A method for constructing a compaction model for coarse-grained material compaction

Abstract

This invention discloses a method for constructing a compaction model for coarse-grained materials. The method involves a process of prototype replication, refinement adjustment, and parameter calibration to build the compaction model. The model is then compared with field experiments to verify its simulation effectiveness, and is used to study the compaction process of gravel. Based on typical real-world gravel compaction tests, this invention establishes a PFC numerical compaction model. Through systematic comparison, micro-parameters are adjusted and calibrated, resulting in a compaction model that balances computational accuracy and efficiency. This transforms numerical simulation from qualitative to quantitative analysis, effectively compensating for limitations imposed by field conditions and enabling further research on the vibration compaction characteristics of gravel.

Description

Technical Field

[0001] This invention relates to the field of compaction model construction technology, and more specifically to a method for constructing a compaction model for coarse-grained material compaction. Background Technology

[0002] With the rapid development of high dam construction in my country towards the 300m level, rockfill dams have become the preferred dam type for water conservancy projects due to their excellent terrain adaptability and economic efficiency. The compaction quality of the coarse-grained fill, the main body of the rockfill dam, directly determines the deformation stability and seepage safety of the dam body. However, as a typical discontinuous medium material, the compaction characteristics of coarse-grained material are influenced by the coupling of multiple scale factors such as gradation characteristics, particle morphology, and microscopic contact parameters. Traditional compaction quality control methods based on continuous medium theory are insufficient to accurately reveal the intrinsic mechanisms of particle redistribution and force chain evolution during the compaction process.

[0003] In the field of coarse-grained material compaction characteristics research, large-scale field compaction tests remain the primary means of obtaining compaction parameters. However, these tests suffer from inherent drawbacks such as long cycles, high costs, and discrete parameter control. With the development of numerical simulation technology, the discrete element method (DEM) has gradually become an effective tool for studying the macroscopic and microscopic mechanical properties of granular materials due to its ability to accurately describe the contact mechanics between particles. Compared to the traditional finite element method, the particle flow program (PFC) can dynamically reproduce key processes such as particle displacement, contact force network evolution, and porosity changes during compaction by constructing a microscopic mechanical model of the particle assembly. It exhibits unique advantages in simulating large deformation and discontinuous failure of granular materials and has been widely applied.

[0004] However, there are still many problems in building the PFC-based compaction model. First, the coarse-grained aggregates in engineering gradations typically encompass various particle groups, including clay particles ≥ 0.005 mm, silt particles ≥ 0.075 mm and > 0.005 mm, sand particles ≥ 2 mm and > 0.075 mm, gravel particles ≥ 60 mm and > 2 mm, pebbles ≥ 200 mm and boulders > 200 mm, with maximum particle sizes potentially reaching 600 mm or even exceeding 1000 mm. Direct modeling would result in ultra-large-scale particle systems, making it difficult to balance computational efficiency and accuracy. Second, the mapping relationship between microscopic parameters such as stiffness coefficient and friction coefficient and macroscopic mechanical response is complex, and the microscopic contact parameters differ for coarse-grained aggregates of different lithologies. Existing research largely relies on empirical values ​​and lacks systematic calibration methods. Third, traditional mill wheel simulations cannot reflect the kneading effect through scientific microscopic parameter calibration and have low computational efficiency. Furthermore, the applicability of different methods in terms of geometric model construction, measurement arrangement, and three-dimensional data conversion still needs to be compared and verified. The aforementioned problems restrict the application of the discrete element method in compaction simulation. This invention provides a method for constructing a compaction model for coarse-grained material compaction to solve these problems. Summary of the Invention

[0005] This invention provides a method for constructing a compaction model for coarse-grained material compaction. Taking a typical sand and gravel compaction test as the research object, the compaction model is constructed using PFC. The refinement parameters of the roller model and the contact parameters of the sample can be calibrated according to the application object, realizing numerical simulation of the compaction test and obtaining accurate simulation data.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] A method for constructing a compaction model for coarse-grained material compaction includes the following steps:

[0008] S1, Building the initial model: Constructing a density barrel model, setting contact parameters, and constructing a rolling mill model;

[0009] S2, Determine contact parameters: Determine and select the set contact parameters to complete the construction of the compaction model;

[0010] S3, Adjust model refinement parameters: Compare the model refinement parameters in the compaction simulation and select different refinement parameters according to different working conditions;

[0011] Detailed parameters include density barrel compaction range, particle drop pattern, and compaction method;

[0012] S4, Compaction Model Validation: After the compaction model is constructed, numerical simulation is performed and compared with field experiments to verify the effectiveness of the compaction model. The validated model is then used for simulation analysis of coarse aggregate compaction.

[0013] Furthermore, in step S1, the initial model construction includes the following steps:

[0014] S11, Constructing a density barrel model: Construct a density barrel according to the on-site experimental model, generate particles with a certain initial porosity according to the set gradation particles, and the generated particles reach an equilibrium state through mutual interaction.

[0015] S12, Set contact parameters: Set contact parameters by selecting a set of standard values ​​as the initial contact parameters;

[0016] S13, Construct the millstone model: Generate the millstone model and set the relevant parameters of the millstone model.

[0017] Further, in step S12, a linear stiffness model is selected to simulate the contact parameters. In the linear stiffness model, the contact between particles is a spring connection, and the contact force consists of linear force and damping force. The linear force is generated by the normal stiffness kn and the tangential stiffness ks, which are determined by the following formula:

[0018] ,

[0019] ,

[0020] In the formula, (1) and (2) represent two particles in contact with each other. and The normal stiffness (N / m) of particles (1) and (2) are respectively. and The tangential stiffness (N / m) of particles (1) and (2) are respectively.

[0021] When particles come into contact, the sliding condition is satisfied by applying a coefficient of friction fric between the particles.

[0022] Furthermore, in step S2, determining the contact parameters includes determining the friction coefficient fric parameter, determining the stiffness parameter, and determining the parameters under the interaction of the friction coefficient fric and stiffness:

[0023] S21, When determining the friction coefficient fric parameter, the specific value of the friction coefficient fric is determined based on its influence on particle overlap and its influence on the compaction effect.

[0024] S22, When determining the stiffness parameters, after selecting a suitable friction coefficient fric, the parameters of normal stiffness kn and tangential stiffness ks are determined based on the different maximum dry densities after rolling simulation.

[0025] S23. When determining parameters by comprehensively considering the mutual influence of friction coefficient fric and stiffness, first select friction coefficient fric, then set different proportional coefficients between normal stiffness kn and tangential stiffness ks, select a suitable proportional coefficient after rolling simulation, and set multiple sets of friction coefficient fric under this proportional coefficient to carry out rolling simulation and select a suitable friction coefficient fric.

[0026] Furthermore, in step S13, a large circular roller is generated, and vibratory compaction is performed using a reciprocating motion. Parameters for the roller's mass, excitation force, horizontal velocity, and rotational angular velocity are set.

[0027] The rotational angular velocity of the grinding wheel is determined by the following formula:

[0028] ,

[0029] In the formula, denoted as σr, where σr is the horizontal velocity of the mill wheel (m / s); r is the radius of the mill wheel; and w is the angular velocity of the mill wheel (rad / s).

[0030] During vibratory compaction, the forces acting on the particles are the weight of the roller and the excitation force. The main driving force of the roller is determined by the following formula:

[0031] ,

[0032] In the formula, G is the weight of the millstone (N); F max ω is the peak value of the excitation force (N); ω is the eccentric rotational velocity of the roller (rad / s); t is the rolling time (s).

[0033] Furthermore, in step S3, when refining the compaction method, a vibrating plate with equivalent load is used instead of a roller. The vibrating plate is composed of multiple small circular vibrating short plates stacked in rows.

[0034] The equation of motion for the vibrating plate is:

[0035] ,

[0036] In the formula, M is the system mass; e is the vibration eccentricity; S is the displacement; g is the gravitational acceleration; ω1 is the vibration source frequency; The critical angle for takeoff is t; the duration of vibration is t.

[0037] The equivalent replacement calculation formula for vibratory compaction is:

[0038] ,

[0039] In the formula, E represents the total energy. d For kinetic energy; E s Potential energy; E c Impact energy;

[0040] The equivalent compaction process of the vibrating plate is as follows: each vibrating short plate vibrates in sequence, the direction of vibration transmission is consistent with the direction of travel of the compaction machine, all vibrating short plates vibrate up and down in a wave-like state to compact, and when all vibrating short plates complete one loading cycle, one compaction operation is completed.

[0041] The formula for calculating the improvement in compaction efficiency after using a vibrating plate is as follows:

[0042] ,

[0043] In the formula, n is the factor that increases the compaction efficiency; T is the vibration period; v is the speed of the roller; and d is the diameter of each small circular vibrating plate of the equivalent vibrating plate.

[0044] Furthermore, in step S3, when determining the compaction range of the density barrel, the compaction range is selected based on the working conditions, either by setting a transition zone outside the density barrel or by using a single density barrel.

[0045] Furthermore, in step S3, when determining the particle falling method, the method is selected based on the working conditions: direct generation in the density tank, natural particle falling, or particle falling through a funnel.

[0046] The beneficial effects of this invention are as follows:

[0047] Based on real-world typical sand and gravel compaction tests, a PFC numerical compaction model was built. The micro-parameters were adjusted and calibrated through systematic comparison, and a compaction model that balances computational accuracy and efficiency was constructed. This transformed the numerical simulation from qualitative analysis to quantitative analysis, effectively compensating for the shortcomings caused by field conditions and enabling further research on the vibration compaction characteristics of sand and gravel.

[0048] The impact of different refinement methods on calculation accuracy and efficiency is compared in the compaction model. Different refinement adjustments are made according to different situations, which are suitable for qualitative analysis or quantitative calculation. For different types of samples, the contact parameters are adapted to make the compaction model applicable to different types of samples. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0050] Figure 2 Here is a table of the composition of the representative graded sample of this invention;

[0051] Figure 3 This is the initial model data table for the present invention;

[0052] Figure 4 Here is a table of conventional contact parameter values ​​for this invention;

[0053] Figure 5 This is a schematic diagram illustrating the effect of the friction coefficient on the particle overlap amount according to the present invention.

[0054] Figure 6 This is a schematic diagram illustrating the effect of the friction coefficient on stress and strain in this invention.

[0055] Figure 7 A comparison table of the compaction effects of different friction coefficients according to the present invention;

[0056] Figure 8 This is a schematic diagram of the rolling process with different friction coefficients according to the present invention;

[0057] Figure 9 A comparative table showing the effect of different contact stiffnesses on the dry density after compaction according to the present invention;

[0058] Figure 10 This is a schematic diagram of the rolling process with different contact stiffnesses according to the present invention;

[0059] Figure 11 This is a comparison table of the maximum dry density results from multiple sets of simulation experiments of the present invention;

[0060] Figure 12 This is a schematic diagram of the rolling effect curves of multiple sets of contact stiffness and multiple sets of friction coefficients according to the present invention;

[0061] Figure 13 This is a schematic diagram comparing different compaction ranges of the density barrel of the present invention;

[0062] Figure 14 This is a schematic diagram comparing the compaction results of density barrels with different compaction ranges according to the present invention;

[0063] Figure 15 A comparison table of simulation results for density barrels with different compaction ranges according to the present invention;

[0064] Figure 16 This is a schematic diagram comparing the compaction results of different falling methods according to the present invention;

[0065] Figure 17 This is a comparison table of simulation results for different drop modes of the present invention;

[0066] Figure 18 This is a schematic diagram of the vibration state of the vibrating plate under the equivalent load of the present invention;

[0067] Figure 19 This is a comparison table of the vibration of the equivalent load and the simulation results of the initial rolling mill in this invention;

[0068] Figure 20 This is a schematic diagram illustrating the principle of the overlapping measurement circle and the enlarged measurement circle of the present invention;

[0069] Figure 21 This is a schematic diagram showing a simulation comparison of different gradations according to the present invention;

[0070] Figure 22 This is a comparison table of numerical simulation and field test results for this invention. Detailed Implementation

[0071] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0073] Specific embodiments of this invention are used to study the influence of factors such as the gradation characteristics of gravel on the degree of compaction, and to analyze the impact of each factor on the dry density of gravel. A large-scale on-site relative density test was conducted at a reservoir using representative gradations and test pit gradations. This embodiment is based on the on-site test at the reservoir and constructs a compaction model through a process of prototype replication, refinement adjustment, and parameter calibration. The model is then compared with the on-site experiment to verify its simulation effect. This model is used to study the compaction process of gravel under pressure and can be used for further research on the vibration compaction characteristics of gravel.

[0074] In the field experiment, a bottomless steel drum was used to fill the sand and gravel. Pre-screened sand and gravel were weighed according to a specific gradation and placed into the drum, then compacted using a large vibrating roller. The field experiment selected... Figure 2 Tests were conducted on representative gradations, following the steps of test material preparation, density bin arrangement, filling and calculation of minimum dry density, mechanical compaction, and maximum dry density testing.

[0075] like Figure 1 As shown in this embodiment, a method for constructing a compaction model for coarse-grained material compaction includes the following steps:

[0076] S1, Build the initial model: Construct the density barrel model, set the contact parameters, and construct the roller model EG1;

[0077] S11, Construct the density barrel model: Construct the model according to the on-site experimental model, as follows: Figure 3 The density barrel shown generates particles with a certain initial porosity according to a set gradation within a specified area. The generated particles reach an equilibrium state through mutual interaction.

[0078] S12, Set contact parameters: Set contact parameters by selecting a set of standard values ​​as the initial contact parameters;

[0079] S13, Construct the millstone model: Generate the millstone model and set the relevant parameters of the millstone model;

[0080] S2, Determine contact parameters: Determine and select the set contact parameters to complete the construction of the compaction model;

[0081] S3, Adjust model refinement parameters: Compare the model refinement parameters in the compaction simulation and select different refinement parameters according to different working conditions;

[0082] Detailed parameters include density barrel compaction range, particle drop pattern, and compaction method;

[0083] S4, Compaction Model Validation: After the compaction model is constructed, numerical simulation is performed and compared with field experiments to verify the effectiveness of the compaction model. The validated model is then used for simulation analysis of coarse aggregate compaction.

[0084] In step S12, the coarse aggregate particles of the gravel and riprap serve as the main supporting framework, while the fine aggregate acts as filler. Both are non-cohesive materials and, without considering particle breakage, are considered rigid materials that cannot be deformed or damaged. Therefore, a linear stiffness model is chosen to simulate the contact parameters. In the linear stiffness model, the contact between particles is a spring connection, and the contact force consists of linear force and damping force.

[0085] ,

[0086] In the formula, F c For contact force, F l For linear force, F d For damping force;

[0087] Linear force F l It is generated by the normal stiffness kn and the tangential stiffness ks, which are determined by the following formula:

[0088] ,

[0089] ,

[0090] In the formula, (1) and (2) represent two particles in contact with each other. and The normal stiffness (N / m) of particles (1) and (2) are respectively. and The tangential stiffness (N / m) of particles (1) and (2) are respectively.

[0091] When particles come into contact, the tension between the springs cannot be maintained, so a coefficient of friction fric is applied between the particles to satisfy the sliding condition.

[0092] like Figure 4 As shown, the parameters that need to be set are normal stiffness kn, tangential stiffness ks, and friction coefficient fric. When setting the contact parameters in the initial model, a set of conventional values ​​is first selected, and the accurate contact parameters are determined by comparison and analysis with field tests.

[0093] In step S2, when determining the contact parameters, it includes determining the friction coefficient fric parameter, determining the stiffness parameter, and determining the parameters under the mutual influence of the friction coefficient fric and stiffness. The method is to first analyze the friction coefficient fric, then analyze the stiffness, and finally conduct a comprehensive analysis of the two.

[0094] S21. When determining the friction coefficient fric parameter, determine the specific value of the friction coefficient fric according to the influence on particle overlap and the influence on the compaction effect.

[0095] As Figure 5 shown, setting a larger friction coefficient fric will make the specimen balance faster. Set the fric values to 0.0, 0.5, and 1.0 respectively. When fric = 0.0, the overlap amount of the particle distribution of the formed sample is relatively small and almost unidentifiable; when fric = 0.5, visible overlap amounts occur in some places between the particles of the specimen; when fric = 1.0, the overlap amount between the particles of the generated specimen is larger than that when fric = 0.5.

[0096] Quantitative analysis of the overlap amount between particles shows that as fric increases, the overlap amount of particles also continuously increases. This overlap amount will make the generated porosity slightly larger than the initially set porosity, while in the actual compaction process, what is reduced is the initially set porosity, that is, replacing embedding with overlap to reach the specified porosity. Therefore, in the process of generating a specimen with the minimum dry density as the goal, the larger fric is, the smaller the compressible space for compaction will be. As Figure 6 shown, when 0 < fric < 0.5, it shows the softening characteristics of dense sand, and when 0.5 < fric < 1.0, it shows the hardening characteristics of loose sand. The closer fric is to 1.0, the more it will show the mechanical properties of sandy soil, deviating from coarse-grained materials. Therefore, in the simulation of coarse-grained materials, the fric value needs to be set within the range of 0.0 - 0.5.

[0097] In the compaction model, keeping other parameters of the model fixed, conduct a sensitivity analysis for the friction coefficient fric of particle contact. As Figure 7 、 8 shown, set three groups of parameters, CG6, CG7, and CG8, for comparison, which are fric = 0.7, fric = 0.4, and fric = 0.1 respectively. As​​​​S22, when determining the stiffness parameters, after selecting a suitable friction coefficient fric, the parameters of normal stiffness kn and tangential stiffness ks are determined based on the different maximum dry densities after rolling simulation.

[0099] like Figure 9 As shown, when determining the contact parameters, the stiffness parameter is usually set to kn=ks. First, the friction coefficient fric=0.4 is selected, and then kn=ks=1×10 6 kn=ks=1×10 7 kn=ks=1×10 8 and kn=ks=1×10 9 The four groups CG9, CG10, CG11 and CG12 were compared.

[0100] like Figure 9 As shown, after running the model for compaction, it was found that the dry density value after compaction decreased accordingly with the increase of kn and ks. This is because kn represents the normal stiffness between particles, and the larger its value, the greater the resistance between particles; ks represents the tangential stiffness between particles, and the larger its value, the more difficult it is for particles to move against each other. Figure 10 As shown, when the contact stiffness kn=ks≤1×10 6 Gravel material is compacted to its maximum value within 1-2 passes, and the maximum dry density does not increase significantly with increasing passes. Particle overlap also occurs, and the dry density curve exhibits a large amplitude, indicating a significant compression effect upon the roller's passage, followed by a substantial rebound after unloading. This phenomenon does not conform to reality. Conversely, as contact stiffness gradually increases, particle overlap disappears, and the dry density curve amplitude decreases. With increasing passes, the curve shows an upward oscillating trend, which aligns better with field test results. Considering that extreme values ​​can easily lead to significant deviations between simulation results and engineering realities, a comprehensive approach was adopted. .

[0101] S23, when determining the parameters considering the interaction between the friction coefficient fric and stiffness, first select the friction coefficient fric = 0.4, and then set different proportionality coefficients between the normal stiffness kn and the tangential stiffness ks, with values ​​ranging from... Between these values, kn is chosen to be 1×10. 8 and 1×10 9 Two sets of parameters, with each set of ks selecting 5 parameters. For example... Figure 11 , 12 As shown in the simulation, it was found that when ks and kn are small, the maximum dry density obtained by compaction is large, and the selected kn value does not produce a large rebound. Therefore, kn=1×10 was chosen. 8 ks=1×10 7After selecting ks=0.1kn, multiple friction coefficients fric were set in the range of 0.1~0.5 under this proportionality coefficient to simulate compaction. The conclusion is that the smaller the fric value, the larger the dry density obtained by the compaction simulation. Finally, the friction coefficient fric=0.1 was selected.

[0102] Further, in step S13, a large circular clump module is generated to simulate a rolling mill. The rolling mill uses a reciprocating motion to vibrate and compact the mill. The parameters of the rolling mill's mass, excitation force, horizontal velocity, and rotational angular velocity are set to simulate the rolling conditions of the rolling mill in the field test. The weight of the rolling mill and the excitation force are adjusted according to the ratio of the original test thickness to the model thickness. The other parameters are calculated using formulas.

[0103] The rotational angular velocity of the grinding wheel is determined by the following formula:

[0104] ,

[0105] In the formula, denoted as , where is the horizontal velocity of the grinding wheel (m / s); r is the radius of the grinding wheel; and w is the angular velocity of the grinding wheel (rad / s). In this embodiment, the radius of the grinding wheel is set to 0.85m.

[0106] During vibratory compaction, the forces acting on the particles are the weight of the roller and the excitation force. The main driving force of the roller is determined by the following formula:

[0107] ,

[0108] In the formula, G is the weight of the millstone (N); F max ω is the peak value of the excitation force (N); ω is the eccentric rotational velocity of the roller (rad / s); t is the rolling time (s).

[0109] After the roller is constructed and its parameters are set, the rolling process is simulated, and relevant data such as contact force, unbalanced force, calculation time, and roller displacement are monitored in real time during the process.

[0110] Furthermore, in step S13, a single clump large-circle simulated grinding wheel is used, which not only realistically replicates the working conditions of the field test, but also reflects the coupling effect of the grinding wheel's kneading effect and the time-varying characteristics of the excitation force. However, the left-right folding motion of the grinding wheel makes the calculation time of the entire model long and the calculation efficiency low, and there is no suitable parameter calibration for the grinding wheel friction force, which cannot well reflect the kneading effect. Therefore, in this embodiment, a method such as... Figure 18 The equivalent load of the vibrating plate replaces the grinding wheel. The vibrating plate is composed of multiple small circular vibrating short plates stacked in a row to achieve an equivalent replacement for the grinding wheel, namely the CG5 scheme.

[0111] In this embodiment, the vibratory plate consists of 24 clumps with a diameter of 0.04m, and a bouncing model of vibratory compaction is used to simulate the working condition of vibratory compaction. The motion equation of the vibratory plate is as follows:

[0112] ,

[0113] In the formula, M is the system mass; e is the vibration eccentricity; S is the displacement; g is the gravitational acceleration; ω1 is the vibration source frequency; The critical angle for takeoff is t; the duration of vibration is t.

[0114] The equivalent replacement parameters for vibratory compaction are calculated using the energy equivalence method, and the formula is as follows:

[0115] ,

[0116] In the formula, E represents the total energy; E d For kinetic energy; E s Potential energy; E c Impact energy;

[0117] The equivalent compaction process of the vibrating plate is as follows: a vertical active force is assigned to each equivalent vibrating short plate, and each vibrating short plate vibrates up and down in sequence. The direction of vibration transmission is consistent with the direction of travel of the compaction machine. All vibrating short plates vibrate up and down in a wave-like state to compact. When all vibrating short plates complete one loading cycle, one compaction operation is completed.

[0118] The above vibration method can be used to equivalently replace the compaction effect of a single roller, but it can effectively improve compaction efficiency. The calculation formula for the improvement in compaction efficiency after using a vibrating plate is as follows:

[0119] ,

[0120] In the formula, n is the factor that increases the compaction efficiency; T is the vibration period; v is the speed of the roller; and d is the diameter of each small circular vibrating plate of the equivalent vibrating plate.

[0121] In this embodiment, as Figure 19 As shown in the simulation test comparison, the final compaction effect of the vibrating plate with equivalent load is basically the same as that of the original single rolling roller scheme, but the operating efficiency is increased by about 5 times, which can effectively improve the operating efficiency of the model.

[0122] The initial model constructed in step S1 uses conventional parameters and is relatively simple. The simulated structure differs significantly from the field experiment. It is necessary to refine the parameters of the initial model to make the simulation results consistent with the field experiment. However, overly refined parameters will reduce the simulation efficiency. Therefore, it is necessary to comprehensively consider the refinement of parameters so that the model simulation can balance efficiency and accuracy.

[0123] Furthermore, when determining the compaction range of the density barrel in step S3, the compaction range is selected based on the working conditions, either by setting a transition zone outside the density barrel or by using a single density barrel.

[0124] During the field test, the compaction machinery uses a zigzag reciprocating motion, which will compact the soil particles around the density barrel into the density barrel, and also compact the fill material in the density barrel to the outside of the density barrel. At the same time, similar graded fill material also needs to be laid around the density barrel in the field test. Therefore, in the model simulation, a transition area of ​​0.5m wide can be set on both sides of the density barrel and filled with the same graded particles to simulate the field working conditions and verify the impact on the final dry density.

[0125] like Figure 13 As shown, specifically, transition zones are added to both sides of the density cylinder. One approach is to fill the transition zone with fine particles of the same gradation, i.e., scheme CG1. The other approach is to fill the transition zone with particles of the same gradation and then expand the compaction range of the roller to the transition zone, i.e., scheme CG2. A single density cylinder is also used for comparison. After compaction simulation, as shown... Figure 14 , 15 As shown, the schemes with added transition zones improved the calculation accuracy by 0.20% and 0.30% respectively, but the calculation time increased by 50% and 65% respectively. After comprehensive consideration, the single-density bucket scheme was adopted.

[0126] In step S3, when determining the particle falling method, the method is selected based on the working conditions: direct generation in the density tank, natural particle falling, or particle falling through a funnel.

[0127] The model uses direct particle generation in the density chamber, but the field test uses a discharge method. To verify whether discharge can improve calculation accuracy, the model simulates the field test for particle generation. The first method involves generating graded particles at a height of 1.8m, then removing the baffle at 1.8m to allow the particles to fall naturally. The surface of the density chamber is then leveled before a compaction test is performed (CG3). The second method involves setting a funnel at 1.8m, using the funnel method to generate particles, leveling the surface after equilibrium, and then compacting (CG1). After simulation, as shown... Figure 16 , 17 As shown, compared to the method of directly generating particles in the density tank, the calculation accuracy of the two particle generation methods mentioned above is improved by 0.10% and 0.24% respectively, but the calculation time increases by 800% and 1000% respectively. Therefore, after comprehensive consideration, the scheme of directly generating particles in the density tank is adopted.

[0128] Furthermore, in step S11, when generating particles with a certain initial porosity, it is necessary to measure the porosity of the sample. The conventional porosity measurement method is the measurement circle method, which is used to output data such as porosity, stress, strain rate and coordination number in the sample. Moreover, the arrangement of the measurement circle needs to be considered in the early stage of model construction, so as to monitor the required data in real time during the simulation process and provide the result data in post-processing.

[0129] The formula for calculating the radius of a circle is:

[0130] ,

[0131] In the formula, n is the porosity; A S The sum of the areas of all particles contained within the measured circle, (m 2 A represents the area of ​​the circle being measured (m²). 2 ).

[0132] Because the measuring circle cannot generate shapes other than a circle, it cannot cover the entire sample during measurement, resulting in a certain blind zone. To ensure coverage of the entire sample, an overlapping arrangement is used. However, this overlapping arrangement introduces certain errors. Figure 20 As shown, there are two overlapping measurement circles, 1 and 2. The area of ​​the particle in measurement circle 1 is A. S1 The area of ​​the particles in circle 2 is measured as A. S2 The area of ​​the overlapping particles is A. SC The areas of the two measuring circles are both A, and the area of ​​the overlapping portion of the two measuring circles is A. C .

[0133] The formulas for calculating the porosity of the two measurement circles covering the region are as follows:

[0134] ,

[0135] The porosity of the measuring circle was calculated using the averaging method. The porosity of circle 1 was measured. Measure the porosity of circle 2 The formula for calculating porosity, obtained by averaging the two values, is as follows:

[0136] ,

[0137] Because A C Greater than A SC Therefore, the actual porosity n must be greater than 1. Therefore, although overlapping measurement circles can cover blind areas, the greater the overlap, the greater the error in the results. Therefore, it is necessary to select the largest measurement circle that covers the entire area as much as possible, or to arrange measurement circles that do not overlap as much as possible.

[0138] In this embodiment, the method for determining the porosity measurement method is the enlarged arrangement of the measuring circle. The specific steps are as follows:

[0139] A single measurement circle is generated to completely cover the density chamber area. Using the porosity n obtained from the measurement circle and the area A of the measurement circle, the total area of ​​the particles inside the chamber is first calculated. The ratio of this area to the total volume of the sample is the true porosity.

[0140] First, calculate the total area A of the particles inside the bucket using the following formula. S :

[0141] ,

[0142] In the formula, A S Let n be the total area of ​​the particles inside the barrel, n be the porosity obtained from the measuring circle, and A be the area of ​​the measuring circle.

[0143] Calculate the total volume V of the compacted sample after compaction using a vibratory plate under an equivalent load:

[0144] ,

[0145] In the formula, V is the total volume of the compacted sample after rolling. R is the average of the y-coordinate values ​​of the equivalent grinding wheel, R is the radius of the equivalent grinding wheel, and L is the bottom width of the sample.

[0146] Then the true porosity is calculated:

[0147] ,

[0148] In the formula, R is the average of the y-coordinate values ​​of the equivalent grinding wheel, R is the radius of the equivalent grinding wheel, and L is the bottom width of the sample.

[0149] The method of expanding the measurement circle has the advantages of accurate calculation results, simple calculation method and high calculation efficiency. However, due to the limitation in obtaining the height of the upper surface of the sample, it needs to be used in conjunction with the rolling equivalent load replacement method.

[0150] Furthermore, since the measured porosity is a two-dimensional value, it needs to be converted into a three-dimensional porosity value. The data conversion method used during the conversion is as follows:

[0151] ,

[0152] In the formula, ρ 2D ρ is a two-dimensional numerical value for porosity. 3D This represents the three-dimensional value of porosity.

[0153] Therefore, the combination of the measurement circle enlargement arrangement method and the three-dimensional porosity conversion formula, along with the rolling equivalent load replacement method, can greatly improve the calculation accuracy and have high computational efficiency.

[0154] In step S4, after the compaction model is constructed and the microscopic parameters are adjusted, a numerical simulation of vibration compaction is performed on the original representative gradation envelope of a reservoir and the gradation envelope of the test pit test material gravel. The simulation is compared with the field experiment to verify the effectiveness of the compaction model. The verified model is then used for simulation analysis of coarse material compaction.

[0155] like Figure 21 As shown, the comparative analysis of numerical simulation and field test results indicate that the numerical model exhibits a significant gradation-dependent characteristic in simulating the vibration compaction process of gravel. When the gravel content is in the range of 70%-90%, the relative error between the simulated and measured values ​​is generally controlled within ±2%, indicating that the numerical model has good applicability to gradation curves with most different gravel contents.

[0156] like Figure 22 As shown, the analysis of the gradation variation law shows that the simulated value of the original gradation envelope shows a non-monotonic trend of first increasing and then decreasing with the increase of gravel content. The peak density appears at 86.7% gravel content, which is basically similar to the trend of first increasing and then decreasing and the extreme point of dry density in the field test. This is consistent with the critical state of fine particles filling the pores of coarse skeleton in the optimal gradation theory of coarse materials.

[0157] In summary, the maximum dry density obtained from the simulation under each gradation is close to the maximum dry density obtained from the field test, and the test error is kept within the allowable range. This verifies the engineering prediction ability of the constructed compaction model for most conventional gradation ranges and can be used for simulation analysis of coarse aggregate compaction.

[0158] The results of the simulation of the compaction model constructed in this embodiment, compared with the field test data of a reservoir, show that the average error of the dry density calculation obtained by the compaction model in this embodiment is controlled within 3%, which has a good simulation effect. It can also vividly reflect the compaction process of sand and gravel, and has certain reference value for field tests, which is convenient for personnel to conduct analysis and research.

[0159] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for constructing a compaction model for coarse-grained material compaction, characterized in that, Includes the following steps, S1, Building the initial model: Constructing a density barrel model, setting contact parameters, and constructing a rolling mill model; S2, Determine contact parameters: Determine and select the set contact parameters to complete the construction of the compaction model; S3, Adjust model refinement parameters: Compare the model refinement parameters in the compaction simulation and select different refinement parameters according to different working conditions; Detailed parameters include density barrel compaction range, particle drop pattern, and compaction method; S4, Compaction Model Validation: After the compaction model is constructed, numerical simulation is performed and compared with field experiments to verify the effectiveness of the compaction model. The validated model is then used for simulation analysis of coarse aggregate compaction. In step S1, the initial model construction includes the following steps: S11, Constructing a density barrel model: Construct a density barrel according to the on-site experimental model, generate particles with a certain initial porosity according to the set gradation particles, and the generated particles reach an equilibrium state through mutual interaction. S12, Set contact parameters: Set contact parameters by selecting a set of standard values ​​as the initial contact parameters; S13, Construct the millstone model: Generate the millstone model and set the relevant parameters of the millstone model; In step S12, a linear stiffness model is selected to simulate the contact parameters. In the linear stiffness model, the contact between particles is a spring connection, and the contact force consists of linear force and damping force. The linear force is generated by the normal stiffness kn and the tangential stiffness ks, which are determined by the following formulas: ， ， In the formula, (1) and (2) represent two particles in contact with each other. and The normal stiffness (N / m) of particles (1) and (2) are respectively. and The tangential stiffness (N / m) of particles (1) and (2) are respectively. When particles come into contact, the sliding condition is satisfied by applying a coefficient of friction fric between the particles; In step S2, determining the contact parameters includes determining the friction coefficient fric parameter, determining the stiffness parameter, and determining the parameters under the interaction of the friction coefficient fric and stiffness: S21, When determining the friction coefficient fric parameter, the specific value of the friction coefficient fric is determined based on its influence on particle overlap and its influence on the compaction effect. S22, When determining the stiffness parameters, after selecting a suitable friction coefficient fric, the parameters of normal stiffness kn and tangential stiffness ks are determined based on the different maximum dry densities after rolling simulation. S23. When determining the parameters by comprehensively considering the mutual influence of friction coefficient fric and stiffness, first select the friction coefficient fric, then set different proportional coefficients between normal stiffness kn and tangential stiffness ks, select a suitable proportional coefficient after rolling simulation, set multiple sets of friction coefficient fric under this proportional coefficient, and select a suitable friction coefficient fric. In step S13, a large circular roller is generated, and vibratory compaction is performed using a reciprocating motion. Parameters for the roller's mass, excitation force, horizontal velocity, and rotational angular velocity are set. The rotational angular velocity of the grinding wheel is determined by the following formula: ， In the formula, denoted as σr, where σr is the horizontal velocity of the mill wheel (m / s); r is the radius of the mill wheel; and w is the angular velocity of the mill wheel (rad / s). During vibratory compaction, the forces acting on the particles are the weight of the roller and the excitation force. The main driving force of the roller is determined by the following formula: ， In the formula, G is the weight of the millstone (N); F max ω is the peak value of the excitation force (N); ω is the eccentric rotational velocity of the roller (rad / s); t is the rolling time (s).

2. The method for constructing a compaction model for coarse-grained material compaction according to claim 1, characterized in that: In step S3, when refining the compaction method, a vibrating plate with equivalent load is used instead of a roller. The vibrating plate is composed of multiple small circular vibrating short plates stacked in rows. The equation of motion for the vibrating plate is: ， In the formula, M is the system mass; e is the vibration eccentricity; S is the displacement; g is the gravitational acceleration; ω1 is the vibration source frequency; The critical angle for takeoff is t; the duration of vibration is t. The equivalent replacement calculation formula for vibratory compaction is: ， In the formula, E represents the total energy. d For kinetic energy; E s Potential energy; E c Impact energy; The equivalent compaction process of the vibrating plate is as follows: each vibrating short plate vibrates in sequence, the direction of vibration transmission is consistent with the direction of travel of the compaction machine, all vibrating short plates vibrate up and down in a wave-like state to compact, and when all vibrating short plates complete one loading cycle, one compaction operation is completed. The formula for calculating the improvement in compaction efficiency after using a vibrating plate is as follows: ， In the formula, n is the factor that increases the compaction efficiency; T is the vibration period; v is the speed of the roller; and d is the diameter of each small circular vibrating plate of the equivalent vibrating plate.

3. The method for constructing a compaction model for coarse-grained material compaction according to claim 1, characterized in that: In step S3, when determining the compaction range of the density barrel, the compaction range is selected based on the working conditions, either by setting a transition zone outside the density barrel or by using a single density barrel.

4. The method for constructing a compaction model for coarse-grained material compaction according to claim 1, characterized in that: In step S3, when determining the particle falling method, the method is selected based on the working conditions: direct generation in the density tank, natural particle falling, or particle falling through a funnel.

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