DEM-based dynamic evolution method and system for contact force between soil particles

Abstract

The invention relates to a dynamic evolution method and system for contact force between soil particles based on a DEM (Digital Elevation Model). A DEM-based dynamic evolution method for contact force between soil particles comprises the following steps: outputting key data of interaction force between large-scale discrete element model particles from simulation data based on an EDEM platform; the particle information at least comprises eight particle information including the size of contact normal force between the particles, coordinates of contact points between the particles in the X / Y / Z directions, particle IDs and particle positions in the X / Y / Z directions; by means of MATLAB, information such as contact force, contact position, particle ID and the like of particles in the large-scale discrete element soil model is identified, screened, extracted, statistically analyzed and sequenced and filtered. According to the method, the problem that a large-scale calculation model in the EDEM cannot clearly and quantitatively display the force chain is solved, and classified presentation of a total force chain network structure, a strong force chain network structure, a secondary strong force chain network structure and a weak force chain network structure and dynamic evolution visualization changing along with time are achieved.

Description

Technical Field

[0001] This invention relates to the field of soil particle mechanics technology. In particular, it relates to a method and system for the dynamic evolution of contact forces between soil particles based on DEM (Digital Emission Model). Background Technology

[0002] The Discrete Element Method (DEM) was first introduced into the field of geotechnical mechanics by American scholar Cundall in 1971. Currently, this method is widely used to study the mechanical behavior of discrete particle systems such as soil and powder. In soil mechanical behavior research, DEM mainly involves the simulation of typical scenarios such as triaxial experiments, shear experiments, and penetration experiments. Commonly used software includes PFC and EDEM. As a mature commercial software platform, EDEM boasts significant application advantages due to its intuitive and user-friendly interface. Through convenient operations such as importing external CAD geometric models, complex simulation models can be quickly built, significantly improving the efficiency of engineering simulation. At the technical architecture level, the platform not only provides a secondary development interface (API) based on the C++ language to provide underlying support for user-customized development, but also innovatively develops a CPU+GPU heterogeneous parallel computing architecture, providing strong hardware support for high-performance simulation computation.

[0003] The macroscopic mechanical response of soil is determined by its complex internal microscopic motion. To deeply understand the macroscopic deformation and mechanical characteristics of granular systems under external loads, it is essential to extract, analyze, and visualize the motion parameters of particles at the particle scale. Traditional discrete element simulation software, such as EDEM, while capable of outputting information on interparticle interaction forces, lacks systematic and automated post-processing tools, resulting in significant deficiencies in post-processing functionality and an inability to clearly visualize and perform batch statistical analysis of interparticle interaction forces. In granular systems, the force transmission paths formed by contact forces between particles are called force chains. Force chains can be viewed as the "skeleton" of interparticle interactions, reflecting the main directions of force transmission. Force chain analysis is an effective technique for revealing the force transmission paths and distribution characteristics in granular systems. Through reasonable modeling, simulation, and data analysis, valuable results can be obtained, providing strong support for engineering design and research. Force chains are typically measured by the average or maximum value of all contact forces along the chain, and can be characterized by the normal contact forces between particles. However, large-scale discrete element models, especially those with fine granularity and large scale, are difficult to represent clearly in mature commercial EDEM software, such as... Figure 2As shown; in addition, due to the limited computational efficiency of large-scale models, the visualization process of force chains in large-scale fine-grained discrete element models is particularly slow, the problem of operation lag is very serious, and it is impossible to quantitatively and batch statistically analyze information such as the size and number of different types of force chains.

[0004] However, how to clearly present discrete element models with small granularity and large scale, and how to solve the problems of presentation efficiency and stuttering, are urgent issues that need to be addressed. Summary of the Invention

[0005] This invention provides a method for dynamic evolution of contact forces between soil particles based on DEM, in order to solve the problem that large-scale computational models in EDEM cannot clearly and quantitatively display force chains.

[0006] To achieve the above objectives, in a first aspect, the present invention relates to a method for dynamic evolution of soil particle contact force based on DEM, comprising: conducting discrete element simulation based on the EDEM platform, and outputting key data of particle interaction force in a large-scale discrete element model, including at least eight particle information items such as the magnitude of the particle contact normal force, the coordinates of the particle-particle contact point in the X / Y / Z directions, particle ID, and particle position in the X / Y / Z directions;

[0007] The output data is read and called using a Matlab programming algorithm to match the contact point with the particle ID, find the ID corresponding to each contact, and identify the contact particle.

[0008] Calculate the distance from the contact coordinates to the particles, find the two particles closest to each contact point, determine them as a pair of particles connected by that contact point, traverse all contact point positions, and thus generate data for visualization and quantitative statistical analysis of different force chain types between particles.

[0009] Based on the generated interparticle force chain data, Matlab was used to calculate the average value of the interparticle contact force. According to the average value, the force chain criteria were defined as strong force chain, medium-strong force chain and weak force chain. The force chain data and distance information were statistically analyzed.

[0010] By running Matlab, the maximum and minimum normal forces were found, the normal force drawing range was determined, and the normal force drawing range was divided into 256 intervals, each interval corresponding to a color. Different colors, line widths, and line lengths were assigned to each interval according to the magnitude of the normal force.

[0011] Draw a dotted line diagram, connect the pair of particles corresponding to each contact point in turn, and find the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact, obtain the corresponding line width, line length and color, and assign the three attributes of line width, line length and color to the line.

[0012] Traverse all contact points to generate a total force chain network diagram, a strong force chain diagram, a second strong force chain diagram, and a weak force chain diagram. Set the viewpoint, line thickness, color, length, frame width, and coordinate axis scaling factor of the force chain diagram. At the same time, output the statistical analysis results of the number, average distance, and average size of different force chain types.

[0013] To achieve the above objectives, in a second aspect, the present invention relates to a dynamic evolution system for interparticle contact forces in soil based on a DEM (Digital Emission Model), comprising:

[0014] The output module is used to carry out discrete element simulation based on the EDEM platform and output key data of the interaction force between particles in the large-scale discrete element model. It includes at least 8 pieces of particle information, such as the magnitude of the contact normal force between particles, the coordinates of the contact point between particles in the X, Y, and Z directions, the particle ID, and the particle position in the X, Y, and Z directions.

[0015] The contact particle identification module is used to read and call the output data through Matlab programming algorithms, match the contact points with particle IDs, find the ID corresponding to each contact, and identify the contact particles.

[0016] The distance calculation module is used to calculate the distance from the contact coordinates to the particles, find the two particles closest to each contact point, determine them as a pair of particles connected by that contact point, traverse all contact point positions, and thus generate data for visualization and quantitative statistical analysis of different force chain types between particles.

[0017] The force chain data statistics module is used to run Matlab to calculate the average value of the interparticle contact force based on the generated interparticle force chain data. According to the average value, the force chain criteria are defined as strong force chain, medium-strong force chain and weak force chain, and the force chain data and distance information are statistically analyzed.

[0018] The drawing range determination module is used to find the maximum and minimum normal forces by running Matlab, determine the normal force drawing range, divide the normal force drawing range into 256 intervals, each interval corresponds to a color, and assign different colors, line widths and line lengths to each interval in order of the magnitude of the normal force;

[0019] The drawing module is used to draw dotted line graphs. It connects the pair of particles corresponding to each contact point in turn, and finds the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact. It then obtains the corresponding line width, line length and color, and assigns these three attributes to the line.

[0020] The graph generation module is used to traverse all contact points and generate total force chain network diagrams, strong force chain diagrams, medium-strong force chain diagrams, and weak force chain diagrams. It allows setting the viewpoint, line thickness, color, length, frame width, and coordinate axis scaling factors of the force chain diagrams, while also outputting statistical analysis results on the number, average distance, and average size of different force chain types.

[0021] The present invention relates to a method and system for dynamic evolution of interparticle contact force in soil based on DEM, which has the following advantages compared with the prior art:

[0022] Based on simulation data from the EDEM platform, this invention utilizes MATLAB to identify, filter, extract, statistically analyze, and sort information such as contact force, contact location, and particle ID in large-scale discrete element soil models. It proposes a dynamic evolution visualization method for contact force between soil particles in large-scale discrete element models, solving the problem that large-scale computational models in EDEM cannot clearly and quantitatively display force chains. This method realizes the classification and presentation of the total force chain, strong, secondary strong, and weak force chain network structures, as well as the dynamic evolution visualization over time. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for dynamic evolution of soil particle contact force based on DEM in Embodiment 1 of the present invention.

[0024] Figure 2 This is a visualization of the force chain of a large-scale model based on the EDEM platform in the prior art of this invention;

[0025] Figure 3 This is an impact penetration simulation model of Example 1 of the DEM-based dynamic evolution method for inter-particle contact force in soil in Embodiment 1 of the present invention.

[0026] Figure 4 This is a visualization implementation process based on EDEM for a dynamic evolution method of soil particle contact force based on DEM in Embodiment 1 of the present invention.

[0027] Figure 5 This is a schematic diagram of the selection of the force chain calculation region in a method for dynamic evolution of interparticle contact force based on DEM in Embodiment 1 of the present invention.

[0028] Figure 6 A schematic diagram illustrating the classification of total force chain network, strong, medium-strong, and weak force chain networks from different perspectives in a dynamic evolution method of soil interparticle contact force based on DEM in Embodiment 1 of the present invention.

[0029] Figure 7 A schematic diagram illustrating the evolution of the total force chain network structure in a dynamic evolution method for interparticle contact forces based on DEM in Embodiment 1 of this invention.

[0030] Figure 8 A schematic diagram illustrating the evolution of a strong chain network structure in a dynamic evolution method for interparticle contact force based on DEM in Embodiment 1 of this invention.

[0031] Figure 9 The diagram shows the total force chain network, strong, medium, and weak force chain diagrams, and statistical analysis results of a dynamic evolution method for interparticle contact forces based on DEM in Embodiment 1 of this invention.

[0032] Figure 10 A visualization of the dynamic evolution of a strong chain network based on a DEM-based method for dynamic evolution of interparticle contact forces in soil, as shown in Embodiment 1 of this invention.

[0033] Figure 11 This is a structural block diagram of a dynamic evolution system for interparticle contact force based on DEM in Embodiment 1 of the present invention. Detailed Implementation

[0034] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0035] Example 1

[0036] A method for dynamic evolution of soil particle contact forces based on DEM (Digital Emission Model) is available in the following document: Figures 1-10 This includes the following steps:

[0037] S101 conducts discrete element simulations based on the EDEM platform, outputting key data on the interaction forces between particles in a large-scale discrete element model, including at least eight pieces of particle information: the magnitude of the contact normal force between particles, the coordinates of the contact points between particles in the X, Y, and Z directions, the particle ID, and the particle position in the X, Y, and Z directions.

[0038] Specifically, by conducting discrete element simulations based on the EDEM platform, key data on inter-particle interaction forces in a large-scale discrete element model are output. This data mainly includes eight pieces of particle information: the magnitude of the contact normal force, the coordinates of the contact points between particles in the X, Y, and Z directions, the particle ID, and the particle position in the X, Y, and Z directions. The output data is in single-column format, forming a raw particle information data material in .csv format (here referred to as PI.csv for later description).

[0039] S102 reads and calls the output data through Matlab programming algorithms to match the contact points with particle IDs, find the ID corresponding to each contact, and identify the contact particles.

[0040] Specifically, the exported data PI.csv is read and called using a Matlab programming algorithm to match the contact points with particle IDs, find the ID corresponding to each contact, and identify the contact particles.

[0041] S103 calculates the distance from the contact coordinates to the particles, finds the two particles closest to each contact point, determines them as a pair of particles connected by that contact point, traverses all contact point positions, and thus generates data for visualization and quantitative statistical analysis of different force chain types between particles.

[0042] S104 uses Matlab to calculate the average value of the interparticle contact force based on the generated interparticle force chain data. According to the average value, the force chain is divided into strong force chain, medium force chain and weak force chain, and the force chain data and distance information are statistically analyzed.

[0043] S105 uses Matlab to find the maximum and minimum normal forces, determines the normal force drawing range, divides the normal force drawing range into 256 intervals, each interval corresponds to a color, and assigns different colors, line widths and line lengths to each interval according to the magnitude of the normal force.

[0044] In this embodiment, the maximum force is assigned to red, and the minimum force is assigned to blue.

[0045] S106 draws a dot-line diagram, connects the pair of particles corresponding to each contact point in turn, and finds the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact, obtains the corresponding line width, line length and color, and assigns the three attributes of line width, line length and color to the line.

[0046] S107 iterates through all contact points, generating a total force chain network diagram, a strong force chain diagram, a second strong force chain diagram, and a weak force chain diagram. It sets the viewpoint, line thickness, color, length, frame width, and coordinate axis scaling factor of the force chain diagram, and outputs statistical analysis results of the number, average distance, and average size of different force chain types.

[0047] In this embodiment, the dynamic evolution visualization step S108 is also included:

[0048] S1081 draws the total force chain network diagram, strong force chain diagram, secondary strong force chain diagram, and weak force chain diagram at different times under the same working condition, using a time interval shorter than the preset time.

[0049] In this embodiment, the total force chain network diagram, strong force chain diagram, secondary strong force chain diagram, and weak force chain diagram are drawn at different times under the same working condition through the previous step. The time interval should be as small as possible. For example, if the total simulation time is 3s, the diagram can be drawn with a time interval of 0.01s or 0.005s. The smaller the time interval, the better the visualization effect of dynamic evolution.

[0050] The S1082 runs Matlab to automatically batch read different types of time-series force chains (.png), and sets the prefix and base playback frame rate of the output video.

[0051] S1083 extracts the time from the PNG name and uses the sort() function in Matlab to sort them by time, while filtering out images whose names do not contain time.

[0052] S1084 uses the `text` function in Matlab to add text descriptions and set titles, uses the `sprintf` function to control the presentation of numbers and text on the display interface, and uses the `writeVideo` function to write the total force chain network diagram, strong force chain diagram, second strong force chain diagram, and weak force chain image data to a file to achieve dynamic evolution visualization of interparticle contact forces.

[0053] In this embodiment, S104 further includes:

[0054] The principal stress F, characterized by the maximum stress value, is used as the criterion for determining the force chain, where F is the principal stress between particle i and its adjacent particle j:

[0055] ;

[0056] Where V is the volume of the particle. It is the component of the force acting on particle i. It is the component of the vector from the center of the adjacent particle j to the center of particle i. It is the total number of particles in contact with the particle.

[0057] A strong chain is defined as a principal stress F between particles that is greater than the average value of the interparticle contact force. <f>The second strongest chain is defined as the average interparticle contact force where F is greater than 0.5 times. <f>A weak chain is defined as a chain where F is less than or equal to 0.5 times the average interparticle contact force. <f>The force chain criterion is defined as follows:

[0058] Extract data from strong chain, medium chain, and weak chain, and statistically analyze the data and distance information of the force chains.

[0059] Based on the dynamic evolution visualization method of soil particle interaction forces using a large-scale discrete element model proposed in this invention, a case study with an initial penetration velocity of 30 m / s is used for illustration. First, the force chain calculation region near the cone tip during penetration is selected, such as... Figure 5 As shown, the selected region can be changed according to needs. This method can classify and present total force chain networks, strong, medium-strength, and weak force chain networks from different perspectives, such as... Figure 6 As shown.

[0060] It can present the evolutionary patterns of different time series of total force chain networks, strong, medium-strong, and weak force chain networks, such as... Figure 7 and Figure 8 As shown (t represents time in the figure), the total force chain network and strong force chain network between soil particles change with time, respectively. Figure 5 The evolution of force chains, as revealed by the advancement of the penetration process and the changes in the macroscopic mechanical response of the cone-soil interaction, can clearly demonstrate the network structure of the force chains.

[0061] The display area can be adjusted according to changing needs, such as... Figure 9 The diagram shows the total force chain network, strong, second-strong, and weak force chain diagrams, and statistical analysis results after changing the display area.

[0062] The above shows the presentation effects of different types of force chain diagrams obtained using this method. Based on different types of time-series force chain diagrams, dynamic evolution visualization effects can be obtained, as illustrated in the diagram. Figure 10 As shown (Time represents time), the strong chain network structure is used as an example.

[0063] In one instance, such as Figure 3 As shown, taking the case of impact penetration of soil by a penetrator based on the EDEM platform as an example, the characteristic of this case is that the discrete element geometric model is large, with the overall model being 1.0m*1.0m*0.7m. After refinement, the total number of particles is 200w+, which can meet the requirements of large-scale discrete element models.

[0064] Example 2

[0065] A method for dynamic evolution of soil particle contact forces based on DEM (Dense Earth Model) is implemented using hardware on an electronic device with a central processing unit, such as a personal computer, smart terminal, local area network, or server. For implementation details in this example, please refer to [link to relevant documentation]. Figure 11 It includes an output module 61, a contact particle identification module 62, a distance calculation module 63, a force chain data statistics module 64, a drawing range determination module 65, a drawing module 66, and a graph generation module 67.

[0066] Output module 61 is used to carry out discrete element simulation based on the EDEM platform and output key data of the interaction force between particles in the large-scale discrete element model, including at least 8 items of particle information such as the magnitude of the contact normal force between particles, the coordinates of the contact point between particles in the X / Y / Z directions, particle ID, and particle position in the X / Y / Z directions.

[0067] The contact particle identification module 62 is used to read and call the output data through Matlab programming algorithms, match the contact point with the particle ID, find the ID corresponding to each contact, and identify the contact particle.

[0068] The distance calculation module 63 is used to calculate the distance from the contact coordinates to the particles, find the two particles that are closest to each contact point, determine them as a pair of particles connected by the contact point, traverse all contact point positions, and thus generate data for visualization and quantitative statistical analysis of different force chain types between particles.

[0069] The force chain data statistics module 64 is used to run Matlab to calculate the average value of the interparticle contact force based on the generated interparticle force chain data. According to the average value, the force chain criteria are defined as strong force chain, medium-strong force chain and weak force chain, and the force chain data and distance information are statistically analyzed.

[0070] The drawing range determination module 65 is used to find the maximum and minimum normal forces by running Matlab, determine the normal force drawing range, divide the normal force drawing range into 256 intervals, each interval corresponds to a color, and assign different colors, line widths and line lengths to each interval according to the magnitude of the normal force.

[0071] The drawing module 66 is used to draw dotted line diagrams. It connects the pair of particles corresponding to each contact point in turn, and finds the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact, obtains the corresponding line width, line length and color, and assigns the three attributes of line width, line length and color to the line.

[0072] The graph generation module 67 is used to traverse all contact points and generate a total force chain network diagram, a strong force chain diagram, a second strong force chain diagram, and a weak force chain diagram. It sets the viewpoint, line thickness, color, length, frame line width, and data scale factor of the coordinate axes of the force chain diagram, and outputs the statistical analysis results of the number, average distance, and average size of different force chain types.

[0073] In this embodiment, a dynamic evolution visualization module 68 (not shown in the figure) is also included, which is used for:

[0074] Draw the total force chain network diagram, strong force chain diagram, second strongest force chain diagram and weak force chain diagram at different times under the same working condition, and draw them with time intervals shorter than the preset time.

[0075] Run Matlab to automatically batch read different types of time-series force chain diagrams (.png), and set the prefix and base playback frame rate of the output video;

[0076] Extract the time from the PNG file name and sort them by time using the sort() function in Matlab, while filtering out images whose names do not contain time information.

[0077] The `text` function in Matlab is used to add text descriptions and set titles. The `sprintf` function is used to control the presentation of numbers and text on the display interface. The `writeVideo` function writes the total force chain network diagram, strong force chain diagram, second strong force chain diagram, and weak force chain image data to a file to realize the dynamic evolution visualization of interparticle contact forces.

[0078] In this embodiment, the force chain data statistics module 64 is also used for:

[0079] The magnitude of the principal stress σ1, characterized by its maximum stress value, is selected as the criterion for determining the force chain:

[0080] ;

[0081] Where V is the volume of the particle. It is the component of the force acting on particle i. It is the component of the vector from the center of the adjacent particle j to the center of particle i. It is the total number of particles in contact with the particle.

[0082] A strong chain is defined as a principal stress F between particles that is greater than the average value of the interparticle contact force. <f>The second strongest chain is defined as the average interparticle contact force where F is greater than 0.5 times. <f>A weak chain is defined as a chain where F is less than or equal to 0.5 times the average interparticle contact force. <f>The force chain criterion is defined as follows:

[0083] Extract data from strong chain, medium chain, and weak chain, and statistically analyze the data and distance information of the force chains.

[0084] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0085] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.< / f> < / f> < / f> < / f> < / f> < / f>

Claims

1. A method for dynamic evolution of interparticle contact forces in soil based on DEM, characterized in that, include: Conduct discrete element simulations based on the EDEM platform and output key data on the interaction forces between particles in a large-scale discrete element model, including at least eight pieces of particle information: the magnitude of the contact normal force between particles, the coordinates of the contact points between particles in the X, Y, and Z directions, the particle ID, and the particle position in the X, Y, and Z directions. The output data is read and called using a Matlab programming algorithm to match the contact point with the particle ID, find the ID corresponding to each contact, and identify the contact particle. Calculate the distance from the contact coordinates to the particles, find the two particles closest to each contact point, determine them as a pair of particles connected by that contact point, and traverse all contact point positions to generate data for visualization and quantitative statistical analysis of different force chain types between particles. Based on the generated interparticle force chain data, Matlab was used to calculate the average value of the interparticle contact force. According to the average value, the force chain criteria were defined as strong force chain, medium-strong force chain and weak force chain. The force chain data and distance information were statistically analyzed. By running Matlab, the maximum and minimum normal forces were found, the normal force drawing range was determined, and the normal force drawing range was divided into 256 intervals, each interval corresponding to a color. Different colors, line widths, and line lengths were assigned to each interval according to the magnitude of the normal force. Draw a dotted line diagram, connect the pair of particles corresponding to each contact point in turn, and find the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact, obtain the corresponding line width, line length and color, and assign the three attributes of line width, line length and color to the line. Traverse all contact points to generate a total force chain network diagram, a strong force chain diagram, a second strong force chain diagram, and a weak force chain diagram. Set the viewpoint, line thickness, color, length, frame width, and coordinate axis scaling factor of the force chain diagram. At the same time, output the statistical analysis results of the number, average distance, and average size of different force chain types.

2. The method for dynamic evolution of soil particle contact force based on DEM according to claim 1, characterized in that, It also includes a dynamic evolution visualization step: Draw the total force chain network diagram, strong force chain diagram, second strongest force chain diagram and weak force chain diagram at different times under the same working condition, and draw them with time intervals shorter than the preset time. Run Matlab to automatically batch read different types of time-series force chain diagrams (.png), and set the prefix and base playback frame rate of the output video; Extract the time from the PNG file name and sort them by time using the sort() function in Matlab, while filtering out images whose names do not contain time information. The `text` function in Matlab is used to add text descriptions and set titles. The `sprintf` function is used to control the presentation of numbers and text on the display interface. The `writeVideo` function writes the total force chain network diagram, strong force chain diagram, second strong force chain diagram, and weak force chain image data to a file to realize the dynamic evolution visualization of interparticle contact forces.

3. The method for dynamic evolution of soil particle contact force based on DEM according to claim 1, characterized in that, Based on the generated interparticle force chain data, Matlab is used to calculate the average value of the interparticle contact force. Force chains are categorized into strong, medium, and weak chains according to the average value. The force chain data and distance information are statistically analyzed. This also includes: The magnitude of the principal stress σ1, characterized by its maximum stress value, is selected as the criterion for determining the force chain: ； Where V is the volume of the particle. It is the component of the force acting on particle i. It is the component of the vector from the center of the adjacent particle j to the center of particle i. It is the total number of particles in contact with the particle. A strong chain is defined as a principal stress F between particles that is greater than the average value of the interparticle contact force. <f>The second strongest chain is defined as the average interparticle contact force where F is greater than 0.5 times. <f>A weak chain is defined as a chain where F is less than or equal to 0.5 times the average interparticle contact force. <f>The force chain criterion is defined as follows:< / f> < / f> < / f> Extract data from strong chain, medium chain, and weak chain, and statistically analyze the data and distance information of the force chains.

4. A dynamic evolution system for interparticle contact forces in soil based on DEM, characterized in that, include: The output module is used to carry out discrete element simulation based on the EDEM platform and output key data of the interaction force between particles in the large-scale discrete element model. It includes at least 8 pieces of particle information, such as the magnitude of the contact normal force between particles, the coordinates of the contact point between particles in the X, Y, and Z directions, the particle ID, and the particle position in the X, Y, and Z directions. The contact particle identification module is used to read and call the output data through Matlab programming algorithms, match the contact points with particle IDs, find the ID corresponding to each contact, and identify the contact particles. The distance calculation module is used to calculate the distance from the contact coordinates to the particles, find the two particles closest to each contact point, determine them as a pair of particles connected by that contact point, traverse all contact point positions, and thus generate data for visualization and quantitative statistical analysis of different force chain types between particles. The force chain data statistics module is used to run Matlab to calculate the average value of the interparticle contact force based on the generated interparticle force chain data. According to the average value, the force chain criteria are defined as strong force chain, medium-strong force chain and weak force chain, and the force chain data and distance information are statistically analyzed. The drawing range determination module is used to find the maximum and minimum normal forces by running Matlab, determine the normal force drawing range, divide the normal force drawing range into 256 intervals, each interval corresponds to a color, and assign different colors, line widths and line lengths to each interval in order of the magnitude of the normal force; The drawing module is used to draw dotted line graphs. It connects the pair of particles corresponding to each contact point in turn, and finds the corresponding interval in 256 intervals according to the magnitude of the normal contact force corresponding to the contact. It then obtains the corresponding line width, line length and color, and assigns these three attributes to the line. The graph generation module is used to traverse all contact points and generate total force chain network diagrams, strong force chain diagrams, medium-strong force chain diagrams, and weak force chain diagrams. It allows setting the viewpoint, line thickness, color, length, frame width, and coordinate axis scaling factors of the force chain diagrams, while also outputting statistical analysis results on the number, average distance, and average size of different force chain types.

5. A dynamic evolution system for interparticle contact force in soil based on DEM according to claim 4, characterized in that, It also includes a dynamic evolution visualization module for: Draw the total force chain network diagram, strong force chain diagram, second strongest force chain diagram and weak force chain diagram at different times under the same working condition, and draw them with time intervals shorter than the preset time. Run Matlab to automatically batch read different types of time-series force chain diagrams (.png), and set the prefix and base playback frame rate of the output video; Extract the time from the PNG file name and sort them by time using the sort() function in Matlab, while filtering out images whose names do not contain time information. The `text` function in Matlab is used to add text descriptions and set titles. The `sprintf` function is used to control the presentation of numbers and text on the display interface. The `writeVideo` function writes the total force chain network diagram, strong force chain diagram, second strong force chain diagram, and weak force chain image data to a file to realize the dynamic evolution visualization of interparticle contact forces.

6. The dynamic evolution system of soil particle contact force based on DEM according to claim 4, characterized in that, The power chain data statistics module is also used for: The principal stress F, characterized by the maximum stress value, is used as the criterion for determining the force chain, where F is the principal stress between particle i and its adjacent particle j: ； Where V is the volume of the particle. It is the component of the force acting on particle i. It is the component of the vector from the center of the adjacent particle j to the center of particle i. It is the total number of particles in contact with the particle. A strong chain is defined as a principal stress F between particles that is greater than the average value of the interparticle contact force. <f>The second strongest chain is defined as the average interparticle contact force where F is greater than 0.5 times. <f>A weak chain is defined as a chain where F is less than or equal to 0.5 times the average interparticle contact force. <f> The force chain criterion is defined as follows:< / f> < / f> < / f> Extract data from strong chain, medium chain, and weak chain, and statistically analyze the data and distance information of the force chains.

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