Novel discrete element method for simulating infiltration sand particle loss

Abstract

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

Description

Technical Field

[0001] The invention belongs to the technical field of geotechnical engineering, and in particular relates to a novel discrete element method for simulating the loss of eroded sand particles. Background Art

[0002] As global climate change intensifies, extreme weather events are becoming more frequent. Heavy rains or floods often cause fine particles in the soil to migrate from the gaps between the coarse particle skeletons due to seepage forces. The loss of fine particles can lead to changes in the soil structure and pores, which in turn leads to changes in the mechanical behavior of the soil, posing new challenges to the bearing capacity and stability of geotechnical structures. Considering these issues, it is crucial to understand the impact of particle loss caused by erosion on the mechanical properties of soil.

[0003] Previous pure discrete element particle removal methods were controlled by random removal or by setting a small number of criteria. However, random removal may result in the removal of stable particles participating in the soil skeleton, while the removal method based on a small number of criteria will cause the minimum particle size of the sample to change before and after removal.

[0004] Therefore, there is an urgent need to provide a new method for simulating the loss of sand particles due to erosion, which can more accurately simulate the loss of sand particles caused by erosion to overcome the above problems. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a novel discrete element method for simulating the loss of sand particles through seepage.

[0006] This new discrete element method for simulating particle loss in eroded sand includes the following steps:

[0007] Step 1: Prepare representative unit sand samples of consolidation through discrete element simulation, and determine the gradation range of sample particles and target loss mass μ t and loss rate;

[0008] Step 2: Compare the particle stress with the average stress of all particles in the sample. Less than the average stress of all particles Then the particle belongs to the particle that can be lost by erosion; sort the loss priorities of all particles that can be lost by erosion; and construct a particle removal algorithm;

[0009] Step 3: Iterate and cycle to simulate the erosion loss process until the sample has eroded mass μ e Reach the target loss mass μ t .

[0010] Preferably, in step 2, the stress state, contact number and particle size of all loseable particles are considered, and a multi-level particle loss priority ranking system is established, with priority given to removing fine particles with lower stress, fewer contact numbers and smaller particle sizes.

[0011] Preferably, in step 2, in the loss priority sorting, the particles are first sorted according to the stress they bear; for particles with the same stress, a secondary sorting is further performed based on the number of contacts; if the stress and contact are the same, the particle size is used as the final sorting basis.

[0012] Preferably, in step 2, the calculation formula of loss priority is:

[0013]

[0014] in, is the stress value of the particle, σ max is the maximum particle stress value in the sample, N is the contact number of particles, N max is the maximum contact number of particles in the sample, D is the diameter of the particle, and D max is the maximum particle diameter, W1, W2 and W3 are weight coefficients, and W1>W2>W3, W1+W2+W3=1.

[0015] Preferably, in each iteration cycle, the mass of particles lost by erosion is accumulated while removing particles until the current step target Δμ is reached. t Or there are no particles that can be eroded and lost, a new balance is reached, and then it is determined whether to proceed to the next iterative cycle.

[0016] Preferably, the particle stress Less than the average stress of all particles Then the particle is a fine particle, otherwise it is a coarse particle; erosion only occurs in fine particles, and the loss of coarse particles forms the soil skeleton, and no erosion loss occurs.

[0017] Preferably, in step 3, the target loss mass μ set in step 1 is t As the maximum particle loss allowed by the particle loss program; and sets the current step target Δμ in each iteration cycle through the loss rate t , controls the proportion of particles removed in each cycle.

[0018] The beneficial effects of the present invention are:

[0019] 1) The present invention distinguishes fine particles that can be eroded through pure discrete element simulation, and judges whether they are erodible by comparing the particle stress size with the mean value, so as to control the particle loss in the fine particles and avoid the error caused by the loss of the soil skeleton; and further proposes a quantification method for loss priority, sorts the particles that can be lost according to the priority to determine the order of fine particles to be removed in each cycle, so that the loss of eroded sand particles simulated by the discrete element method is more in line with the actual situation, and provides accurate data support for the subsequent research on the mechanical properties of soil.

[0020] 2) The present invention combines the newly proposed particle removal algorithm with the expected loss mass as the goal, and establishes a new particle loss model after erosion based on the pre-placed sand sample to be eroded; it realizes the automated simulation of the particle loss of the sand sample erosion, and quickly and accurately obtains the sand sample with particle loss after erosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a novel discrete element method for simulating the loss of sand particles through seepage according to the present invention;

[0022] Figure 2 It is a cloud diagram of representative volume units and particle parameters of the un-eroded sand sample used in the present invention;

[0023] Figure 3 is a gradation diagram of simulated erosion particle loss of the present invention;

[0024] Figure 4 is a graph showing the research results of the present invention and the prior art in simulating the loss of eroded particles;

[0025] Figure 5 It is a particle size distribution diagram for simulating different target loss amounts using the method of the present invention;

[0026] Figure 6 It is a stress diagram of cyclic simple shearing of the specimens after simulating different target loss amounts by the method of the present invention;

[0027] Figure 7 It is a strain diagram of cyclic simple shearing of samples after simulating different target loss amounts using the method of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0029] Embodiment 1

[0030] As an example, Figure 1 As shown in Figure 1, this new discrete element method for simulating the loss of sand particles through erosion includes the following steps:

[0031] Step 1: Run the program file of this method through Yade; select the representative unit sand sample prepared by discrete element simulation and determine the following parameters: the gradation range of the sample particles, the target loss mass μ t , loss rate; set the target loss mass μ t The maximum particle loss in the particle loss program is controlled; the loss rate controls the proportion of particles removed in each cycle.

[0032] Determine the parameters based on the sand sample to be eroded: Since erosion only causes loss of fine particles in the soil, the coarse particles that make up the soil skeleton are considered to be non-lost. Therefore, determining the gradation of the sample helps to control the particle loss to occur only in fine particles; set the target loss mass to control the maximum particle loss of the particle loss program; the loss rate controls the proportion of particles removed in each cycle.

[0033] Step 2: Perform particle removal algorithm; through particle stress The mean stress of all particles in the sample By comparison, the particles that can be lost by erosion can be distinguished, so determining the gradation of the sample helps to control the particle loss to occur only in fine particles.

[0034]

[0035] Where σ p is the stress tensor of the particle, is the contact force vector, is the vector connecting the particle center to the contact point, k represents the calculated contact on particle p, V p is the volume of the calculated particle, where N p is the number of all particles in the sand sample. and The relative size of the particles determines whether they can be lost due to erosion. Less than When , this particle is a fine particle and can be removed, otherwise it is a coarse particle, which is part of the soil skeleton and cannot be lost. In this embodiment, the algorithm is written in Python 3.10.12 programming language and runs in Yade 2022.01a.

[0036] Step 3: Perform an iterative cycle. Each cycle accumulates the mass of particles that have been eroded while removing particles until the target of the current cycle is reached or there are no particles that can be eroded. Finally, a new balance is reached and the iteration is completed.e , determine the sample erosion mass μ e Whether the target loss mass μ is achieved t , if not reached, continue iterating.

[0037] After each cycle of particle removal algorithm is completed, the program automatically outputs a data package that saves all simulation information of the current sand sample, which can be used for further research. Establish a particle loss model after erosion.

[0038] Embodiment 2

[0039] As another embodiment, this embodiment 2 proposes, based on the embodiment 1, a more specific new discrete element method for simulating the loss of eroded sand particles:

[0040] In step 2, a multi-level particle loss priority ranking system was established by comprehensively considering the stress state, contact number and particle size of all the particles that can be lost. This system gives priority to removing fine particles with lower stress, fewer contact numbers and smaller particle sizes. The ranking process is as follows:

[0041] First, the particles are mainly sorted according to the magnitude of stress they are subjected to;

[0042] Secondly, for particles with the same stress, they are further ranked according to their contact numbers;

[0043] Finally, if stress and contact are the same, particle size is used as the final sorting basis.

[0044] This multi-level ranking method ensures a comprehensive and accurate priority evaluation of all particles in the sample, thereby achieving the most optimized particle removal order. In order to more accurately quantify the removal priority of particles, the quantitative formula is proposed as follows:

[0045]

[0046] Among them, P is the removal priority of particles. The higher the value, the higher the removal priority. is the stress value of the particle, σ max is the maximum particle stress value in the sample, N is the contact number of particles, N max is the maximum contact number of particles in the sample, D is the diameter of the particle, and D max is the maximum particle diameter, W1, W2 and W3 are weight coefficients, indicating the relative importance of each factor, and W1>W2>W3, W1+W2+W3=1.

[0047] It should be noted that the parts in this embodiment that are the same or similar to those in the first embodiment can be referenced to each other and will not be described in detail in this application.

[0048] Embodiment 3

[0049] As another embodiment, this embodiment 3 proposes, based on the first and second embodiments, to use this new discrete element method for simulating the loss of eroded sand particles to simulate the loss of eroded sand particles:

[0050] In this embodiment, Figure 2 As shown in the figure, the sand sample adopts discontinuous grading, the coarse particles adopt non-spherical particles, the particle size range is 1.04-1.24mm, the fine particles range is 0.17-0.26mm, and the side length of the sample after consolidation is 12.4mm, which is 10 times the maximum particle size of the coarse particles. The sample contains a total of 60,000 particles, including 1459 coarse particles and 58,541 fine particles, which meet the specified conditions of the representative unit. The use of non-spherical particles for coarse particles can simulate more realistic soil particles. At the same time, the size of fine particles is very small compared to coarse particles. The use of spherical particles will not affect the migration of fine particles, and at the same time reduce the calculation cost.

[0051] Figure 3 The particle size distribution diagram for simulating different target loss amounts using the method of the present invention is well consistent with the research results of Hu et al. (2020) and Xiong et al. (2021) using CFD-DEM to simulate the fine particle loss caused by erosion. Figure 4 As shown in the figure, the particle size range has not changed, which verifies the reliability of the method of the present invention. It also shows that compared with the previous pure discrete element particle removal method (Muir Wood et al., 2010; Scholtès et al., 2010; Zhang et al., 2018), the simulation results of this method are more consistent with the actual situation.

[0052] Figure 5 The method of the present invention is used to take a snapshot of the particle loss of the sand sample. From the results, it can be seen that no matter how large the proportion of fine particle loss is, there are still fine particles with the smallest particle size represented by dark blue in the sample. The simulation results of the present invention are in good agreement with the existing research results. The particle size range has not changed, and the particles with the smallest particle size still exist, which further verifies the reliability of the present method. At the same time, the use of pure discrete element simulation of erosion particle loss provides a theoretical basis and technical support.

[0053] Figure 5 In order to simulate the particle grading diagram of different target loss amounts using the method of the present invention, the particle size range remains unchanged, which further verifies the reliability of the method of the present invention. At the same time, it provides a theoretical basis and technical support for simulating erosion particle loss using pure discrete elements.

[0054] Figure 6 and Figure 7The stress-strain diagram of cyclic single shear of samples after simulating different target loss amounts using the method of the present invention is shown. As the number of cycles increases, the average effective stress of the sample continues to decrease until the sample is completely liquefied. The shear strain increases significantly when it is close to complete liquefaction, showing a typical cyclic liquefaction pattern. After a large amount of fine particles are lost, the failure mode changes and turns into flow failure. During the cyclic loading process, although the effective stress gradually decreases, no obvious deformation occurs in the initial stage. However, in the last half cycle of loading, sudden failure occurs, which is characterized by uncontrolled deformation and complete strength loss. For samples that have not been lost, the cyclic single shear results of the method of the present invention are consistent with a large number of existing test and simulation results.

[0055] It should be noted that the parts in this embodiment that are the same or similar to those in the first embodiment can be referenced to each other and will not be described in detail in this application.

[0056] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

Claims

1. A novel discrete element method for simulating the loss of sand particles through erosion, characterized in that: The following steps are involved: Step 1: Prepare representative unit sand samples of consolidation through discrete element simulation, and determine the gradation range of sample particles and target loss mass μ t and loss rate; Step 2: Compare the particle stress with the average stress of all particles in the sample. Less than the average stress of all particles Then the particle belongs to the particle that can be lost by erosion; sort the loss priorities of all particles that can be lost by erosion; and construct a particle removal algorithm; Step 3: Iterate and cycle to simulate the erosion loss process until the sample has eroded mass μ e Reach the target loss mass μ t .

2. The novel discrete element method for simulating the loss of sand particles according to claim 1 is characterized in that: In step 2, the stress state, contact number and particle size of all loseable particles are considered, and a multi-level particle loss priority ranking system is established, giving priority to removing fine particles with lower stress, fewer contact numbers and smaller particle sizes.

3. The novel discrete element method for simulating the loss of sand particles according to claim 1 is characterized in that: In step 2, in the loss priority sorting, the particles are first sorted according to the stress they are subjected to; for particles with the same stress, they are further sorted according to the number of contacts; if the stress and contact are the same, the particle size is used as the final sorting basis.

4. The novel discrete element method for simulating the loss of sand particles according to claim 1 is characterized in that: In step 2, the loss priority is calculated as: in, is the stress value of the particle, σ max is the maximum particle stress value in the sample, N is the contact number of particles, N max is the maximum contact number of particles in the sample, D is the diameter of the particle, and D max is the maximum particle diameter, W1, W2 and W3 are weight coefficients, and W1>W2>W3, W1+W2+W3=1.

5. The novel discrete element method for simulating the loss of sand particles according to claim 1 is characterized in that: In each iteration cycle, the mass of particles lost by erosion is accumulated while removing particles until the current step target Δμ is reached. t Or there are no particles that can be eroded and lost, a new balance is reached, and then it is determined whether to proceed to the next iterative cycle.

6. The novel discrete element method for simulating the loss of sand particles according to claim 1 is characterized in that: Particle stress Less than the average stress of all particles Then the particle is a fine particle, otherwise it is a coarse particle; erosion only occurs in fine particles, and the loss of coarse particles forms the soil skeleton, and no erosion loss occurs.

7. The novel discrete element method for simulating the loss of sand particles by erosion according to claim 1 is characterized in that: In step 3, the target loss mass μ set in step 1 is t As the maximum particle loss allowed by the particle loss program; and sets the current step target Δμ in each iteration cycle through the loss rate t , controls the proportion of particles removed in each cycle.

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