141 results about "Discrete element simulation" patented technology

The invention discloses a breakable crop stalk discrete element simulation model construction method, which comprises the following steps: firstly, measuring the actual feature size of a crop stalk, and layering the internal structure of the crop stalk based on a microstructure; then performing an experiment to obtain intrinsic parameters and contact parameters of the stalks, constructing a multi-layer geometry three-dimensional model according to measurement characteristics, filling each layer of the geometry three-dimensional model with different types of particles, establishing an initial discrete element simulation model, introducing a contact model, and constructing an initial breakable discrete element simulation model; and finally, adjusting the mesoscopic parameters of the simulation model according to the actually measured data until the mesoscopic parameters are kept consistent. According to the method, the contact model is introduced, different structural layers are filled with particles with different material attributes, the biological characteristics and mechanical characteristics of multiple layers of real stalks from outside to inside are restored, and errors are smaller when mechanical behaviors are simulated.

The invention provides a method for predicting the sand-bearing state of a ballast track bed based on deep learning. The method comprises the steps that a discrete element simulation model of a sandstorm track bed is established, and rigidity parameters of the sandstorm track bed in different positions and depth areas are obtained through analysis; based on the rigidity parameters, a vehicle-desertification ballast track coupling finite element simulation model is established, and the vibration acceleration of the sleeper in different sand-containing states is obtained through analysis; and inputting the vibration acceleration as training data into the constructed sand-bearing state prediction model to train the sand-bearing state prediction model to obtain a trained sand-bearing state prediction model, and performing sand-bearing state prediction by using the trained sand-bearing state prediction model to obtain a sand-bearing state prediction result. The sand-bearing state prediction model is a deep learning model based on a convolutional neural network and a long short-term memory network. According to the invention, the sand-bearing state of the ballast bed can be predicted with high precision. The method has important reference value for guiding line maintenance work and guaranteeing stable operation of the train.

The invention provides a discrete element simulation method of an implicit particle environment, which comprises the following steps: S1, fully mixing an ore grinding medium and a ground material, taking the mixture as a whole, and measuring a stacking angle; s2, constructing an ore grinding medium discrete element model of implied particles; s3, constructing a discrete element model of the accumulation angle measuring device; and S4, parameter optimization and discrete element simulation. The maximum error between the collision energy spectrum obtained through simulation of the method and conventional simulation is only 3.3%, meanwhile, it is avoided that a large number of mineral particles are added into simulation calculation, a large number of calculation resources are saved, and energy, speed and other information of mutual collision between media in the ore grinding operation process can be rapidly and accurately obtained.

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 provides a disease identification method and device for a dirty ballast bed in a spring melting period, and relates to the technical field of ballast bed disease identification, and the method specifically comprises the steps: building a discontinuous ligand cementation contact model based on a discrete element simulation method, and introducing a temperature-rigidity coupling factor and a temperature-strength coupling factor into the model; the method comprises the following steps: acquiring mechanical response data of a dirty ballast under the action of different temperature gradients, dirty contents and dynamic loads through a partition cascade temperature control large triaxial experimental device, and constructing a critical dynamic strain calculation model by using the mechanical response data based on a plastic stability theory; a ballast bed structure simulation model is established by combining the cemented contact model and the critical dynamic strain calculation model, a ballast bed microscopic contact behavior under the dynamic-melting coupling effect in the spring melting period is simulated, and simulation data are obtained; according to the method, correlation analysis is carried out on simulation data to generate a disease assessment index of the ballast bed, so that the disease occurrence risk is assessed, and the risk dynamic assessment of the dirty ballast bed in the spring melting period is realized.

The invention discloses a wet particle medium mechanical property numerical calculation method based on CFD-DEM multiphase flow coupling, and relates to the technical field of discrete element simulation technology and computer fluid dynamics, and the method comprises the following steps: firstly, obtaining initial information of a wet particle medium; secondly, establishing a geometric model of wet particles through a DEM solver; then, on the basis of the JKR theory, interaction parameters of the wet particles are obtained; then, on the basis of the geometric model and the interaction parameters of the wet particles, a fluid dynamics and discrete element method CFD-DEM coupling simulation model is built through a VOF coupling interface, and analogue simulation is conducted on the movement process of the robot foot paddle on the wet particle medium ground. Therefore, by adopting the numerical calculation method for the mechanical properties of the wet particle medium based on CFD-DEM multiphase flow coupling, the interaction among the wet particles can be accurately simulated, the simulation analysis precision and reliability of the wet particle medium are improved, and the dynamic simulation of the behavior of the wet particle medium is realized.

The invention discloses a cutterhead fatigue life calculation method and system, electronic equipment and a storage medium, and the method comprises the steps: dividing a cutterhead construction process into a plurality of working conditions according to geological parameters, sequentially carrying out discrete element simulation analysis and finite element simulation analysis for each working condition, and obtaining a finite element simulation result of each sub-model under the working condition; therefore, the fatigue damage value of each cutter and the cutter head body under the working condition can be calculated, and finally, the fatigue life of the cutter head is calculated by integrating the fatigue damage values of each cutter and the cutter head body under all working conditions, so that the method is more suitable for the actual construction condition of the cutter head, and the calculation accuracy of the fatigue life of the cutter head is greatly improved. Moreover, on-site construction parameters are utilized to verify the discrete element simulation calculation result, so that the accuracy of the discrete element simulation result is ensured, the finite element simulation analysis result is closer to the actual condition in the cutterhead construction process, and the calculation accuracy of fatigue damage and fatigue life is further improved.

The invention relates to the technical field of metallurgical equipment control, in particular to a material guide device and a control system, and the device comprises a material track planning module, an unpowered dust suppression module, a speed cooperative control module, a dynamic feedback regulation module and a genetic algorithm optimization module. The material trajectory planning module constructs a sliding trajectory model based on a discrete element simulation algorithm, and generates guiding curved surface geometric parameters and damper rigidity control instructions; the dynamic feedback regulation module collects a vibration spectrum through a high-frequency piezoelectric sensor array, and outputs a classification result through matching of a convolutional neural network and an abnormal vibration mode library; the genetic algorithm optimization module constructs a multi-objective fitness function to generate a global optimization parameter set, drives guide curved surface inclination angle adjustment, guide grid damping compensation and rotating speed control logic co-evolution, and forms a closed-loop parameter collaboration mechanism. According to the invention, systematic cooperation of material sliding track optimization, dust dynamic suppression and equipment vibration energy control is realized.

The invention relates to a novel discrete element method for simulating infiltration sand particle loss, which comprises the following steps: comparing particle stress with an average stress mean value of all particles in a sample, and when the particle stress # imgabs0 # is smaller than the average stress mean value # imgabs1 # of all the particles, determining that the particles belong to infiltration loss particles; carrying out loss priority ranking on all particles which can be subjected to infiltration loss; and a particle removal algorithm is constructed. The method has the beneficial effects that fine particles which can be subjected to permeation and erosion loss are judged and distinguished through pure discrete element simulation, whether permeation and erosion can be carried out or not is judged by comparing the particle stress size with a mean value, the particle loss is controlled in the fine particles, and errors caused by loss of a soil skeleton are avoided; and the lossable particles are sorted according to priorities to determine the sequence of the removed fine particles in each cycle, so that the loss of the permeable sand particles simulated by the discrete element method is more in line with the real situation.

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 thermal regeneration asphalt mixture functional reinforcement phase optimization design method based on discrete element simulation in the technical field of thermal regeneration road engineering material optimization, and the method comprises the steps: constructing a discrete element model of an asphalt mixture, extracting skeleton gap characteristic parameters, including the skeleton gap size, the volume ratio and the distribution condition, and obtaining a discrete element model of the asphalt mixture; according to the optimization design method, the mixing amount, the diameter range and the length parameters of the functional reinforcing material are optimized according to the characteristic parameters, the blindness of a traditional experiment depending on experience is avoided, and the design efficiency and performance of the functional asphalt mixture are improved. According to the method, the microstructure characteristics of the thermally recycled asphalt mixture are accurately quantified through the discrete element technology, the correlation model of the functional reinforced phase parameters and the skeleton gaps is established, the problems that a traditional test depends on experience and is low in efficiency are solved, and a high-performance material solution is provided for a functional asphalt pavement.

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 loader load process prediction method based on MBD-DEM coupling simulation data driving, and is applied to the technical field of loader load prediction. Comprising the following steps: establishing a multi-rigid-body dynamic model of a loading machine working device; constructing a discrete element model of the material particles by adopting discrete element simulation software; mBD-DEM coupling is realized based on the multi-rigid-body dynamic model and the discrete element model; different working condition combinations are set, and load history data are obtained; preprocessing the load history data and the corresponding working condition data; constructing and training an LSTM neural network; and predicting the loading process of the loader based on the trained LSTM neural network. According to the method, the mechanical kinematics and dynamics characteristics of the loading machine working device and the discrete characteristics of materials can be considered at the same time, and the load prediction accuracy is improved.

The invention provides a method for calibrating contact parameters of micron-sized spherical aluminum powder for an energetic material, and the basic principle of the method is that discrete element simulation and experiment are combined, errors of a simulation repose angle and an experiment repose angle are compared, different calibration sequences are adopted for different errors, and the contact parameters of micron-sized spherical aluminum powder for the energetic material are calibrated. And gradually completing the contact parameter calibration of the micron-sized spherical aluminum powder for the energetic material. According to the method, it is proposed for the first time that for micron-sized spherical aluminum powder, four contact parameters including the collision recovery coefficient, the sliding friction coefficient, the rolling friction coefficient and the adsorption energy among particles need to be calibrated, and the proper coarsening range of the aluminum powder during modeling, the calibration sequence under different conditions and the contact parameter calibration range are defined for the first time; according to the method, the contact parameter calibration workload of the micron-sized spherical aluminum powder is reduced, and efficient preparation of the energetic material is promoted.

The invention discloses a simulation analysis method of flexible particles based on a bonding combination hyper-ellipsoid model. The simulation analysis method comprises the following steps: determining shapes, sizes, positions, numbers and material attributes of first-level sub-particles forming the flexible particles according to real flexible particles; determining the shape, the size, the position and the number of second-level sub-particles forming each first-level sub-particle, and constructing the first-level sub-particles through a hyper-ellipsoid model; according to the determined position and material attribute of the first-level sub-particle, setting a bonding bond model for the contact model of the material attribute of the adjacent first-level sub-particle; corresponding coordinate positions, rotation angles and material attributes are given to the first-level sub-particles, and the first-level sub-particles are combined to form a bonding combination hyper-ellipsoid model of the flexible particles; and applying the bonded combined hyper-ellipsoid model to a flexible particle system in a discrete unit analogue simulation industrial process to obtain a simulation result. According to the simulation method, accurate modeling of the flexible particles is achieved, the motion law of the flexible particles is obtained, and the authenticity of the simulation result of the flexible particle system is improved.

The invention discloses a wall targeting structure design method and system for a thin-wall ring piece vibration polishing and grinding device, and relates to the technical field of surface barreling finishing machining, and the method comprises the steps that container parameters of the thin-wall ring piece vibration polishing and grinding device are initialized; an EDEM simulation model is constructed, discrete element analog simulation is conducted on the initialized container parameters, and a simulation result is obtained; establishing a targeting capability index based on the particle medium energy state density; obtaining an optimal container motion parameter based on the dynamic behavior and the targeting ability index of the particle medium energy state density; taking the targeting capability index as a response value to obtain the sensitivity of the container wall targeting structure, and obtaining a design variable based on a sensitivity threshold value; and fitting a target function based on a targeting capability index by taking the design variable as a constraint condition, solving the design variable to obtain an optimal design parameter of the container wall targeting structure, and finishing the design of the thin-wall ring vibration polishing and grinding device wall targeting structure in combination with the optimal container motion parameter.

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 method for quickly generating a railway ballast numerical file based on machine vision and geometric feature matching, which comprises the following steps of: scanning a certain number of railway ballast particles, establishing a three-dimensional model library, extracting projection contour geometric features of a plurality of surfaces of each railway ballast model, and establishing a two-dimensional information table of the railway ballast models; meanwhile, instance segmentation is carried out on the ballast aggregate real-shot picture through a machine vision model, binarization processing is carried out, geometric features of the outline of each ballast particle in a binary picture are extracted, and a ballast particle two-dimensional information table is established; and matching the geometric features of the railway ballast particles in the real shot with the geometric features of each railway ballast model in the three-dimensional model library, comparing the similarity between the particle projection features and the model projection features, and selecting or calculating to generate a numerical file of the railway ballast particles. The matching method is easy to operate, clear in principle, high in matching speed and more accurate, provides a more real and reliable simulation material for discrete element simulation, and has good practical value and popularization prospects.

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 invention relates to the technical field of geotechnical engineering and geological disaster prevention, in particular to a method for testing and predicting a retaining wall of a soil-rock mixture high landfill. The method comprises the following steps: S1, sample preparation: determining soil materials with different maximum particle sizes by adopting a rejection method, a similar gradation method and an equivalent substitution method according to a field soil material gradation curve of the soil-rock mixture; s2, large-scale direct shear test: performing direct shear test on each group of samples prepared in the step 1 by using a direct shear apparatus, and determining the shear strength of each sample. According to the method, the stability of the soil-rock mixture high landfill site can be improved, indoor tests, physical models, discrete element simulation and theoretical analysis are integrated, the stability influence main cause and the instability mechanism are accurately recognized, effective prevention and treatment measures are provided, a scientific basis can be provided for safety risk assessment and site selection optimization of the stockpiling site, and the method is suitable for popularization and application. And the land reclamation technology is combined, engineering safety and environment harmony are ensured, and remarkable social and economic benefits are achieved.

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.