The invention discloses an efficient simulation method for collision between long ellipsoid particles and a wall surface based on discrete elements, and belongs to the technical field of gas-solid two-phase flow numerical simulation. The method comprises the steps that a universal long ellipsoid particle-wall surface collision elastic force model is established, and the model represents a universal function relation between elastic force and particle geometry, material attributes and kinematics state parameters; based on the elastic force model and the momentum theorem, constructing a particle-wall surface collision dynamic model; based on the kinetic equation, a discrete time method and a numerical integration algorithm are adopted, and force, speed and energy parameters, changing along with time, of the long ellipsoid particles and the wall face in the whole collision period are solved. According to the method, the analytic model is embedded into a discrete element simulation framework to replace calculation-intensive finite element solution, so that second-level efficient solution is realized while the calculation precision is ensured, and the calculation efficiency is remarkably improved.

The invention relates to the technical field of particle crushing simulation, in particular to a particle crushing simulation method and system based on a KAN neural network. The method comprises the following steps: constructing a single-particle discrete element simulation model, and simulating particle crushing strength through the single-particle discrete element simulation model; adjusting the positions of the loaded particles to obtain a plurality of stress states, and analyzing the characteristic strength indexes in the stress states by using a traditional failure criterion to obtain an initial data set; processing the initial data set through rotation enhancement, exchange enhancement and balanced sampling to obtain an enhanced data set; and training a KAN neural network which introduces a learnable activation function by using the enhanced data set to obtain a particle crushing simulation model, and completing particle crushing simulation through the particle crushing simulation model. The device can accurately simulate the crushing behavior of the two-dimensional circular particles in a complex stress state, and is suitable for particle crushing research and engineering application in the fields of geotechnical engineering, powder technology and the like.

The present application relates to the field of oil and gas field development engineering, disclose a kind of intelligent optimization method for discrete element contact parameters of proppant particle and flexible fiber mixture, comprising: based on the principle of injection method angle of repose experiment, build micro funnel device, reduce the scale of experiment under the premise of keeping the original size of material, improve the efficiency of calculation;Adopt "ball column chain + joint" fiber model, coupling JKR adhesion model represents the interfacial adhesion between particles, establish high-precision discrete element simulation model;Through Plackett-Burman, steepest climbing experiment screening key influencing factors, for the problem that each factor and angle of repose is nonlinear mapping relationship, construct GA-BP model, combine GA algorithm optimization, preferred parameter combination, compare the results of discrete element simulation and laboratory test results, verify the accuracy of contact parameters.The present application solves the problem that the existing method is difficult to calibrate mixture, improves the precision and efficiency of parameter calibration, and is helpful to the research and development of hydraulic fracturing process.

The invention discloses a particle coating elastic modulus rapid calibration method based on a fractal continuum theory, and the method comprises the steps: firstly constructing free accumulation particle coatings with different heights, and recording the height, particle size, residual dimension in the height direction and section fractal dimension perpendicular to the height direction of the particle coatings; constructing a relative coating height parameter, and establishing a correlation between the relative coating height and the remainder dimension as well as the section fractal dimension; discrete element simulation of the uniaxial compression process is carried out on each structural coating, and the compression amount and the compression force change of the coating in the compression process are recorded; constructing a relational expression of the compression amount and the compression force of the particle coating, wherein the relational expression relates to a remainder dimension, a section fractal dimension and a harmonic index to be fitted; fitting a harmonic index p based on the simulation data; based on the relational expression and the harmonic index, the coating compression amount and the compression force reconstruction value are recorded, and based on the constitutive relation of the uniaxial compression process of the continuum, inversion calibration is carried out on the coating elasticity modulus, and rapid calibration of the elasticity modulus of the whole coating growth process can be achieved.

The invention relates to a method for improving the dustpan filling rate of a bucket elevator. The method comprises the steps that dustpan filling images and equipment data are obtained in real time through a sensor network; transmitting the data to an edge AI calculation unit, performing millisecond-level reasoning by using a lightweight convolutional neural network model, estimating a filling rate in real time and identifying a material type; based on a reasoning result, dynamically and cooperatively adjusting operation parameters, including changing the geometric volume of the deformable intelligent winnowing pan through a micro driving mechanism, adjusting the speed of the elevator and controlling a feeding auxiliary device, and continuously optimizing decision logic of the system by a digital twin model, according to the model, an optimization strategy set is generated in advance through discrete element simulation and a Bayesian optimization algorithm, and the system has a maintenance function of predicting performance degradation by analyzing current and vibration data and can cooperate with upstream and downstream equipment to realize global energy efficiency optimization; according to the scheme, the problem that the filling rate of a traditional elevator is unstable due to variable materials and fixed bucket types is solved, and self-adaptive precise control is achieved.

The invention discloses a grouting body surface roughness calculation method, device and equipment and a product. The grouting body surface roughness calculation method comprises the steps that the porosity and grain composition curve of a to-be-grouted area soil body is obtained; target parameters are extracted from the grain composition curve, and the average grain size of grain groups of different specifications is determined; based on the grain composition curve, a preset coefficient representing a parameter linear relation is obtained through discrete element simulation solution; and based on the target parameter and the porosity, combining with a preset coefficient for quantification to obtain the surface roughness. By means of the method, before grouting construction, the roughness can be rapidly predicted only through conventional soil test data, and high cost, heavy labor and material waste for preparing a large solid grouting body sample can be avoided; according to the method, reliable roughness input parameters are provided for accurately analyzing complex interface mechanical behaviors of the grouting body and the surrounding soil body in the coarse-grained soil, the safety and economical efficiency of engineering design can be improved, and the method can be widely applied to the technical field of electric digital data processing.

The application relates to the technical field of salt rock damage self-healing, in particular to a salt rock self-healing characteristic discrete element simulation method and device.The method comprises the following steps: based on a pre-constructed healing starting criterion of target salt rock, in the case that the preset healing condition is met among the particles of the damage interface of the target salt rock, reconstructing the broken contact among the particles into a rebonding contact, giving new parallel bonding force among the particles, determining the equivalent healing stress of the damage interface of the target salt rock after healing, further quantifying the strain evolution of the volume before healing and the volume after healing, and in the case that the strain evolution result meets the preset healing termination condition, generating a discrete element simulation result of the self-healing characteristic of the target salt rock. Thus, the problem that in the related art, after the contact among the particles is broken, the bonding with strength cannot be re-established, and the microscopic mechanics recovery process of the self-healing of the salt rock after damage cannot be truly reflected is solved.

The invention belongs to the technical field of methods for simulating and evaluating the wear resistance of a screw, and particularly relates to a method for simulating and evaluating the wear resistance of the screw based on discrete elements, which comprises the following steps of: constructing a three-dimensional assembly model of the screw and a machine barrel, importing the three-dimensional assembly model into discrete element simulation software EDEM, and finishing parameter setting in the EDEM, a particle model is created through a particle generator, the density and Poisson's ratio of particles, the static / dynamic friction coefficient with the surface of a screw and particle size distribution are defined, the density, shear modulus, Poisson's ratio and friction coefficient of the screw and a machine barrel are input, and the contact model is used for describing mechanical behaviors between the particles and between the particles and the screw. The abrasion model is used for representing the screw abrasion process, determining the size and the feeding efficiency of a virtual feeding factory, adjusting the rotating speed of the screw according to equipment parameters, configuring simulation calculation parameters and then starting simulation calculation.

The invention discloses a coarse aggregate void ratio measurement method and device based on discrete element simulation, a computer program product and a storage medium, and the measurement method comprises the steps: obtaining three-dimensional point cloud data of a plurality of particles in coarse aggregate through a three-dimensional scanning device, and constructing a basic particle template library; performing particle shape feature extraction and classification on the data in the basic particle template library, selecting representative particles through a median clustering algorithm based on a classification result, and constructing a feature particle template library; and based on the characteristic particle template library, constructing a simulation environment in discrete element simulation software, carrying out coarse aggregate simulation accumulation, and calculating the void ratio. According to the method, through construction of the particle template library and selection of representative particles, diversity and representativeness of particle types are ensured, the number of particles needed by simulation is reduced, the calculation efficiency of the simulation process is optimized, and personal errors are effectively eliminated.

A biomimetic crusher blade based on the upper jaw of the longhorn beetle and its design method are disclosed, relating to the field of agricultural machinery and engineering bionics. The blade features an incline angle that slopes inward from both sides of the bottom surface, and biomimetic teeth evenly arranged at preset tooth spacing at both ends. Its contour is designed based on the upper jaw contour of the longhorn beetle, effectively disrupting the surface structure of rice straw. After penetrating the straw surface, it tears and cuts through a sliding action. This invention extracts the contour coordinates of the longhorn beetle's upper jaw to construct the geometry of the biomimetic blade, establishes a discrete element simulation model of the rice straw and the blade, and performs simulations to obtain the cutting resistance corresponding to different blade parameters such as tooth height, incline angle, and tooth spacing. Based on the simulation results, specific blade parameters are optimized and determined. This biomimetic blade can be applied to crushing rice straw, reducing cutting resistance and equipment power consumption, and improving straw crushing efficiency.