Multi-process coupling based particle packing simulation method and medium
By employing a multi-process coupled particle loading simulation method, the problems of uneven particle distribution and structural instability during the loading of mixed ball beds were solved. This method enables systematic modeling and simulation of the particle loading process, improves the uniformity and stability of loading, and provides an effective means of process optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 聚变新能(安徽)有限公司
- Filing Date
- 2026-04-29
- Publication Date
- 2026-05-29
AI Technical Summary
In the solid breeder blanket of fusion reactors, there are problems such as uneven particle distribution, uneven pore structure and local loose areas during the filling process of the mixed sphere bed. Existing technologies are difficult to achieve systematic analysis and effective control of the particle filling process, especially under vibration compaction conditions, the coupling relationship between structural response and particle behavior has not been fully considered.
A particle filling simulation method based on multi-process coupling is adopted. By constructing a discrete element model of particles and a dynamic model of filling equipment, the coupling relationship is established, filling units are divided and cyclic simulation is performed to optimize filling process parameters. Combined with evaluation indicators, analysis is carried out to achieve system modeling and simulation of the filling process.
It improves the uniformity of particle distribution and the density of filling, enhances the stability of the filling structure, provides an optimization analysis method for the filling process of complex particle systems, and improves the controllability of the filling process and the consistency of results.
Smart Images
Figure CN122113548A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of fusion reactor solid breeder blanket hybrid sphere bed filling technology, and in particular to a particle filling simulation method and medium based on multi-process coupling. Background Technology
[0002] In the solid-state breeder blanket of fusion reactors, a mixed pellet bed composed of different particle sizes and materials is typically used as the functional filling medium. The breeder material and multiplicative material particles need to be stably packed within a confined space, simultaneously meeting requirements for volume distribution, spatial distribution, overall filling rate, and local compaction. However, the filling process of the mixed pellet bed is not a simple particle filling process, but a complex dynamic process influenced by various factors such as particle size differences, density differences, contact characteristics, filling space boundary constraints, material distribution methods, and external vibration conditions. During the filling process, particles are prone to sieving effects, stratification, and local enrichment during falling, colliding, and accumulating. This leads to problems such as uneven component distribution, uneven pore structure, and localized loose areas within the pellet bed after filling, which in turn affect the heat and mass transfer performance and structural stability of the pellet bed.
[0003] In relevant engineering applications, particle filling is typically achieved through a combination of processes such as premixing, spreading, leveling, and vibration compaction. However, the relevant process parameters often rely on empirical settings, lacking systematic analytical methods for the evolution of particle behavior. In practical applications, different process parameters often have coupled effects. For example, the mixing process influences the subsequent spreading state, and the vibration compaction process readjusts the particle distribution. These factors work together to make the filling results highly uncertain. Furthermore, when there are significant particle size differences in the particle system, particle grading and segregation are more likely to occur during filling, further increasing the difficulty of filling quality control.
[0004] Furthermore, while some related technologies have introduced discrete element methods or multiphysics coupling analysis to study particle motion, they mostly focus on single processes or specific equipment operating states, lacking a continuous description and unified modeling of the entire filling process, and failing to reflect the state transfer relationships between different processes. In complex filling structures, the interaction between particle motion behavior and the filling equipment and the structure itself also lacks a unified analytical framework, especially under conditions such as vibration compaction, where the coupling relationship between structural response and particle behavior has not been fully considered. Therefore, in the process of filling mixed ball beds, related technologies still struggle to effectively coordinate and control the process parameters throughout the entire process while ensuring mixing uniformity, filling rate, and structural stability. Summary of the Invention
[0005] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the purpose of this invention is to propose a particle filling simulation method and medium based on multi-process coupling, so as to achieve process parameter optimization and filling effect prediction under the synergistic effect of multiple process stages during particle filling.
[0006] To achieve the above objectives, a first aspect of the present invention proposes a particle loading simulation method based on multi-process coupling, comprising: Based on the particle system parameters and the geometric boundary parameters of the filling equipment, a discrete element model of the particles and a dynamic model of the filling equipment are constructed respectively, and a coupling relationship is established between the discrete element model of the particles and the dynamic model. The filling space is divided into several filling units, and each filling unit is provided with a corresponding filling process stage; the filling process stage includes at least a roller mixing treatment stage, a discharge fabric spreading stage, and a vibration compaction stage. According to the divided loading units, cyclic loading simulation is executed sequentially based on the coupling relationship; wherein, within the same loading unit, the particle state of the previous loading process stage is used as the initial state of the next loading process stage; within two loading units with adjacent execution order, the particle state of the previous loading unit after the completion of the loading process stage is used as the initial state of the next loading unit. Based on the cyclic filling simulation process, an evaluation index is constructed to comprehensively characterize the spatial distribution state of particles and the response characteristics of the filling structure. Based on the evaluation index, the parameter combination of the filling process is optimized and analyzed to determine the range of filling process parameters corresponding to the filling space.
[0007] In addition, the method of the above embodiments of the present invention may also have the following additional technical features: According to an embodiment of the present invention, constructing the granular discrete element model includes: Based on the material properties, particle size characteristics, and proportions of different types of particles, a mixed particle discrete element model containing at least two types of particles is established, and a contact relationship model between particles and between particles and the boundary of the filling equipment is constructed. The particle discrete element model is based on particle system parameters and contact mechanical parameters to dynamically describe the translational and rotational behavior of particles under force.
[0008] According to one embodiment of the present invention, constructing a dynamic model of a loading device includes: Based on the structural parameters, motion patterns, and boundary constraints of the filling equipment, a dynamic model is constructed that includes a drum mixing device, a discharge device, a spreading device, a leveling device, and a vibration device. The dynamic model is used to describe the motion process of the filling equipment under driving conditions and the dynamic boundary interaction relationship with the particle system, and to characterize the dynamic response characteristics of the equipment structure during the filling process.
[0009] According to one embodiment of the present invention, establishing the coupling relationship includes: During the simulation, the mechanical interaction information between the particle discrete element model and the dynamic model is updated synchronously based on the time step. The particle force output by the particle discrete element model is loaded into the dynamic model, and the motion response output by the dynamic model is reacted back onto the particle discrete element model.
[0010] According to one embodiment of the present invention, during the drum mixing stage, the sequential execution of cyclic loading simulation based on the coupling relationship includes: Under the coupling condition of the particle discrete element model and the dynamic model, the motion boundary of the drum mixing device is used as the dynamic constraint of particle motion, and the contact action of particles on the inner wall of the drum and components is fed back to the dynamic model. Based on the motion input parameters of the dynamic model, the tumbling, dropping and redistribution process of particles in the drum is simulated and analyzed to obtain the premixed distribution state of the particles.
[0011] According to an embodiment of the present invention, during the material spreading stage, the sequential execution of cyclic loading simulation based on the coupling relationship includes: Using the particle state obtained in the mixing stage as the initial condition, the process of particles being released from the discharge device, dispersed by the distribution device, and entering the filling unit to form the initial accumulation layer is simulated within the coupled framework of the particle discrete element model and the dynamic model. The motion of the leveling device is solved based on the dynamic model, and the solution is used as the dynamic boundary condition for particle motion to level the particle accumulation surface and obtain the spatial distribution state of particles in the filling unit.
[0012] According to one embodiment of the present invention, during the compaction stage, the sequential execution of cyclic loading simulation based on the coupling relationship includes: Based on the current particle packing state of the filling unit, and under the coupling relationship between the particle discrete element model and the dynamic model, a vibration compaction condition is introduced, and the coupling conditions between the vibration device, the filling boundary, and the particle system are defined. Based on the aforementioned coupling conditions, the particle filling, rearrangement, and contact network evolution processes under vibration are simulated and analyzed by changing vibration parameters. During the simulation, the dynamic response of the filling boundary is calculated synchronously, and the vibration parameters are synchronously constrained based on the dynamic response.
[0013] According to one embodiment of the present invention, the optimization analysis of the parameter combination of the filling process includes: The motion input parameters of the dynamic model are selected as variables to be optimized, and the evaluation index results under different parameter combinations are calculated based on the cyclic loading simulation process. The parameter combinations are comprehensively evaluated based on the evaluation indicators to screen parameter combinations that meet the preset constraints and determine the range of filling process parameters.
[0014] According to one embodiment of the present invention, the method further includes: Actual filling experiments were conducted based on the filling process parameter range to obtain the distribution state and structural response data of the particles after filling, and the results were compared with those of the cyclic filling simulation. If the deviation of the comparison results exceeds the preset threshold, the parameters in the particle discrete element model or the kinetic model are corrected, and the cyclic filling simulation and parameter optimization analysis process is repeated until the simulation results and actual results meet the consistency requirements. Based on the results that meet the consistency requirements, the final filling process parameter range is determined.
[0015] To achieve the above objectives, a second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the above-described particle loading simulation method based on multi-process coupling.
[0016] The particle filling simulation method and medium based on multi-process coupling of this invention can describe the continuous evolution of different process stages in particle filling within a unified coupled analysis framework. Through filling unit division and state transfer mechanisms, it achieves system modeling and simulation analysis of the entire filling process. Simultaneously, by constructing an evaluation index that comprehensively characterizes the spatial distribution state of particles and the response characteristics of the filling structure, and by optimizing parameter combinations based on this evaluation index, it helps improve the controllability and consistency of the filling process. This, to a certain extent, improves particle distribution uniformity, increases filling density, and enhances the stability of the filling structure, providing an effective analytical means for optimizing the filling process of complex particle systems. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating a particle loading simulation method based on multi-process coupling in one embodiment. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0019] The implementation details of the technical solutions of the embodiments of the present invention are described in detail below.
[0020] In one embodiment, such as Figure 1 The diagram illustrates a process flow chart for a particle loading simulation method based on multi-process coupling. This method may include the following steps: Step S101: Based on the particle system parameters and the geometric boundary parameters of the filling equipment, construct the particle discrete element model and the dynamic model of the filling equipment respectively, and establish a coupling relationship between the particle discrete element model and the dynamic model.
[0021] The filling object is a mixed particle system composed of multiple types of particles and the filling space therein. The filling equipment includes devices for mixing, conveying, spreading, leveling and vibrating to compact the particles, as well as container boundary structures for defining the particle filling range.
[0022] Based on a clear understanding of the filling object and the composition of the filling equipment, the filling object and the filling process are discretized and modeled according to the particle system parameters and the geometric boundary parameters of the filling equipment. This transforms the actual filling process into a computable numerical analysis problem. The particle system parameters are used to characterize the basic physical properties and motion response characteristics of different types of particles, while the geometric boundary parameters of the filling equipment are used to characterize the filling space and the boundary constraints of the equipment.
[0023] Based on the above parameters, a discrete element model of particles and a dynamic model of the filling equipment are constructed respectively. The discrete element model of particles is used to characterize the motion evolution behavior of particles under the action of gravity and mutual contact, while the dynamic model of the filling equipment is used to characterize the motion response characteristics of the equipment and boundary structure under external loads during the filling process.
[0024] A coupling relationship is established between the particle discrete element model and the dynamic model to realize the correlation analysis between particle motion behavior and the motion state of the filling equipment. This enables the particle motion process to respond to changes in the boundary of the filling equipment, and at the same time enables the motion analysis of the filling equipment to reflect the particle effect, thus forming a foundation for collaborative simulation analysis of the entire filling process.
[0025] In one embodiment, the process of establishing the coupling relationship includes: during the simulation calculation, solving the particle discrete element model and the dynamic model synchronously with a unified time step, and transmitting mechanical interaction information bidirectionally within each time step.
[0026] Specifically, within each time step, based on the calculation results of the particle discrete element model, the contact force between the particle and the boundary of the loading equipment is obtained, and this force is applied as an external load to the dynamic model to drive the dynamic response calculation of the loading equipment and the boundary structure. Simultaneously, based on the solution results of the dynamic model within that time step, the displacement, velocity, and acceleration of the loading equipment are obtained, and this information is fed back to the particle discrete element model as boundary motion conditions to update the particle's motion boundary constraints.
[0027] In the above process, the information exchange frequency between the particle system and the filling equipment is controlled by the time step, so that the stress state of the particles and the motion state of the equipment evolve synchronously in time, thereby realizing the dynamic coupling calculation between particle motion behavior and structural dynamic response.
[0028] The above methods enable the transfer of particle forces to structural responses and the reverse constraint of structural motion on particle motion, providing a basic computational mechanism for coupled simulation in subsequent filling process stages.
[0029] In one embodiment, the process of constructing a particle discrete element model includes: establishing a mixed particle discrete element model based on the material properties, particle size characteristics, and proportions of different types of particles. The mixed particles include at least a first type of particle and a second type of particle, which correspond to particle types with different materials or particle size characteristics, respectively, and are used to characterize the actual composition and structure of the multi-component particle system in the mixed particle bed. In practical applications, the first type of particle can be lithium titanium oxide multiplier spheres, and the second type of particle can be beryllium titanium multiplier spheres, or they can be large-diameter particles and small-diameter particles, respectively.
[0030] In the modeling process, different types of particles are first discretized based on the parameters of the particle system, transforming the continuous particle system into a computational set composed of multiple independent particle units, and establishing corresponding first-type particle models and second-type particle models respectively. Each particle model is used to describe the mass attributes, geometric features and motion state of the corresponding type of particle.
[0031] In the modeling process, the particle discrete element model includes a first-type particle model, a second-type particle model, and contact relationship models between particles and between particles and the boundaries of the filling equipment. The first-type particle model characterizes the spatial distribution and motion behavior of first-type particles, the second-type particle model characterizes the corresponding behavior of second-type particles, and the contact relationship models uniformly describe the collision, friction, and energy transfer processes between different types of particles and between particles and the equipment walls and the boundaries of the filling space. The contact relationship model between particles describes the collision, friction, and energy transfer behavior of different particles during the contact process, while the contact relationship model between particles and the boundaries of the filling equipment describes the interaction processes between particles and the equipment walls and the boundaries of the filling space.
[0032] The input parameters of the particle discrete element model include particle system parameters and contact mechanical parameters. The particle system parameters include particle density, equivalent particle size, and particle size distribution, which are used to characterize the mass properties and spatial scale characteristics of the particles. The contact mechanical parameters include the coefficient of restitution, static friction coefficient, rolling friction coefficient, and contact parameters between the particles and the wall, which are used to characterize the energy dissipation and friction characteristics of the particles during the contact process.
[0033] In the discrete element method (DEM) calculation, the motion of each particle satisfies the dynamic equilibrium relationship. The translation and rotation of the particles satisfy the following relationships:
[0034]
[0035] in, Indicates the first The mass of each particle is expressed in kilograms. Indicates the first The position vector of each particle, in meters; Indicates the action on the first The resultant force on each particle, measured in Newtons; This represents the acceleration due to gravity, measured in meters per second squared. Indicates the first The moment of inertia of a single particle, expressed in kilograms per square meter. Indicates the first The angular velocity of each particle, in radians per second; Indicates the action on the first The resultant torque on each particle, measured in Newton-meters; Indicates time, in seconds.
[0036] The above translational and rotational relationships are used to update the motion state of particles step by step during numerical calculations. By solving for the net force and net torque on the particles, the dynamic evolution calculation of particle displacement, velocity and rotation state is realized, thus providing a basis for analyzing the packing morphology, distribution characteristics and contact network changes of particle systems.
[0037] The above methods enable numerical simulation of the motion state, contact behavior, and spatial distribution evolution of a mixed particle system under the influence of gravity and contact.
[0038] In one embodiment, the process of constructing a dynamic model of the filling equipment includes: modeling the drum mixing device, the discharge device, the spreading device, the leveling device, the vibration device, and the boundary of the filling container according to the structural parameters, motion form, and boundary constraints of the filling equipment, thereby constructing a dynamic model of the filling equipment and boundary structure.
[0039] In the specific modeling process, the structural composition and motion characteristics of each device are integrated into the dynamic description. Specifically, the dynamic model of the drum mixing device not only reflects the overall rotation of the drum but also considers the guiding effect of internal flow-guiding components on the particle movement path and the rotational boundary conditions applied to the drum by the drive end. The dynamic model of the feeding device, combining the geometry of the discharge port and the spatial arrangement of the guiding components, describes the particle release path and its motion boundaries. The dynamic model of the leveling device reflects the particle surface reshaping process by depicting the motion trajectory of the leveling components and their contact relationship with the particles. The dynamic model of the vibration device includes at least the excitation components, support components, and the constraint relationships between them and the object being filled.
[0040] Based on the above modeling results, the established dynamic model is used to describe the transient motion process, contact response process and structural dynamic response process of each device under driving conditions, including the motion boundary formed in the drum mixing process, the dynamic contact boundary formed in the material spreading process in the discharge spreading and flattening process, the contact disturbance boundary formed in the flattening process, and the acceleration input boundary formed in the vibration compaction process.
[0041] Based on the above description of continuous evolution and dynamics, in order to achieve a unified solution for various boundary responses, structural dynamic equilibrium relationships are used to characterize the dynamic behavior of the device and boundary structures, so that the responses of different devices under external excitation can be expressed in a unified mathematical form:
[0042] in, This is a mass matrix, with units in kilograms; This is the damping matrix, with units of Newton-seconds per meter; This is the stiffness matrix, with units of Newtons per meter. This is a displacement vector, in meters; This is a velocity vector, with units of meters per second. This is the acceleration vector, with units of meters per second squared. This is the external load vector, in Newtons (N).
[0043] The above-mentioned structural dynamic equilibrium relationship is used to describe the dynamic response behavior of the filling equipment and boundary structure under external excitation. By solving this equation, the displacement, velocity and acceleration responses of each device at different times can be obtained, thereby determining the motion boundary conditions and load input forms acting on the particle system during the filling process.
[0044] The above method enables a unified description of the dynamic response behavior of various devices and filling boundaries under driving action during the filling process, and provides time-varying boundary constraints for particle movement.
[0045] Step S102: Divide the filling space into several filling units and set corresponding filling process stages for each filling unit.
[0046] The filling space is a spatial area used to accommodate the mixed particle system and complete the filling operation. It is jointly defined by the filling container body, internal functional components, and the boundary of the filling area. Based on the structural composition of this filling space, the filling object is geometrically modeled according to the structure of the filling container body, the distribution of internal functional components, and the boundary of the filling area. After deducting cooling components, detection components, reinforcing components, and non-filling areas, the effective filling space of the mixed particle system is determined.
[0047] Based on this, the effective filling space is divided along the filling direction to form several filling units. Each filling unit corresponds to an independent filling cycle process, which is used to limit the analysis range of single mixing, spreading, leveling and vibration compaction, thereby decomposing the overall filling process into multiple repeatable local processes.
[0048] Each filling unit is equipped with a corresponding filling process stage, which includes at least a roller mixing stage, a discharge and spreading stage, and a vibration compaction stage, so that the particles undergo mixing, conveying and stacking, and compaction processes in each filling unit in sequence.
[0049] Within the filling unit, the filling state of the particles is characterized by the filling rate, which is expressed as:
[0050] in, The fill rate is expressed as a percentage. This refers to the total volume of particles, in cubic meters. The effective filling volume of the filling unit is expressed in cubic meters.
[0051] By dividing the process into units and setting up process stages as described above, the filling process can be decomposed and reconstructed in terms of space and process, which facilitates independent analysis and overall optimization of parameters at each stage.
[0052] Step S103: Perform cyclic loading simulation sequentially according to the divided loading units based on the coupling relationship.
[0053] After completing the division of filling units and setting the filling process stages, the entire filling process is simulated in a unit-by-unit cyclical manner based on the coupling relationship between the established particle discrete element model and the dynamic model.
[0054] Specifically, within the same filling unit, the filling process stages such as roller mixing, discharge spreading and vibration compaction are executed sequentially according to a preset order, and the particle distribution state at the end of the previous filling process stage is used as the initial state of the next filling process stage, thereby achieving continuous connection between different process stages.
[0055] After completing the coupled simulation of a filling unit, the particle distribution state of the filling unit after vibration compaction is used as the initial boundary condition for the next filling unit. The coupled analysis process of roller mixing, material discharge, surface leveling and vibration compaction is repeated in the next filling unit until all filling units are completed.
[0056] In this process, the particle surface morphology, local compaction state and boundary response characteristics formed by the previous filling unit will serve as the initial conditions for the subsequent filling unit, affecting its material distribution process and vibration compaction process, thus forming a state transfer and response coupling relationship between different filling units.
[0057] The above method enables the progressive advancement of the filling process under spatial unit conditions and the continuous transmission of parameters and states, thereby reflecting the cumulative effect of particle distribution evolution and structural response during the overall filling process.
[0058] In one embodiment, during the coupled simulation process of the drum mixing stage, the established discrete element model of the mixed particles is coupled with the established dynamic model of the drum mixing device, and the motion boundary of the drum mixing device is used as the dynamic constraint condition of the particle motion. The dynamic response results of the drum body, the guide component and its motion boundary are used as the real-time boundary conditions of the particle motion. At the same time, the contact action of the particles on the inner wall of the drum and the internal components is fed back to the dynamic model, thereby forming a two-way coupling relationship between the particle motion and the equipment boundary.
[0059] Under this coupling condition, based on the motion input parameters set in the dynamic model, including drum speed, mixing time, loading rate, drum structure, and internal component arrangement, the motion process of the drum under different working conditions is driven. The tumbling, dropping, collision, exchange, and redistribution behavior of particles in the drum are simulated and analyzed, so that the particles achieve spatial rearrangement under the combined action of gravity and drum motion, thereby obtaining the evolution law of particle motion behavior under different process parameter combinations and its influence on the mixing effect.
[0060] During the simulation, the mixing uniformity is quantitatively characterized by statistically analyzing the spatial distribution of particles. The mixing uniformity is represented by the Lacey mixing index, which is expressed as follows:
[0061] in, The Lacey Mixed Index; This represents the variance of component concentrations under actual sampling conditions. The variance of component concentrations under completely separated conditions; The variance of component concentrations under ideal random mixing conditions.
[0062] By calculating the mixing uniformity, the changes in the degree of particle mixing under different process parameters can be evaluated, thereby obtaining the particle premixing distribution state at the end of the drum mixing process.
[0063] The above methods not only allow us to obtain the characteristics of particle tumbling, falling and redistribution under different drum conditions, but also reveal the influence mechanism of process parameters on particle mixing uniformity. This transforms the drum mixing process from an empirical operation into a coupled simulation process based on the interaction of particle motion behavior and equipment dynamic boundaries, providing more reasonable initial particle distribution conditions for subsequent material spreading and compaction stages.
[0064] In one embodiment, during the coupled simulation of the material feeding and spreading stage, the particle distribution state obtained in the roller mixing stage is used as the initial condition. Under the coupled framework of the particle discrete element model and the dynamic model, the process of mixed particles being released by the discharge device, dispersed by the spreading device, and entering the filling unit to form the initial accumulation layer is simulated.
[0065] In this process, the motion of the discharge device and the feeding device is solved by the dynamic model. Their motion trajectory and boundary shape serve as dynamic constraints on the particle motion. Under the action of gravity and the action of the components, the particles fall, diffuse and adjust their spatial distribution, thereby forming an initial stacking structure in the filling unit.
[0066] Subsequently, the motion of the leveling device was solved based on a dynamic model, and the results were used as dynamic boundary conditions for particle motion to level the particle accumulation surface. During this process, the particles slip, diffuse, and rearrange locally under the action of the leveling device, gradually forming a relatively flat accumulation surface.
[0067] Meanwhile, the contact interaction and mechanical response of the particles during the fabric spreading and flattening process are solved by the discrete element model, thereby obtaining the actual distribution state of the particles in the cross-sectional and height directions.
[0068] By using the above method, continuous coupled simulation of the material discharge, distribution and spreading processes is achieved, so that the particles form a spatial distribution structure with a certain uniformity and stability in the filling unit, providing initial conditions for the subsequent vibration compaction stage.
[0069] In one embodiment, during the coupled simulation of the compaction stage, based on the completed material distribution and flattened particle stacking state, the particle system within the current filling unit is imported into the vibration compaction condition. Under the coupling relationship between the particle discrete element model and the dynamic model, the coupling conditions between the vibration device, the filling boundary, and the particle bed are established. This allows the excitation input of the vibration device, the structural transmission path, and the boundary support constraints to participate in the particle force calculation simultaneously within the same time step, thereby achieving effective transmission of vibration input to the particle system and feedback description of the particle reaction on the structural response. In this process, by defining the excitation form, loading position, and boundary constraint state of the vibration device in the dynamic model, and mapping the corresponding displacement, velocity, or acceleration response to the boundary elements in the particle discrete element model, the coupling relationship between particles and structure is constructed.
[0070] Under this coupling condition, the excitation mode of the vibration device, the vibration transmission path, and the support state of the filling boundary are solved by the dynamic model and used as the dynamic boundary input for particle motion. The gap filling, rearrangement, contact network changes, and possible re-grading behaviors of particles under vibration are solved by the discrete element model.
[0071] By changing vibration parameters (such as vibration frequency, vibration amplitude, vibration time, and vibration direction), the particle compaction process is iteratively simulated and analyzed to obtain the evolution characteristics of particle compaction behavior under different vibration conditions. During the simulation, the dynamic response of the filling boundary is calculated simultaneously, and the vibration parameters are constrained based on the dynamic response to ensure that the vibration input can meet the particle compaction requirements while preventing the structural response from exceeding the allowable range.
[0072] For periodic vibrations, the peak acceleration satisfies the following relationship:
[0073] in, The peak ground acceleration is expressed in meters per second squared. The vibration frequency is expressed in Hertz (Hz). The amplitude of the vibration is expressed in meters. Pi is used as the mathematical constant. The above relationship is used to convert the vibration frequency and amplitude parameters into equivalent acceleration indicators, thereby providing a unified quantitative constraint on vibration intensity during parameter optimization and evaluating its comprehensive impact on particle compaction and filling boundary response.
[0074] By using the above method, we can achieve a synergistic analysis of particle behavior and structural response during vibration compaction, thereby obtaining a combination of vibration parameters that satisfies both the compaction effect and structural constraints.
[0075] Step S104: Based on the cyclic filling simulation process, construct an evaluation index to comprehensively characterize the spatial distribution state of particles and the response characteristics of the filling structure, and optimize the parameter combination of the filling process based on the evaluation index to determine the range of filling process parameters corresponding to the filling space.
[0076] In this process, statistical analysis was performed on the particle spatial distribution, contact behavior, and filling boundary response results obtained from each filling unit and process stage in the cyclic filling simulation. Characteristic parameters reflecting particle mixing uniformity, spatial distribution characteristics, and structural response level were extracted, and a multi-dimensional evaluation index system was constructed based on these characteristic parameters. The evaluation index system uniformly characterizes particle distribution and structural response characteristics, covering not only indicators reflecting particle behavior such as particle mixing uniformity, particle segregation degree, filling rate, local compaction consistency, surface smoothness, and particle collision intensity, but also peak response of filling equipment or structure and vibration acceleration constraint satisfaction, thereby achieving a synergistic characterization of filling quality and structural safety.
[0077] This allows simulation results under different process parameters to be compared and analyzed under a unified evaluation standard. During the evaluation process, simulation results corresponding to different combinations of filling process parameters are mapped to the evaluation index system, thereby forming a correlation between multiple parameters and multiple indicators, providing a quantitative basis for subsequent parameter selection.
[0078] Based on this, by analyzing the evaluation index results corresponding to different parameter combinations, the parameter value range that meets the filling performance requirements and structural constraints is determined. Then, among multiple parameter combinations that meet the constraints, the parameter range with the better comprehensive evaluation results is selected, thereby forming a filling process parameter range and corresponding parameter window description applicable to the target filling space. The parameter window includes the range of roller mixing parameters, the range of fabric parameters, the range of leveling operation parameters, and the range of local vibration compaction parameters. It also includes full-process response constraints to guide the process implementation and control of the actual filling process.
[0079] In one embodiment, the process of optimizing the parameter combination of the filling process includes: selecting the motion input parameters of the dynamic model as variables to be optimized, including drum speed, mixing time, filling rate, material distribution method, flattening method, vibration frequency, vibration amplitude, vibration time and filling boundary related parameters, and calculating the evaluation index results under different parameter combinations based on the cyclic filling simulation process.
[0080] During the calculation process, for each set of parameters, the corresponding evaluation results such as particle mixing uniformity, particle segregation degree, filling rate, local compaction consistency, surface smoothness, particle collision intensity, and filling boundary structure response are obtained, thus forming a multi-index evaluation data set.
[0081] Based on the evaluation indicators, a comprehensive evaluation of each parameter combination is conducted. Under the premise of meeting constraints such as vibration acceleration constraints and structural response limitations, the parameter combination with better overall performance is selected, and the range of filling process parameters is determined accordingly.
[0082] Meanwhile, by comparing the analysis results of multiple parameter combinations, the influence of different process parameters on the filling effect can be obtained, thus providing a basis for the selection of filling process parameters. In the process of solving and screening parameter combinations, various optimization analysis methods can be combined to traverse or approximate the parameter space, including scanning analysis based on discrete parameter combinations, representative experimental design methods, and function approximation or optimization strategies oriented towards multivariate coupling relationships, to improve parameter screening efficiency and enhance the stability and interpretability of the results.
[0083] In one embodiment, an actual filling experiment is conducted based on the filling process parameter range to obtain data on the mixing uniformity, filling state, surface smoothness, local compaction consistency, and structural response after particle filling. The experimental results are then compared and analyzed with the results of cyclic filling simulation.
[0084] During the comparison process, the degree of deviation between the simulation results and the actual results is determined by analyzing the differences between the spatial distribution characteristics of particles and the response characteristics of the filling structure.
[0085] When the deviation of the comparison results exceeds the preset threshold, the contact parameters, particle property parameters in the particle discrete element model or the boundary parameters and coupling conditions in the dynamic model are corrected, and the cyclic loading simulation and parameter optimization analysis process is re-executed based on the corrected parameters.
[0086] During the correction process, the simulation results can be verified by combining the flow characteristics of the packed particle bed. The rationality of the particle packing structure can be verified by introducing the fixed bed pressure drop relationship, so that the flow resistance characteristics can correspond to the spatial distribution state of the particles. The change of pressure drop along the flow path can be expressed as:
[0087] in, Pressure drop, measured in Pascals, reflects the resistance loss of fluid passing through a particle bed; The length of the flow path is in meters. Porosity; This is the fluid dynamic viscosity, measured in Pascals per second. Apparent flow velocity, measured in meters per second; The equivalent particle diameter is expressed in meters. The fluid density is expressed in kilograms per cubic meter. This relationship establishes a connection between particle packing structure parameters and macroscopic flow performance, thus aiding in determining whether the particle bed structure meets engineering application requirements.
[0088] Through the above iterative correction process, the simulation results are gradually made closer to the actual filling results until the two meet the consistency requirements. Based on the analysis results that meet the consistency requirements, the final filling process parameter range is determined.
[0089] To provide a more specific and intuitive explanation of the particle filling simulation method based on multi-process coupling in this application, a typical application example is given below in conjunction with the analysis scenario of a single filling unit, to demonstrate the correspondence between the selection of filling process parameters and the coupled simulation process.
[0090] In the first application example, a single filling unit is taken as the analysis object. By performing continuous coupling analysis on the filling process stages, the preliminary feasible filling process parameter range within the scope of a single filling unit is obtained.
[0091] First, the actual filling area is determined based on the three-dimensional structural model of the target filling object. After deducting the cooling components, detection components, reinforcing components and non-filling space, the effective filling space is obtained. Then, a single filling unit is divided along the filling direction so that the filling unit corresponds to a complete metering, mixing, spreading, leveling and vibration compaction process.
[0092] Based on this, a discrete element model (DEM) of the particles is established according to the designed mix proportions. The first and second types of particles correspond to different material or particle size characteristics; for example, the first type could be lithium titanium oxide multiplier spheres, and the second type could be beryllium titanium multiplier spheres. The input parameters of the particle DEM include particle density, equivalent particle size, particle size distribution, coefficient of restitution, static friction coefficient, rolling friction coefficient, and contact parameters between the particles and the wall surface. The particle size distribution and density are derived from actual test data, while the contact parameters are determined through experimental calibration or database analysis. For parameters that cannot be directly measured, a consistency calibration method reflecting the angle of repose, rolling behavior, and collision behavior can be used to determine them.
[0093] Meanwhile, dynamic models of the drum mixing device, discharge device, feeding device, leveling device, vibration device, and filling boundary structure are established. Each model is used to characterize the motion boundary, contact response, and structural dynamic response of the corresponding device under different working conditions.
[0094] Subsequently, within the equipment's capacity and structural constraints, multiple sets of candidate parameters were set, including drum speed, mixing time, loading rate, material distribution method, leveling speed, and vibration parameters. Each set of candidate parameters covered different process intensity levels, enabling it to reflect the evolution of particle mixing uniformity, filling state, and structural response under varying process conditions from low to high, thus providing a sufficient basis for comparison in subsequent screening.
[0095] In the simulation analysis of the drum mixing stage, the discrete element model of the mixed particles is coupled with the drum dynamics model to obtain the tumbling, dropping, exchange, collision, and redistribution processes of particles within the drum under different parameter sets. Based on this process, the spatial distribution of particles is sampled in zones, and the mixing uniformity index is calculated. When the mixing index of two consecutive samples under the same parameter set reaches a stable state and the fluctuation range is controlled (e.g., the mixing uniformity index is not lower than 0.90 and the difference between the two samples is not greater than 0.02), it is determined that the parameter set has formed a stable mixture in the drum stage; if the component distribution in a local area deviates significantly, it is determined that segregation exists and the parameter set is removed.
[0096] After the screening in the drum mixing stage, the particle distribution state under the corresponding parameter group is used as the initial condition for discharge. A continuous coupled analysis of discharge, distribution, and surface leveling is then performed to obtain the spatial distribution characteristics of particles in the cross-sectional and height directions within the filling unit. Surface smoothness, localized accumulation areas, and the filling state of corner areas are evaluated. When the relative deviation of the component volume fraction in each region within the filling unit is no greater than 5%, and the surface height after leveling meets the design uniform layer height deviation, the parameter group is judged to have passed the distribution and leveling stage screening. When there is obvious unilateral enrichment, concentrated corner voids, or excessive surface layer height, the parameter group is judged to have failed the screening.
[0097] Furthermore, for the parameter groups that passed the above screening, a vibration compaction condition was introduced while maintaining the current particle packing state. By changing the vibration frequency, amplitude, duration, and direction, the particle gap filling, contact network reconstruction, and local rearrangement processes were analyzed, and the structural dynamic response was extracted simultaneously. When the filling rate improved and compaction consistency was enhanced after vibration, and the structural response did not exceed the allowable range, the parameter group was deemed valid. When segregation worsened or the structural response exceeded limits, it was deemed an invalid parameter group. Parameter groups with limited compaction improvement (e.g., filling rate improvement of less than 0.5%) but low structural response could be retained as conservative solutions. If mixed deterioration or local loose areas existed simultaneously, the parameter group was directly eliminated.
[0098] Finally, the comprehensive results of each candidate parameter group in each stage of drum mixing, material distribution, surface leveling and vibration compaction are compared and analyzed. The parameter combination that simultaneously meets the constraints of mixing uniformity, filling rate, surface flatness and structural response is selected to form a preliminary feasible parameter range within the scope of a single filling unit. The results include drum speed range, mixing time range, filling rate range, material distribution method, leveling operation method and vibration parameter range, etc., and are output in combination with the corresponding applicable boundary conditions.
[0099] Through the above process, the initial screening and optimization of the filling process parameters within a single filling unit are achieved, providing a foundation for subsequent coupled analysis of multiple filling units.
[0100] In the second application example, based on the preliminary feasible parameter range of a single filling unit obtained in the first application example above, the analysis object is extended to the overall filling process consisting of multiple filling units. The entire filling process is jointly optimized by passing the sub-units in a cyclical manner to obtain a parameter combination range applicable to the overall filling space.
[0101] In this embodiment, the effective filling space of the target filling object is first divided into multiple filling units along the filling direction, so that each filling unit corresponds to a complete mixing, spreading, flattening and vibration compaction process, thereby constructing a sub-unit model structure consistent with the actual filling process.
[0102] Based on this, the preliminary parameter range obtained from the first application embodiment is used as the design space to combine and configure the roller speed, mixing time, material distribution method, flattening method, and vibration parameters in different filling units. For areas near the boundary, areas with geometric changes, or areas with limited local space, the material distribution path and vibration loading method can be adjusted to adapt the parameter settings to the structural characteristics of different areas.
[0103] Subsequently, the coupled simulation analysis of the first filling unit was completed according to the selected parameter combination, and the particle distribution, surface morphology and structural response results of the filling unit after the mixing, spreading, leveling and vibration compaction processes were obtained.
[0104] After completing the analysis of the first filling unit, its final particle distribution state is used as the initial boundary condition for the next filling unit. The same filling process is repeated in subsequent filling units, allowing the particle state to be transferred step by step between filling units. During the state transfer process, the surface undulation features, local compaction differences, and boundary packing states formed in the previous filling unit are retained and used in subsequent calculations to reflect the cumulative effect of layered filling in actual engineering.
[0105] After all filling units have completed the coupled calculation, the overall filling results are evaluated in a unified manner. The filling rate, mixing uniformity differences, local compaction consistency, surface morphology and particle segregation of each filling unit are statistically analyzed. Combined with the particle collision intensity generated during the filling process and the peak response of the equipment or structure, the overall filling quality and structural safety are comprehensively characterized.
[0106] During the evaluation process, the differences in filling rate and the variation in mixing uniformity between adjacent filling units are used as important criteria. When each filling unit meets the following conditions: the peak structural response does not exceed the design allowable value; the difference in filling rate between adjacent filling units is no greater than 2%; the difference in mixing uniformity index between adjacent filling units is no greater than 0.05; and the final overall filling rate meets the design requirements, the parameter combination is deemed to be suitable for the overall process. Conversely, if any filling unit exhibits localized over-vibration, loosening in corner areas, or amplified accumulation of delamination differences, the parameter combination is considered unsuitable for the overall filling process.
[0107] Based on this, multiple parameter combinations that meet the constraints are comprehensively compared and ranked. By analyzing the influence trends of different parameter configurations on the filling effect and structural response, the parameter combination range that can balance mixing uniformity, filling efficiency, and structural safety is further identified. In this process, various parameter optimization strategies, such as parameter scanning, orthogonal experiments, response surface analysis, surrogate models, or multi-objective optimization methods, can be combined to explore the parameter space, thereby improving parameter screening efficiency and enhancing the stability of results.
[0108] When using a multi-objective optimization approach, the preferred objectives are mixing uniformity and filling rate, while the objectives for suppressing segregation degree, surface height deviation, particle collision intensity, and structural peak response are selected. When the overall levels of different parameter groups are similar, the parameter group with lower vibration acceleration, wider process window, and better repeatability is preferred as the recommended scheme.
[0109] Finally, based on the comprehensive evaluation results, a parameter window applicable to the complete filling process was determined, and the filling characteristics of different regions were distinguished and described, so that regions near the boundary or with significant geometric changes correspond to different parameter value ranges with conventional regions, thereby improving the adaptability and feasibility of process parameters in actual engineering.
[0110] In the third application example, based on the obtained overall filling parameter window, the simulation results are verified through actual filling experiments, and an iterative correction mechanism is established between the experimental results and the simulation results to further improve the accuracy of the model and determine the final process parameter range.
[0111] In the third embodiment, the actual filling operation is first carried out according to the filling process parameter range determined in the second application embodiment, and the mixing, spreading, leveling and vibration compaction processes are completed in sequence according to the unit cycle method. At the same time, the process execution status and related operating parameters of each filling unit are recorded.
[0112] During the filling process, the entire process of roller rotation speed, mixing time, loading status, material distribution path, leveling operation method, and vibration parameters is recorded. At the same time, the changes in particle distribution, surface morphology evolution, and possible local anomalies are observed to serve as the basis for subsequent comparative analysis.
[0113] After filling is completed, the overall and local distribution of the particle bed is tested. The test content includes at least the overall filling quality, final filling height, surface flatness, component distribution at different height positions and different cross-sectional positions, local compaction consistency, and purging pressure drop characteristics.
[0114] In specific testing procedures, when using sampling to evaluate mixing uniformity, sampling locations cover the upper, middle, and lower areas of the filling space, while also considering locations near the boundaries and center to improve the representativeness of the test results. When using non-destructive testing or indirect evaluation methods, a comprehensive judgment is made by combining quality records, layer height change records, and pressure drop test results. Simultaneously, key testing indicators are repeatedly measured, and the consistency of the measurement results is verified. If a single measurement result deviates from the average value by more than a set range, the data is reviewed to ensure the reliability of the evaluation results.
[0115] Subsequently, the experimental results were compared with the simulation results under the corresponding parameter combinations in the second application embodiment. By analyzing the differences between various evaluation indicators, the degree of deviation between the model prediction results and the actual filling results was determined. When the relative deviations between the key evaluation indicators are within the allowable range (e.g., none greater than 10%), it can be considered that the established coupled model can accurately reflect the characteristics of the actual filling process, thereby verifying the effectiveness of the obtained parameter window.
[0116] When significant deviations are observed in the comparison results (e.g., the relative deviation of any evaluation index exceeds 10%), it indicates that the current model parameters or boundary conditions cannot fully represent the actual working conditions. In this case, the model should be specifically modified based on the deviation, including adjustments to particle contact characteristics, interaction parameters between particles and boundaries, and equipment motion input and structural damping characteristics. Specifically, when there are large deviations between the simulation and experimental results during the drum mixing stage, the particle recovery coefficient, friction coefficient, and boundary conditions corresponding to the internal flow guiding components of the drum should be adjusted first to improve the tumbling and exchange behavior of particles during the mixing process. When the deviation mainly occurs during the discharge, feeding, and leveling stages, the discharge boundary conditions, feeding path, and contact relationship between the leveling components and particles should be modified to improve the accuracy of predicting particle accumulation morphology and surface topography. When the deviation is concentrated in the vibration compaction stage, the vibration excitation input, support constraints, and structural damping parameters should be adjusted to make the particle compaction behavior and structural response results closer to the actual working conditions.
[0117] After completing the parameter correction, the single filling unit analysis and overall filling optimization process are re-executed so that the updated model gradually approaches the actual filling behavior in multiple iterations until the simulation results and experimental results reach the consistency requirements.
[0118] Based on this, and combined with validated parameter combinations, the final filling process window suitable for engineering implementation is determined, and corresponding process execution rules are further formulated. These rules include not only the parameter value ranges for each stage, but also filling sequence control requirements, process monitoring content, and anomaly detection and adjustment strategies. When situations such as decreased mixing uniformity, localized loosening, or abnormal structural response occur during the filling process, the relevant parameters are corrected according to the established adjustment path to ensure the stability and repeatability of the filling quality.
[0119] The above technical solution transforms the particle filling process from a traditional, experience-based, decentralized operation into a continuous simulation process coupled with a particle discrete element model and a filling equipment dynamics model. Within the filling space, through unit division and sequential connection of multiple process stages, the orderly transfer of particle state between different process stages and filling units is achieved. This allows for the simultaneous characterization of the interaction between particle motion behavior and the dynamic response of the filling structure within a unified modeling framework. Based on this, by constructing an evaluation index system that comprehensively characterizes the spatial distribution state of particles and structural response characteristics, and combining the results of cyclic filling simulations to analyze and optimize multi-parameter combinations, the solution not only systematically reveals the coupled influence of multiple process parameters such as roller mixing, fabric spreading, and vibration compaction on filling quality and structural safety, but also obtains a range of process parameters applicable to the target filling space while meeting structural constraints. This improves the predictability of the filling process and the rationality of parameter selection, reduces reliance on experiments, and enhances the consistency of filling quality and the feasibility of engineering applications.
[0120] In one embodiment, a computer storage medium is provided on which a computer program is stored, which, when executed by a processor, implements a particle loading simulation method based on multi-process coupling.
[0121] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A particle loading simulation method based on multi-process coupling, characterized in that, include: Based on the particle system parameters and the geometric boundary parameters of the filling equipment, a discrete element model of the particles and a dynamic model of the filling equipment are constructed respectively, and a coupling relationship is established between the discrete element model of the particles and the dynamic model. The filling space is divided into several filling units, and each filling unit is provided with a corresponding filling process stage; the filling process stage includes at least a roller mixing treatment stage, a discharge fabric spreading stage, and a vibration compaction stage. According to the divided loading units, cyclic loading simulation is executed sequentially based on the coupling relationship; wherein, within the same loading unit, the particle state of the previous loading process stage is used as the initial state of the next loading process stage; within two loading units with adjacent execution order, the particle state of the previous loading unit after the completion of the loading process stage is used as the initial state of the next loading unit. Based on the cyclic filling simulation process, an evaluation index is constructed to comprehensively characterize the spatial distribution state of particles and the response characteristics of the filling structure. Based on the evaluation index, the parameter combination of the filling process is optimized and analyzed to determine the range of filling process parameters corresponding to the filling space.
2. The particle loading simulation method based on multi-process coupling according to claim 1, characterized in that, The construction of the particle discrete element model includes: Based on the material properties, particle size characteristics, and proportions of different types of particles, a mixed particle discrete element model containing at least two types of particles is established, and a contact relationship model between particles and between particles and the boundary of the filling equipment is constructed. The particle discrete element model is based on particle system parameters and contact mechanical parameters to dynamically describe the translational and rotational behavior of particles under force.
3. The particle loading simulation method based on multi-process coupling according to claim 1, characterized in that, Construct a dynamic model of the loading equipment, including: Based on the structural parameters, motion patterns, and boundary constraints of the filling equipment, a dynamic model is constructed that includes a drum mixing device, a discharge device, a spreading device, a leveling device, and a vibration device. The dynamic model is used to describe the motion process of the filling equipment under driving conditions and the dynamic boundary interaction relationship with the particle system, and to characterize the dynamic response characteristics of the equipment structure during the filling process.
4. The particle loading simulation method based on multi-process coupling according to claim 1, characterized in that, The establishment of the coupling relationship includes: During the simulation, the mechanical interaction information between the particle discrete element model and the dynamic model is updated synchronously based on the time step. The particle force output by the particle discrete element model is loaded into the dynamic model, and the motion response output by the dynamic model is reacted back onto the particle discrete element model.
5. The particle loading simulation method based on multi-process coupling according to claim 3, characterized in that, During the drum mixing stage, the sequential execution of the cyclic loading simulation based on the coupling relationship includes: Under the coupling condition of the particle discrete element model and the dynamic model, the motion boundary of the drum mixing device is used as the dynamic constraint of particle motion, and the contact action of particles on the inner wall of the drum and components is fed back to the dynamic model. Based on the motion input parameters of the dynamic model, the tumbling, dropping and redistribution process of particles in the drum is simulated and analyzed to obtain the premixed distribution state of the particles.
6. The particle loading simulation method based on multi-process coupling according to claim 1 or 3, characterized in that, During the material spreading and flattening stage, the sequential execution of the cyclic loading simulation based on the coupling relationship includes: Using the particle state obtained in the mixing stage as the initial condition, the process of particles being released from the discharge device, dispersed by the distribution device, and entering the filling unit to form the initial accumulation layer is simulated within the coupled framework of the particle discrete element model and the dynamic model. The motion of the leveling device is solved based on the dynamic model, and the solution is used as the dynamic boundary condition for particle motion to level the particle accumulation surface and obtain the spatial distribution state of particles in the filling unit.
7. The particle loading simulation method based on multi-process coupling according to claim 1 or 3, characterized in that, During the compaction phase, the sequential execution of cyclic loading simulation based on the coupling relationship includes: Based on the current particle packing state of the filling unit, and under the coupling relationship between the particle discrete element model and the dynamic model, a vibration compaction condition is introduced, and the coupling conditions between the vibration device, the filling boundary, and the particle system are defined. Based on the aforementioned coupling conditions, the particle filling, rearrangement, and contact network evolution processes under vibration are simulated and analyzed by changing vibration parameters. During the simulation, the dynamic response of the filling boundary is calculated synchronously, and the vibration parameters are synchronously constrained based on the dynamic response.
8. The particle loading simulation method based on multi-process coupling according to claim 1 or 3, characterized in that, The optimization analysis of parameter combinations in the filling process includes: The motion input parameters of the dynamic model are selected as variables to be optimized, and the evaluation index results under different parameter combinations are calculated based on the cyclic loading simulation process. The parameter combinations are comprehensively evaluated based on the evaluation indicators to screen parameter combinations that meet the preset constraints and determine the range of filling process parameters.
9. The particle loading simulation method based on multi-process coupling according to claim 1, characterized in that, The method further includes: Actual filling experiments were conducted based on the filling process parameter range to obtain the distribution state and structural response data of the particles after filling, and the results were compared with those of the cyclic filling simulation. If the deviation of the comparison results exceeds a preset threshold, the parameters in the particle discrete element model or the kinetic model are corrected, and the cyclic filling simulation and parameter optimization analysis process is repeated until the simulation results and actual results meet the consistency requirements. Based on the results that meet the consistency requirements, the final filling process parameter range is determined.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the particle loading simulation method based on multi-process coupling as described in any one of claims 1 to 9.