Macro-micro coupled granular material aging deformation simulation method

Abstract

The invention discloses a macro-micro coupled granular material aging deformation simulation method, and relates to the technical field of material mechanics numerical simulation, and the macro-micro coupled granular material aging deformation simulation method comprises the following steps: collecting experimental data of a granular material sample, establishing a micro particle model by using a discrete element method, and realizing dynamic evolution of micro particles based on a dynamic updating mechanism of particle aging deformation; the method comprises the following steps: constructing a macroscopic constitutive model by combining Gay-Berne potential and an EEPA particle contact model on the basis of a microcosmic particle model, constructing a dynamic link library by coupling microcosmic evolution parameters to automatically update the aging deformation characteristics of microcosmic particles, and optimizing the parameters of the microcosmic particle model and the macroscopic constitutive model by using a Bayesian filtering algorithm and a Gaussian mixture model. According to the method, the macro-micro coupled granular material model with a dynamic updating mechanism is established and the optimization algorithm is combined to realize parameter adaptive updating, so that the precision, the efficiency and the operability of the aging deformation simulation of the granular material are improved.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology in materials mechanics, and in particular to a macro-micro coupled method for simulating the aging deformation of granular materials. Background Technology

[0002] In recent years, the study of the mechanical behavior of particulate materials has received widespread attention, especially in engineering applications. Accurately simulating the aging deformation characteristics of particulate materials has become a major challenge in materials design and performance optimization. The discrete element method (DEM) can simulate the interactions between particles at the microscopic scale and predict the macroscopic mechanical response of particulate materials under external loads. Traditional particulate material simulations often focus on static properties or short-term behavior, but the aging deformation of particulate materials during long-term use, such as expansion, contraction, and water absorption, is often overlooked.

[0003] Although there are existing studies on particle material simulation based on the discrete element method, most methods still have certain limitations in considering the aging deformation of particles. Existing techniques usually rely on static or short-term particle interaction models, ignoring the dynamic evolution of particles under long-term loading or environmental changes, which limits the accuracy and applicability when dealing with complex aging deformation characteristics. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a macro-micro coupled simulation method for the aging deformation of granular materials, which solves the problems of lack of dynamic update mechanism and insufficient coupling between macro and micro models in the simulation of aging deformation of granular materials.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a macro-micro coupled method for simulating the aging deformation of particulate materials, comprising, Experimental data of particulate material samples were collected, a micro-particle model was established using the discrete element method, and the dynamic evolution of micro-particles was realized based on the dynamic update mechanism of particle age deformation. Based on the micro-particle model, a macro-constitutive model is constructed by combining the Gay-Berne potential and the EEPA particle contact model. By coupling the micro-evolution parameters, a dynamic link library is constructed to automatically update the time-dependent deformation characteristics of the micro-particles. The parameters of the micro-particle model and the macro-constitutive model are optimized using the Bayesian filtering algorithm and the Gaussian mixture model. Based on the optimized micro-particle model and macro-constitutive model, time-dependent deformation simulation is performed, and the macro-stress-strain response and micro-evolution characteristics of the granular material are output. The data generated during the entire simulation process are uploaded to the database for storage.

[0007] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for particulate materials described in this invention, the step of collecting experimental data from particulate material samples and establishing a micro-particle model using the discrete element method includes the following steps: The physical properties of particulate material samples were collected, and the particle size distribution, particle shape and surface roughness data of the particulate material samples were obtained using electron microscopy. Numerical methods are used to generate a three-dimensional geometric model of the particles based on their size distribution and shape. The discrete element method is then used to calculate the contact force of each particle at the contact point in the three-dimensional geometric model, including normal force, friction force, and adhesion force. The contact force between particles is obtained by summing the normal force, frictional force, and cohesive force between the particles. Based on the contact force between particles, the equation of motion of the particles is calculated using Newton's second law. The position and velocity of the particles are updated using numerical integration methods based on the equations of motion. The contact force between particles, the motion state of the particles, and their dynamic changes over time are calculated using the discrete element method to obtain a microscopic particle model.

[0008] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for particulate materials described in this invention, the dynamic update mechanism based on particle aging deformation realizes the dynamic evolution of microparticles, including the following steps: Based on the dynamic update mechanism of particle age deformation, the size change of particles during water absorption and expansion is calculated; Calculate the density and stiffness changes of particles based on particle size changes; Based on the calculated particle changes, the contact force and motion equations of the particles are recalculated to realize the dynamic evolution of microscopic particles.

[0009] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for particulate materials described in this invention, the construction of a macroscopic constitutive model based on a micro-particle model, combined with the Gay-Berne potential and the EEPA particle contact model, includes the following steps: Based on the contact force of particles in the micro-particle model, the interaction force between particles is calculated as the Gay-Berne potential. By initially modeling the interparticle contact behavior, the key parameters of the EEPA particle contact model are defined. The least squares method is used to compare the calculation results of the EEPA particle contact model with the experimental data, and the parameters of the EEPA model are gradually optimized. After iteration, the trained EEPA particle contact model is obtained. By combining the Gay-Berne potential with the trained EEPA particle contact model, a comprehensive particle contact model is obtained that can simulate the elastic contact between particles and the long-distance interaction force between particles, and output the elastic contact and surface interaction force between particles. Using the RVE method, representative volume elements containing particle interactions are extracted from the micro-particle model, and the stress-strain relationship of the volume elements is calculated. By integrating and statistically averaging all representative volume elements, a macroscopic constitutive model that reflects macroscopic mechanical behavior is obtained.

[0010] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for particulate materials described in this invention, the step of automatically updating the aging deformation characteristics of microparticles by constructing a dynamic link library through coupling micro-evolution parameters includes the following steps: The physical properties of the particles are updated in real time, reflecting the dynamic evolution characteristics of the particles over time during the aging process, and the macroscopic constitutive model is updated in real time. Meanwhile, a dynamic link library is built on the discrete element simulation platform through the API interface to simulate the growth process of particles, automatically update the physical properties of particles, and automatically calculate the mass and momentum of particles at each time step, feeding them back to the micro-particle model and the macro-constitutive model.

[0011] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for granular materials described in this invention, the optimization of the parameters of the micro-particle model and the macro-constitutive model using a Bayesian filtering algorithm and a Gaussian mixture model includes the following steps: Based on existing data and literature, the prior distribution of the Bayesian filter is initialized. The micro-particle model and the macro-constitutive model are updated in each iteration. The Bayesian filter calculates the posterior distribution based on the output of the current micro-particle model and the macro-constitutive model and the experimental data, and updates the parameters of the micro-particle model and the macro-constitutive model based on the prior distribution and the posterior distribution. Based on the parameters of the micro-particle model and the macro-constitutive model, a parameter space is generated. The number of parameters in the parameter space is set to the number of Gaussian distributions. The Gaussian mixture model is trained using the expectation-maximization algorithm. Each training iteration uses the posterior distribution obtained by Bayesian filtering as the initial value of the Gaussian mixture model. When the number of iterations reaches the set iteration threshold, the trained Gaussian mixture model and the optimized parameter space are obtained. The optimized parameters in the parameter space are applied to the micro-particle model and the macro-constitutive model to obtain the optimized micro-particle model and the macro-constitutive model.

[0012] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for granular materials described in this invention, the step of aging deformation simulation based on the optimized micro-particle model and macro-constitutive model, and outputting the macro-stress-strain response and micro-evolution characteristics of the granular material, includes the following steps: Using the optimized microparticle model, the changes in particle size, density and contact stiffness are simulated based on the dynamic update mechanism of particle age deformation. The mechanical response of the macroscopic constitutive model is updated based on the age deformation characteristics of the microparticle model. The simulation results are used to output the macroscopic stress-strain response and microscopic evolution characteristics of the particulate material. The simulation results are compared with the experimental data to verify the accuracy of the macroscopic stress-strain response and microscopic particle evolution characteristics. If the simulation results are inconsistent with the experimental data, the parameters of the microscopic particle model and the macroscopic constitutive model need to be readjusted.

[0013] As a preferred embodiment of the macro-micro coupled aging deformation simulation method for granular materials described in this invention, the step of uploading the data generated during the entire simulation process to a database for storage includes the following steps: Data from collected particulate material samples, microscopic evolution characteristics, macroscopic stress-strain response data, and parameter optimization data from microscopic particle models and macroscopic constitutive models are uploaded to the database in a unified format for storage.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the macro-micro coupled aging deformation simulation method for particulate materials as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the macro-micro coupled aging deformation simulation method for particulate materials as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By using the discrete element method to establish a micro-particle model and combining it with a dynamic update mechanism for particle aging deformation, the dynamic evolution of particles during aging deformation is realized, improving the accuracy and adaptability of the particle model in long-term use; a macro-constitutive model is constructed by combining the Gay-Berne potential and the EEPA particle contact model, and the aging deformation characteristics of micro-particles are automatically updated by coupling micro-evolution parameters, ensuring that changes in micro-particles can be reflected in macro-material properties in real time; the parameters of the micro-particle model and the macro-constitutive model are optimized using a Bayesian filtering algorithm and a Gaussian mixture model, improving the prediction accuracy of the micro-particle model and the macro-constitutive model, ultimately achieving accurate aging deformation simulation, and the data is uploaded to a database for storage, facilitating subsequent analysis and performance optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a macro-micro coupled granular material aging deformation simulation method.

[0019] Figure 2 This is a schematic diagram illustrating the construction and parameter optimization of a macroscopic constitutive model. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figure 1 and Figure 2This is one embodiment of the present invention, which provides a macro-micro coupled method for simulating the aging deformation of particulate materials, comprising the following steps: Experimental data of particulate material samples were collected, a micro-particle model was established using the discrete element method, and the dynamic evolution of micro-particles was realized based on the dynamic update mechanism of particle age deformation.

[0024] Specifically, the physical properties of particulate material samples are collected, and electron microscopy is used to obtain data on particle size distribution, particle shape, and surface roughness of the particulate material samples. Numerical methods are used to generate a three-dimensional geometric model of the particles based on their size distribution and shape. The discrete element method is then used to calculate the contact force between each particle and the contact point in the three-dimensional geometric model, including normal force, frictional force, and cohesive force. The normal force between particles is calculated using the following formula: ; in, The normal force between particles, The normal contact stiffness is determined by the particle's physical properties and the contact area. This represents the normal displacement.

[0025] The frictional force between particles is calculated based on the normal force between them, using the following formula: ; in, The frictional force between particles The coefficient of friction is determined by the physical properties of the particles.

[0026] The formula for calculating the cohesive force between particles is: ; in, The adhesive force between particles, Where is the adhesive force constant. The characteristic length of the material is determined by the surface roughness of the particles.

[0027] The contact force between particles is obtained by summing the normal force, frictional force, and cohesive force between the particles. Based on the contact forces between particles, the equation of motion for the particles is calculated using Newton's second law, and the formula is: ; in, For granular indexing, For the first The mass of each particle For the first The position vector of each particle For time indexing, For the first The total contact force on each particle This refers to integral calculations in mathematical formulas.

[0028] The position and velocity of the particles are updated using numerical integration based on the equations of motion, as shown in the following formula: ; ; in, For time step, For the first Each particle Location at any given moment For the first Each particle Location at any given moment For the first Each particle The speed of time For the first Each particle The speed of time.

[0029] The contact force between particles, the motion state of the particles, and their dynamic changes over time are calculated using the discrete element method to obtain a microscopic particle model.

[0030] By collecting physical properties of particulate material samples and obtaining data on their particle size distribution, shape, and surface characteristics, a precise characterization of particle geometry was achieved. A three-dimensional geometric model was generated using numerical methods, and the contact force between particles was calculated using the discrete element method, accurately reflecting the interaction law between particles. A quantitative description of particle force and motion behavior was achieved through comprehensive calculation of normal force, frictional force, and cohesive force, and by establishing equations of motion based on Newton's second law. Furthermore, particle position and velocity were updated through numerical integration, characterizing the dynamic evolution process of the particles. Finally, a microscopic particle model reflecting the contact, motion, and temporal changes of particles was obtained based on the discrete element method, realizing high-precision simulation and dynamic analysis of the micromechanical behavior of particulate materials, and improving the realism and reliability of research on the mechanical properties of particulate materials.

[0031] Furthermore, based on the dynamic update mechanism of particle age deformation, the size change of particles during water absorption and expansion is calculated using the following formula: ; in, For particles in The size of time, The initial size of the particle. The coefficient of water absorption and expansion is determined by the properties of the granular material.

[0032] The density change of particles is calculated based on the size change of the particles, using the following formula: ; in, For particles in Density of time, The initial density of the particles is given.

[0033] The stiffness change of a particle is calculated based on its size variation, using the following formula: ; in, For particles in Stiffness at any moment Let be the initial stiffness of the particle. The coefficient that sets the relationship between stiffness and particle size.

[0034] Based on the calculated particle changes, the contact force and motion equations of the particles are recalculated to realize the dynamic evolution of microscopic particles.

[0035] By calculating the size changes of particles during water absorption and expansion through a dynamic update mechanism based on particle aging deformation, the law of particle volume change is accurately depicted. By further calculating the density and stiffness changes of particles, the micro-particle model can dynamically reflect the evolution of the mechanical properties of particulate materials during aging. Then, the contact force and motion equations are recalculated based on the changes in particle parameters, realizing the real dynamic evolution of micro-particles under different states. This improves the physical consistency and prediction accuracy of aging deformation simulation and enhances the ability to simulate and analyze the long-term mechanical behavior of particulate materials.

[0036] Based on the micro-particle model, a macro-constitutive model is constructed by combining the Gay-Berne potential and the EEPA particle contact model. By coupling the micro-evolution parameters, a dynamic link library is constructed to automatically update the time-dependent deformation characteristics of the micro-particles. The parameters of the micro-particle model and the macro-constitutive model are optimized using the Bayesian filtering algorithm and the Gaussian mixture model.

[0037] Specifically, based on the contact force of particles in the micro-particle model, the interaction force between particles is calculated as the Gay-Berne potential. By initially modeling the interparticle contact behavior, the key parameters of the EEPA particle contact model are defined. The least squares method is used to compare the model calculation results with experimental data, and the parameters of the EEPA model are gradually optimized. After iteration, the trained EEPA particle contact model is obtained. By combining the Gay-Berne potential with the trained EEPA particle contact model, a comprehensive particle contact model is obtained that can simulate the elastic contact between particles and the long-distance interaction force between particles, and output the elastic contact and surface interaction force between particles. Using the RVE method, representative volume elements containing particle interactions are extracted from the micro-particle model, and the stress-strain relationship of the volume elements is calculated. The formula is as follows: ; in, For macroscopic stress of volume elements, For the macroscopic strain of a volume element, This is the elastic modulus calculated based on elastic contact and surface interaction forces.

[0038] By integrating and statistically averaging all representative volume elements, a macroscopic constitutive model that reflects macroscopic mechanical behavior is obtained.

[0039] By calculating the interparticle interaction forces based on the contact forces of particles in the micro-particle model and introducing the Gay-Berne potential, a precise description of the long-range interaction characteristics of particles is achieved. By modeling the particle contact behavior and optimizing the EEPA model parameters using the least squares method, the particle contact model can more accurately fit the experimental results. By combining the Gay-Berne potential with the optimized EEPA model, a comprehensive contact model with both elastic contact and long-range interaction characteristics is constructed, achieving a unified expression of complex interparticle interactions. Furthermore, by extracting representative volume elements containing particle interactions using the RVE method and calculating their stress-strain relationships, the scale transformation from micro-contact behavior to macro-mechanical response is realized. Finally, by integrating and statistically analyzing multiple representative volume elements, a macro-constitutive model that can comprehensively reflect the overall mechanical properties of particulate materials is obtained, achieving accurate prediction and multi-scale simulation of the mechanical properties of particulate materials under macro-micro coupling.

[0040] Furthermore, the physical properties of the particles are updated in real time to reflect the dynamic evolution characteristics of the particles over time during the aging process, and the macroscopic constitutive model is updated in real time. Meanwhile, a dynamic link library is built on the discrete element simulation platform through the API interface to simulate the growth process of particles, automatically update the physical properties of particles, and automatically calculate the mass and momentum of particles at each time step, feeding them back to the micro-particle model and the macro-constitutive model.

[0041] By updating the physical properties of particles in real time, the dynamic evolution characteristics of particles during the aging process are characterized, and the macroscopic constitutive model is updated synchronously, so that the macroscopic response can accurately reflect the microscopic changes. At the same time, by building a dynamic link library with API interface on the discrete element simulation platform, the particle growth process is automatically simulated and the particle mass and momentum are calculated in real time. This realizes the adaptive feedback of information between the microscopic particle model and the macroscopic constitutive model, improves the dynamic response accuracy and computational efficiency of the aging deformation simulation, and enhances the coupling stability between the microscopic particle model and the macroscopic constitutive model.

[0042] Furthermore, based on existing data and literature, the prior distribution of the Bayesian filter is initialized. The micro-particle model and the macro-constitutive model are updated in each iteration. The Bayesian filter calculates the posterior distribution based on the output of the current micro-particle model and the macro-constitutive model and experimental data, and updates the parameters of the micro-particle model and the macro-constitutive model based on the prior distribution and the posterior distribution. Based on the parameters of the micro-particle model and the macro-constitutive model, a parameter space is generated. The number of parameters in the parameter space is set to the number of Gaussian distributions. The Gaussian mixture model is trained using the expectation-maximization algorithm. Each training iteration uses the posterior distribution obtained by Bayesian filtering as the initial value of the Gaussian mixture model. When the number of iterations reaches the set iteration threshold, the trained Gaussian mixture model and the optimized parameter space are obtained. The optimized parameters in the parameter space are applied to the micro-particle model and the macro-constitutive model to obtain the optimized micro-particle model and the macro-constitutive model.

[0043] By initializing the prior distribution of the Bayesian filter based on existing data and literature, and dynamically updating the parameters of the micro-granular model and the macro-constitutive model using the posterior distribution during model iteration, adaptive correction and uncertainty control of the parameters of the micro-granular model and the macro-constitutive model are achieved. By constructing a parameter space based on the parameters of the micro-granular model and the macro-constitutive model and training it with a Gaussian mixture model, the parameter distribution can more accurately fit the actual data characteristics, improving the ability of the micro-granular model and the macro-constitutive model to express complex nonlinear relationships. Finally, the optimized parameter space is applied to the micro- and macro-models, achieving globally optimal parameter configuration and improved accuracy.

[0044] Based on the optimized micro-particle model and macro-constitutive model, time-dependent deformation simulation is performed, and the macro-stress-strain response and micro-evolution characteristics of the granular material are output. The data generated during the entire simulation process are uploaded to the database for storage.

[0045] Specifically, using the optimized microparticle model, the changes in particle size, density, and contact stiffness are simulated based on the dynamic update mechanism of particle age deformation, and the mechanical response of the macroscopic constitutive model is updated based on the age deformation characteristics of the microparticle model. The simulation results are used to output the macroscopic stress-strain response and microscopic evolution characteristics of the particulate material. The simulation results are compared with the experimental data to verify the accuracy of the macroscopic stress-strain response and microscopic particle evolution characteristics. If the simulation results are inconsistent with the experimental data, the parameters of the microscopic particle model and the macroscopic constitutive model need to be readjusted.

[0046] By using an optimized microparticle model and combining it with a dynamic update mechanism for particle aging deformation, the evolution of particles in terms of size, density, and contact stiffness is simulated. This achieves synchronous updates of microparticle properties and macroscopic mechanical responses, ensuring that the microparticle model can reflect the true deformation behavior of materials during aging. By outputting and comparing the macroscopic stress-strain response and microscopic evolution characteristics of particulate materials, the results are verified and adjusted, forming a parameter self-calibration mechanism, which improves the accuracy and verifiability of aging deformation simulation of particulate materials.

[0047] Furthermore, the data collected from the particulate material samples, the microscopic evolution characteristics data, the macroscopic stress-strain response data, and the parameter optimization data of the microscopic particle model and the macroscopic constitutive model will be uploaded to the database in a unified format for storage.

[0048] By uniformly formatting and uploading the experimental data, micro-evolution characteristics, macro-stress-strain response, and model parameter optimization results of particulate material samples to the database for storage, standardized management and centralized storage of multi-source data are achieved, facilitating subsequent data retrieval, model reproduction, and result tracking.

[0049] This embodiment also provides a computer device applicable to the macro-micro coupled aging deformation simulation method for particulate materials, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the macro-micro coupled aging deformation simulation method for particulate materials as proposed in the above embodiment.

[0050] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0051] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for simulating the age-deformation of particulate materials with macro-micro coupling as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0052] In summary, this invention achieves the dynamic evolution of particles during aging deformation by: establishing a micro-particle model using the discrete element method and combining it with a dynamic update mechanism for particle aging deformation; improving the accuracy and adaptability of the particle model in long-term use; constructing a macro-constitutive model by combining the Gay-Berne potential and the EEPA particle contact model; and automatically updating the aging deformation characteristics of micro-particles by coupling micro-evolution parameters, ensuring that changes in micro-particles are reflected in macro-material properties in real time; and optimizing the model parameters using a Bayesian filtering algorithm and a Gaussian mixture model, improving the prediction accuracy of both the micro-particle model and the macro-constitutive model. Ultimately, this invention achieves accurate simulation of aging deformation, and the data is uploaded to a database for storage, facilitating subsequent analysis and performance optimization.

[0053] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A macro-micro coupled method for simulating the aging deformation of particulate materials, characterized in that: include, Experimental data of particulate material samples were collected, a micro-particle model was established using the discrete element method, and the dynamic evolution of micro-particles was realized based on the dynamic update mechanism of particle age deformation. Based on the micro-particle model, a macro-constitutive model is constructed by combining the Gay-Berne potential and the EEPA particle contact model. By coupling the micro-evolution parameters, a dynamic link library is constructed to automatically update the time-dependent deformation characteristics of the micro-particles. The parameters of the micro-particle model and the macro-constitutive model are optimized using the Bayesian filtering algorithm and the Gaussian mixture model. Based on the optimized micro-particle model and macro-constitutive model, time-dependent deformation simulation is performed, and the macro-stress-strain response and micro-evolution characteristics of the granular material are output. The data generated during the entire simulation process are uploaded to the database for storage.

2. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 1, characterized in that: The experimental data collected from the particulate material samples are used to establish a microscopic particle model using the discrete element method, including the following steps: The physical properties of particulate material samples were collected, and the particle size distribution, particle shape and surface roughness data of the particulate material samples were obtained using electron microscopy. Numerical methods are used to generate a three-dimensional geometric model of the particles based on their size distribution and shape. The discrete element method is then used to calculate the contact force of each particle at the contact point in the three-dimensional geometric model, including normal force, friction force, and adhesion force. The contact force between particles is obtained by summing the normal force, frictional force, and cohesive force between the particles. Based on the contact force between particles, the equation of motion of the particles is calculated using Newton's second law. The position and velocity of the particles are updated using numerical integration methods based on the equations of motion. The contact force between particles, the motion state of the particles, and their dynamic changes over time are calculated using the discrete element method to obtain a microscopic particle model.

3. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 2, characterized in that: The dynamic update mechanism based on particle age deformation realizes the dynamic evolution of microparticles, including the following steps: Based on the dynamic update mechanism of particle age deformation, the size change of particles during water absorption and expansion is calculated; Calculate the density and stiffness changes of particles based on particle size changes; Based on the calculated particle changes, the contact force and motion equations of the particles are recalculated to realize the dynamic evolution of microscopic particles.

4. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 3, characterized in that: The construction of a macroscopic constitutive model based on a microscopic particle model, combined with the Gay-Berne potential and the EEPA particle contact model, includes the following steps: Based on the contact force of particles in the micro-particle model, the interaction force between particles is calculated as the Gay-Berne potential. By initially modeling the interparticle contact behavior, the key parameters of the EEPA particle contact model are defined. The least squares method is used to compare the calculation results of the EEPA particle contact model with the experimental data, and the parameters of the EEPA model are gradually optimized. After iteration, the trained EEPA particle contact model is obtained. By combining the Gay-Berne potential with the trained EEPA particle contact model, a comprehensive particle contact model is obtained that can simulate the elastic contact between particles and the long-distance interaction force between particles, and output the elastic contact and surface interaction force between particles. Using the RVE method, representative volume elements containing particle interactions are extracted from the micro-particle model, and the stress-strain relationship of the volume elements is calculated. By integrating and statistically averaging all representative volume elements, a macroscopic constitutive model that reflects macroscopic mechanical behavior is obtained.

5. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 4, characterized in that: The method of automatically updating the time-dependent deformation characteristics of microparticles by constructing a dynamic link library through coupling micro-evolution parameters includes the following steps: The physical properties of the particles are updated in real time, reflecting the dynamic evolution characteristics of the particles over time during the aging process, and the macroscopic constitutive model is updated in real time. Meanwhile, a dynamic link library is built on the discrete element simulation platform through the API interface to simulate the growth process of particles, automatically update the physical properties of particles, and automatically calculate the mass and momentum of particles at each time step, feeding them back to the micro-particle model and the macro-constitutive model.

6. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 5, characterized in that: The optimization of parameters for the micro-particle model and the macro-constitutive model using Bayesian filtering algorithm and Gaussian mixture model includes the following steps: Based on existing data and literature, the prior distribution of the Bayesian filter is initialized. The micro-particle model and the macro-constitutive model are updated in each iteration. The Bayesian filter calculates the posterior distribution based on the output of the current micro-particle model and the macro-constitutive model and the experimental data, and updates the parameters of the micro-particle model and the macro-constitutive model based on the prior distribution and the posterior distribution. Based on the parameters of the micro-particle model and the macro-constitutive model, a parameter space is generated. The number of parameters in the parameter space is set to the number of Gaussian distributions. The Gaussian mixture model is trained using the expectation-maximization algorithm. Each training iteration uses the posterior distribution obtained by Bayesian filtering as the initial value of the Gaussian mixture model. When the number of iterations reaches the set iteration threshold, the trained Gaussian mixture model and the optimized parameter space are obtained. The optimized parameters in the parameter space are applied to the micro-particle model and the macro-constitutive model to obtain the optimized micro-particle model and the macro-constitutive model.

7. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 6, characterized in that: The simulation of time-dependent deformation based on the optimized micro-particle model and macro-constitutive model, and the output of the macro-stress-strain response and micro-evolution characteristics of the granular material, includes the following steps: Using the optimized microparticle model, the changes in particle size, density and contact stiffness are simulated based on the dynamic update mechanism of particle age deformation. The mechanical response of the macroscopic constitutive model is updated based on the age deformation characteristics of the microparticle model. The simulation results are used to output the macroscopic stress-strain response and microscopic evolution characteristics of the particulate material. The simulation results are compared with the experimental data to verify the accuracy of the macroscopic stress-strain response and microscopic particle evolution characteristics. If the simulation results are inconsistent with the experimental data, the parameters of the microscopic particle model and the macroscopic constitutive model need to be readjusted.

8. The macro-micro coupled aging deformation simulation method for particulate materials as described in claim 7, characterized in that: Uploading the data generated throughout the simulation process to the database for storage includes the following steps: Data from collected particulate material samples, microscopic evolution characteristics, macroscopic stress-strain response data, and parameter optimization data from microscopic particle models and macroscopic constitutive models are uploaded to the database in a unified format for storage.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the macro-micro coupled aging deformation simulation method for particulate materials as described in any one of claims 1 to 8.

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 steps of the macro-micro coupled aging deformation simulation method for particulate materials as described in any one of claims 1 to 8.

Images