A modeling method and system for discrete particle samples with arbitrary gradation

Abstract

The present invention relates to the technical field of discrete element modeling and simulation, and provides a modeling method and system for a discrete particle sample of arbitrary gradation, including: for a radius interval greater than a threshold value, based on the calculated number of particles in the radius interval, randomly generate particles in order of the radius interval from large to small, and put them into a closed shape; for a radius interval less than a threshold value, based on the calculated number of particles in the radius interval, in order of the radius interval from large to small, generate particles in a cluster manner in order, and put them into a closed shape; apply supergravity sedimentation or compressive stress that decreases linearly with time to the particles in the closed shape, and release the internal stress at the same time, to obtain a particle distribution model. The density of sample filling is improved, the specified porosity is reached faster, and the efficiency of sample preparation is effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of discrete element modeling and simulation, and in particular relates to a modeling method and system for a discrete particle sample with arbitrary gradation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, discrete element modeling and simulation is a popular research method for rock mass structure systems in civil engineering. It effectively overcomes the fact that simulation methods based on continuous medium mechanics (such as finite element method and finite difference method) cannot simulate the discreteness, deformation and cracking of rock and soil. Because the discrete element modeling process has a great influence on the subsequent experimental results, the first step of discrete element modeling, sample preparation, is crucial.

[0004] Although the current sample preparation methods are relatively mature, none of the existing methods have the function of preparing soil samples that can take into account both continuous grading and discontinuous grading; the samples generated by the radius expansion method are significantly different from the target samples; the compaction method is time-consuming and inefficient; and the samples generated by the one-time generation method are uneven. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a modeling method and system for discrete particle samples of arbitrary gradation, adopts a method of randomly generating particles in the order of particle radius from large to small, and proposes a cluster placement method, which improves the density of sample filling, reaches the specified porosity more quickly, and effectively improves the efficiency of sample preparation.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a modeling method for a discrete particle sample of arbitrary gradation, comprising:

[0008] Obtain model range, particle porosity and particle grading curve;

[0009] Generate any closed shape according to the model range;

[0010] According to the model range, particle porosity and particle grading curve, the total number of particles is calculated, and after obtaining several radius intervals according to the particle grading curve, the number of particles in each radius interval is calculated in combination with the total number of particles;

[0011] For radius intervals greater than the threshold, based on the calculated number of particles in the radius interval, particles are randomly generated in order from large to small radius intervals and placed into the closed shape;

[0012] For radius intervals smaller than the threshold, based on the calculated number of particles in the radius interval, particles are generated in clusters in descending order of radius intervals and placed into closed shapes;

[0013] The particles in the closed shape are subjected to high gravity sedimentation or compressive stress that decreases linearly with time, while the internal stress is released to obtain a particle distribution model.

[0014] Furthermore, the total number of particles is:

[0015]

[0016] Where S is the model range, n is the particle porosity, R max and R min are the maximum radius and the minimum radius of the particle respectively.

[0017] Furthermore, the distribution form of the particle radius in each radius interval is: uniform distribution, exponential distribution or normal distribution.

[0018] Furthermore, if the particle grading curve is a continuous grading, a method of evenly dividing the radius interval is adopted; if the particle grading curve is a discontinuous grading, the radius interval is divided according to the position of the discontinuity point and the characteristic radius.

[0019] Furthermore, the specific method for randomly generating particles is: for a certain radius interval, a coordinate position of a particle is specified within the radius interval using a random function, and whether it overlaps with an existing particle is detected. If not, the particle is directly generated; if so, the coordinate position of the particle is respecified until it does not overlap with the existing particle.

[0020] Furthermore, the specific method of generating particles in a cluster manner is as follows: for a certain radius interval, the particles in the radius interval are generated by hexagonal closest packing and formed into clusters, and it is detected whether the cluster particles overlap with existing particles. If so, the overlapping particles in the cluster particles are deleted, and the other particles are retained and placed in a closed shape; if not, all of them are retained and placed in a closed shape.

[0021] Furthermore, in the process of generating particles in a clustering manner, if the porosity reaches a given porosity, the generation and addition of particles are stopped.

[0022] A second aspect of the present invention provides a modeling system for a discrete particle sample of arbitrary gradation, comprising:

[0023] A data acquisition module is configured to: acquire a model range, a particle porosity, and a particle grading curve;

[0024] A closed shape generation module is configured to: generate an arbitrary closed shape according to the model range;

[0025] A particle number calculation module is configured to: calculate the total number of particles according to the model range, particle porosity and particle grading curve, and after obtaining a number of radius intervals according to the particle grading curve, calculate the number of particles in each radius interval in combination with the total number of particles;

[0026] A first particle generation module is configured to: for a radius interval greater than a threshold, based on the calculated number of particles in the radius interval, randomly generate particles in order from large to small radius intervals, and place them into a closed shape;

[0027] The second particle generation module is configured to: for a radius interval less than a threshold, based on the calculated number of particles in the radius interval, generate particles in a cluster manner in descending order of the radius intervals, and place the particles into a closed shape;

[0028] The model generation module is configured to: apply hypergravity sedimentation or compressive stress that decreases linearly with time to particles in the closed shape, and release the internal stress at the same time, so as to obtain a particle distribution model.

[0029] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned method for modeling a discrete particle sample of arbitrary gradation.

[0030] The fourth aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for modeling a discrete particle sample of arbitrary gradation as described above are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention provides a modeling method for a discrete particle sample of arbitrary gradation. Compared with other preparation methods, the modeling method can be simultaneously applicable to the preparation of samples of arbitrary gradation including continuous gradation and discontinuous gradation, and has a wide application range.

[0033] The present embodiment provides a modeling method for a discrete particle sample of arbitrary gradation, which adopts a method of randomly generating particles in order of particle radius from large to small. This can effectively solve the problems of difficulty and time-consuming trial placement caused by the traditional method of randomly generating particles of different radii, thereby speeding up the preparation of samples.

[0034] This embodiment provides a modeling method for a discrete particle sample of arbitrary gradation, and the cluster placement method proposed therein can increase the density of sample filling, reach the specified porosity more quickly, and effectively improve the efficiency of sample preparation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 It is a flow chart of a modeling method of a discrete particle sample of arbitrary gradation according to the first embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of trial casting of particles within the first 50% to 90% radius range of Example 1 of the present invention;

[0038] Figure 3 It is a schematic diagram of the placement of particle clusters within the last 50% to 10% radius range of the first embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] Embodiment 1

[0042] In view of the problem that it is time-consuming or difficult to meet the expected requirements for preparing a specified particle gradation and porosity during discrete element modeling, this embodiment provides a modeling method for a discrete particle sample with arbitrary gradation, such as Figure 1 As shown, the specific steps include:

[0043] Step 1: Determine the model generation range S according to the target requirements. The model range is not limited to a rectangle and can be any closed shape.

[0044] Step 2: According to the model range S, particle porosity n, particle grading curve (particle maximum radius R max The minimum particle radius R min ), the total number of estimated particles N, where

[0045] Step 3: According to the control point of the grading curve (the maximum particle radius R max The minimum particle radius R min) Divide the soil sample into m (0≤m≤20) radius intervals, and sort the radius intervals in descending order; and calculate the number of particles Q in m different radius intervals according to the grading curve i (Specifically, the difference in particle content percentage corresponding to the mth radius interval on the grading curve multiplied by the estimated total number of particles N can be obtained to obtain the number of particles Q in m different radius intervals. i ), i = 1, 2, ..., m. The control point refers to the point on the grading curve corresponding to the maximum radius and the minimum radius of the particle.

[0046] The distribution form of the particle radius in each radius interval can be specified, such as uniform distribution, exponential distribution, and normal distribution. The basis for dividing the radius interval is: for components with continuous grading (i.e., the particle grading curve is continuous grading), the radius interval can be divided uniformly; for components with discontinuous grading (i.e., the particle grading curve is discontinuous grading), the radius interval can be reasonably divided according to the position of the discontinuity point and the characteristic radius. Among them, the characteristic radius refers to: a certain particle radius in the discontinuous grading is used as the characteristic radius of the component classification.

[0047] Step 4: For radius intervals greater than the threshold, random particles are generated and released in order from large to small radius intervals.

[0048] Specifically, for the first 50% to 90% of the radius interval, after executing steps 401-403, the particles within the first 50% to 90% of the radius interval are randomly placed from large to small according to the radius interval, in order to form a material skeleton, as shown in the schematic diagram Figure 2 shown.

[0049] Step 401: In the first radius interval, the x-coordinate and y-coordinate of the first large particle are specified by a random function to generate the first large particle; the x-coordinate and y-coordinate of the second large particle are specified by a random function to generate the second large particle; and so on, until the number of particles in the first radius interval reaches Q1; the radius of each randomly generated particle is determined by a random function, which uses the left and right endpoints of the radius interval as upper and lower bounds to generate Q i A random number is used to determine the radius, and the random number can be a random number that obeys uniform distribution, a random number that obeys linear distribution, or a random number that obeys exponential distribution, and is used as the radius of all particles in the interval.

[0050] Step 402: within the i-th (i=1, 2, 3, ... m) radius interval, use a random function to specify the x-coordinate and y-coordinate of a large particle, and detect whether there are other generated particles within the radius of the particle (i.e., detect whether they overlap with existing particles); if not, directly generate particles and put them into a closed shape; if so, re-specify the x-coordinate and y-coordinate of the large particle until it does not overlap with existing particles.

[0051] Step 403: Repeat step 402 to generate all particles that should be generated within the i-th radius interval (i.e., the number of particles within the i-th radius interval reaches Q i ), the loop stops.

[0052] Step 404, let i=i+1, determine whether the i-th radius interval is greater than the threshold, if so, return to step 402; otherwise, go to step 5.

[0053] Step 5: For radius intervals smaller than the threshold, based on the number of particles in each radius interval, particles are generated in a clustering manner in order from large to small radius intervals, and are placed into a closed shape.

[0054] Specifically, for particles within the last 50% to 10% of the radius range, a cluster delivery method is adopted in order of radius range from large to small.

[0055] Step 501: For the i-th radius interval, the small particles in the radius interval are generated by the hexagonal closest packing method, and the generated a closely arranged small particles form a cluster, which is tested as a whole. Check whether the cluster particles overlap with the existing particles: if so, delete the overlapping particles in the cluster particles, keep the other particles, and put them into a closed shape; if not, keep all of them and put them into a closed shape, as shown in the schematic diagram. Figure 3 shown.

[0056] Step 502: Repeat step 501 to generate all particles that should be generated within the i-th radius interval (i.e., the number of particles within the i-th radius interval reaches Q i ), the loop stops.

[0057] Step 503, calculate whether the porosity n1 of the generated sample is equal to the given porosity n, if so, stop the placement process; if not, set i=i+1 and continue the placement process of steps 501 and 502.

[0058] Step 6: Apply ultra-gravity sedimentation or compressive stress of 10 to 1000G, which decreases linearly with time, to all generated particles to make the particle arrangement more compact. At the same time, release the internal stress inside the sample to allow the particles to fully rebound. When the particle movement speed is close to 0, the force applied to the particles is reduced to G (gravitational acceleration, generally equal to 9.8m / s2). The particle distribution model obtained at this time is the desired particle structure model.

[0059] The modeling method of a discrete particle sample of arbitrary gradation provided in this embodiment can be applied to the preparation of samples of arbitrary gradation including continuous gradation and discontinuous gradation, compared with other preparation methods, and has a wide range of applications.

[0060] The present embodiment provides a modeling method for a discrete particle sample of arbitrary gradation, which adopts a method of randomly generating particles in order of particle radius from large to small. This can effectively solve the problems of difficulty and time-consuming trial placement caused by the traditional method of randomly generating particles of different radii, thereby speeding up the preparation of samples.

[0061] This embodiment provides a modeling method for a discrete particle sample of arbitrary gradation and a cluster placement method, which can increase the sample filling density, reach the specified porosity more quickly, and effectively improve the efficiency of sample preparation.

[0062] Embodiment 2

[0063] This embodiment provides a modeling system for a discrete particle sample of arbitrary gradation, which specifically includes:

[0064] A data acquisition module is configured to: acquire a model range, a particle porosity, and a particle grading curve;

[0065] A closed shape generation module is configured to: generate an arbitrary closed shape according to the model range;

[0066] A particle number calculation module is configured to: calculate the total number of particles according to the model range, particle porosity and particle grading curve, and after obtaining a number of radius intervals according to the particle grading curve, calculate the number of particles in each radius interval in combination with the total number of particles;

[0067] A first particle generation module is configured to: for a radius interval greater than a threshold, based on the calculated number of particles in the radius interval, randomly generate particles in order from large to small radius intervals, and place them into a closed shape;

[0068] The second particle generation module is configured to: for a radius interval less than a threshold, based on the calculated number of particles in the radius interval, generate particles in a cluster manner in descending order of the radius intervals, and place the particles into a closed shape;

[0069] The model generation module is configured to: apply hypergravity sedimentation or compressive stress that decreases linearly with time to particles in the closed shape, and release the internal stress at the same time, so as to obtain a particle distribution model.

[0070] It should be noted here that the various modules in this embodiment correspond one-to-one to the various steps in Example 1, and the specific implementation process is the same, which will not be repeated here.

[0071] Embodiment 3

[0072] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the method for modeling a discrete particle sample of arbitrary gradation as described in the first embodiment above are implemented.

[0073] Embodiment 4

[0074] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the modeling method of a discrete particle sample of arbitrary gradation as described in the above-mentioned embodiment 1 are implemented.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A modeling method for a discrete particle sample of arbitrary gradation, characterized in that: include: Obtain model range, particle porosity and particle grading curve; Generate any closed shape according to the model range; According to the model range, particle porosity and particle grading curve, the total number of particles is calculated, and after obtaining several radius intervals according to the particle grading curve, the number of particles in each radius interval is calculated in combination with the total number of particles; For radius intervals greater than the threshold, based on the calculated number of particles in the radius interval, particles are randomly generated in order from large to small radius intervals and placed into the closed shape; For radius intervals smaller than the threshold, based on the calculated number of particles in the radius interval, particles are generated in clusters in descending order of radius intervals and placed into closed shapes; The particles in the closed shape are subjected to high gravity sedimentation or compressive stress that decreases linearly with time, while the internal stress is released to obtain a particle distribution model.

2. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: The total number of particles is: Where S is the model range, n is the particle porosity, R max and R min are the maximum radius and the minimum radius of the particle respectively.

3. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: The distribution form of particle radius in each radius interval is: uniform distribution, exponential distribution or normal distribution.

4. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: If the particle grading curve is a continuous grading, the method of evenly dividing the radius interval is adopted; if the particle grading curve is a discontinuous grading, the radius interval is divided according to the position of the discontinuity point and the characteristic radius.

5. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: The specific method of randomly generating particles is: for a certain radius interval, a coordinate position of a particle is specified within the radius interval using a random function, and whether it overlaps with an existing particle is detected. If not, the particle is directly generated; if so, the coordinate position of the particle is re-specified until it does not overlap with the existing particle.

6. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: The specific method of generating particles by clustering is as follows: for a certain radius interval, the particles in the radius interval are generated by hexagonal closest packing and formed into clusters, and it is detected whether the cluster particles overlap with the existing particles. If so, the overlapping particles in the cluster particles are deleted, and the other particles are retained and placed in a closed shape; if not, all of them are retained and placed in a closed shape.

7. A modeling method for a discrete particle sample of arbitrary gradation as claimed in claim 1, characterized in that: In the process of generating particles by clustering, if the porosity reaches a given porosity, the generation and placement of particles are stopped.

8. A modeling system for discrete particle samples of arbitrary gradation, characterized in that: include: A data acquisition module is configured to: acquire a model range, a particle porosity, and a particle grading curve; A closed shape generation module is configured to: generate an arbitrary closed shape according to the model range; A particle number calculation module is configured to: calculate the total number of particles according to the model range, particle porosity and particle grading curve, and after obtaining a number of radius intervals according to the particle grading curve, calculate the number of particles in each radius interval in combination with the total number of particles; A first particle generation module is configured to: for a radius interval greater than a threshold, based on the calculated number of particles in the radius interval, randomly generate particles in order from large to small radius intervals, and place them into a closed shape; The second particle generation module is configured to: for a radius interval less than a threshold, based on the calculated number of particles in the radius interval, generate particles in a cluster manner in descending order of the radius intervals, and place the particles into a closed shape; The model generation module is configured to: apply hypergravity sedimentation or compressive stress that decreases linearly with time to particles in the closed shape, and release the internal stress at the same time, so as to obtain a particle distribution model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the modeling method of a discrete particle sample of arbitrary graded distribution as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for modeling a discrete particle sample of arbitrary graded distribution as described in any one of claims 1-7 are implemented.

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