A multi-stage calibration method of mesoscopic parameters for discrete element linear contact bond model

Abstract

The application discloses a kind of mesoscopic parameter multistage calibration method suitable for discrete element linear contact bonding model.The method follows the three-level progressive system of "single component-mixture-structure", and adopts the decoupling strategy of "stiffness first, strength later".Firstly, based on the uniaxial compression simulation of single component and mixture, the mapping relationship between macro modulus and mesoscopic normal stiffness is established to calibrate the stiffness parameter, and the parameter precision is further improved by structure load model fine tuning;then, based on the splitting simulation of single component and mixture, the mapping relationship between macro strength and mesoscopic tensile strength is established to calibrate the strength parameter.The method solves the problem of low efficiency and unclear physical meaning of traditional trial-and-error method, significantly improves the efficiency, accuracy and physical reasonableness of discrete element mesoscopic parameter calibration.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, and in particular to a microscopic numerical modeling technique for composite materials such as asphalt concrete and cement concrete. Specifically, it relates to a multi-level calibration method for microscopic parameters applicable to discrete element linear contact bonding models. Background Technology

[0002] Discrete element method (DEM) numerical simulation is an important tool for studying the mechanical behavior of discrete media. It is widely used in fields such as particulate materials, discontinuous media, and multiphase systems, and has significant advantages, especially in mesoscopic modeling and complex mechanical response analysis. In current DEM modeling practices, obtaining key mesoscopic parameters typically relies on trial and error. This method involves pre-setting mesoscopic parameters, repeatedly performing numerical calculations at the mixture level, and comparing the simulated macroscopic mechanical properties with experimentally measured data to deduce the mesoscopic parameters that meet the required computational accuracy.

[0003] However, existing trial-and-error methods have certain limitations in practical applications. On the one hand, these methods often only consider the overall macroscopic mechanical properties of the mixture as the sole calibration target, neglecting the independent rationality of the macroscopic mechanical properties of the constituent materials (such as asphalt mortar and coarse aggregate in asphalt mixtures). This leads to unclear physical meanings of the parameters of each individual component material in the model, thus affecting the reliability and generalizability of the microscopic parameters. On the other hand, the lack of clear basis for setting the initial microscopic parameters may result in an irregular parameter optimization process, making the trial-and-error process time-consuming and labor-intensive, which to some extent restricts the computational efficiency of the microscopic parameters of the discrete element model. Therefore, establishing a rapid calculation method for multi-level key microscopic parameters with clear physical logical constraints is of great significance for improving the modeling efficiency and parameter rationality of the discrete element method. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a multi-level calibration method for mesoscopic parameters applicable to discrete element linear contact bonding models, characterized by the following steps:

[0005] Phase 1: Stiffness parameter calibration;

[0006] A discrete element uniaxial compression simulation model of a single-component material is established, and the first mapping relationship between the mesoscopic normal stiffness and the macroscopic compressive resilient modulus of the single component is established by fitting calculation.

[0007] A discrete element uniaxial compression simulation model of the mixture is established. Using the first mapping relationship, a second mapping relationship is established between the mesoscopic normal stiffness of each component and the macroscopic compressive resilience modulus of the mixture. Based on this, the mesoscopic normal stiffness of each component is calibrated.

[0008] A discrete element structural load model is established, and the structural mechanical response is calculated using the calibrated mesoscopic normal stiffness of each component. The mesoscopic normal stiffness is then fine-tuned based on the deviation between the simulated and measured values.

[0009] Second stage: Strength parameter calibration;

[0010] Based on the fixed calibrated mesoscopic normal stiffness, a discrete element splitting simulation model of a single-component material is established, and a third mapping relationship between mesoscopic tensile strength and single-component macroscopic splitting strength is established through fitting calculation.

[0011] A discrete element splitting simulation model of the mixture is established. Using the third mapping relationship, a fourth mapping relationship is established between the microscopic tensile strength of each component and the macroscopic splitting strength of the mixture. Based on this, the microscopic tensile strength of each component is calibrated.

[0012] Furthermore, in both the first and second stage model building processes, the linear contact bonding model is used as the mesoscopic contact model;

[0013] In the first stage, the mesoscopic normal stiffness is fixed. With tangential stiffness Conversion factors between them, and micro tensile strength With shear strength Conversion factors between them, and micro tensile strength Set to the maximum value to prevent model breakage;

[0014] In the second stage, the conversion factor is fixed for each component material, and the mesoscopic normal stiffness of each component, which was calibrated in the first stage, is set.

[0015] Furthermore, the single-component material simulation model, mixture simulation model, and structural load model established in the first and second stages are all flexible cluster models; the particle size in the models is consistent, and the micro-particle arrangement adopts a tetragonal or hexagonal arrangement structure.

[0016] Furthermore, the specific steps for establishing the first mapping relationship include:

[0017] Determine the actual values ​​of the compressive resilient modulus of each component material. The range of intervals;

[0018] Preset several sets of mesoscopic normal stiffness Calculate the simulated values ​​of the macroscopic compressive resilience modulus of each component material. ;

[0019] The preset sets of mesoscopic normal stiffness The value of makes the calculated simulated values ​​of several sets of macroscopic compressive resilient modulus [the value of ] so that the calculated [values ​​of ] are [the value of ], and the calculated [values ​​of ] are ... Covering the main part of the range;

[0020] Simulated values ​​of the compressive and resilient modulus of each component material were established by fitting. With microscopic normal stiffness The functional relationship is used as the first mapping relationship.

[0021] Furthermore, the specific steps for establishing the second mapping relationship and calibrating the mesoscopic normal stiffness include:

[0022] Based on the actual values ​​of the compressive resilient modulus of each component material Using the first mapping relationship, several sets of mesoscopic normal stiffness are calculated. ;

[0023] Using a discrete element uniaxial compression simulation model of the mixture, the simulated values ​​of the macroscopic compressive resilient modulus of the mixture under different component combinations are calculated. ;

[0024] Simulated values ​​of the compressive resilient modulus of the mixture were established by fitting. The functional relationship between the normal stiffness of each component and the microscopic normal stiffness is used as the second mapping relationship;

[0025] Using the actual value of the compressive resilient modulus of the mixture Actual values ​​of compressive resilient modulus of each component material The mesoscopic normal stiffness of each component material is calibrated based on the second mapping relationship.

[0026] Furthermore, the principle for calibrating the mesoscopic normal stiffness of each component material is: to match the actual value of the macroscopic compressive resilient modulus of the mixture. Prioritize this, and on this basis, constrain the mesoscopic normal stiffness of each component to be within a preset reasonable range.

[0027] Furthermore, the specific steps for fine-tuning the mesoscopic normal stiffness include:

[0028] The structural mechanical response is calculated using the mesoscopic normal stiffness of each component, which has been calibrated in the second mapping relationship.

[0029] When there is a deviation between the simulated value of the structural mechanical response and the calibration result of the mixture layer, a fine-tuning operation is performed;

[0030] The principle of fine-tuning is to prioritize maintaining the stability of the mixture layer calibration results and adjust the parameters while meeting the accuracy requirements.

[0031] Furthermore, the specific steps for establishing the third mapping relationship include:

[0032] Determine the actual value of the splitting strength of each component material. The range of intervals;

[0033] Preset several sets of micro tensile strength Calculate the simulated macroscopic splitting strength values ​​of each component material. ;

[0034] The preset set of micro tensile strengths The value of makes the calculated sets of macroscopic splitting strength simulation values... Covering the main part of the range;

[0035] Simulated values ​​of the splitting strength of each component material were established by fitting. With micro tensile strength The functional relationship is used as the third mapping relationship.

[0036] Furthermore, the specific steps for establishing the fourth mapping relationship and calibrating the mesoscopic tensile strength include:

[0037] Based on the actual values ​​of the splitting strength of each component material Several sets of mesoscopic tensile strengths are calculated using the third mapping relationship. ;

[0038] A discrete element method (DEM) model of the mixture was used to calculate the simulated macroscopic splitting strength of the mixture under different component combinations. ;

[0039] Simulated values ​​of the splitting strength of the mixture were established by fitting. The functional relationship between the tensile strength of each component and the microscopic tensile strength is used as the fourth mapping relationship;

[0040] Using the actual value of the splitting strength of the mixture Actual values ​​of splitting strength of each component material The microscopic tensile strength of each component material is calibrated based on the fourth mapping relationship.

[0041] Furthermore, the principle for calibrating the microscopic tensile strength of each component material is: to match the actual value of the macroscopic splitting strength of the mixture. Prioritize, and on this basis, constrain the microscopic tensile strength of each component to be within a preset reasonable range.

[0042] The beneficial effects of this invention are:

[0043] This invention proposes a discrete element method for calibrating micro-parameters that follows a hierarchical calibration system of "single component—mixture—structure" and incorporates a decoupling strategy of "modulus first, strength later." This method effectively fills the gap in the systematic calibration process of existing technologies. By prioritizing the physical rationality of both single-component materials and mixtures, it avoids the parameter offset problem that is prone to occur in traditional trial-and-error methods, thereby significantly improving the accuracy and reliability of micro-parameters.

[0044] Meanwhile, this invention establishes a quantitative mapping relationship between macroscopic mechanical indicators and microscopic model parameters. By constructing mathematical functions, the microscopic parameters become predictable and computable, changing the previous calibration method of blind trial and error and significantly improving the efficiency of parameter acquisition. This method is not only applicable to asphalt pavement materials, but can also provide a reference for the discrete element microscopic parameter calibration of other composite materials. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a multi-level calibration method for mesoscopic parameters applicable to discrete element linear contact bonding models, as described in this application.

[0047] Figure 2 This is a schematic diagram of the road surface structure studied in the embodiments of this application;

[0048] Figure 3 This is a uniaxial compression test model of a single-component material in the embodiments of this application;

[0049] Figure 4 It is the mapping function between the mesoscopic normal stiffness and the macroscopic compressive resilient modulus in the embodiments of this application;

[0050] Figure 5 This is a uniaxial compression test model of the mixture in the embodiments of this application (taking SMA-13 ​​asphalt mixture as an example).

[0051] Figure 6 This is a splitting test model of a single-component material in the embodiments of this application;

[0052] Figure 7 It is the mapping function between the microscopic tensile strength and the macroscopic splitting strength in the embodiments of this application;

[0053] Figure 8This is a splitting test model of the mixture in the application embodiment (taking SMA-13 ​​asphalt mixture as an example). Detailed Implementation

[0054] To make the purpose, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] Example 1:

[0056] like Figure 1 As shown, this embodiment provides a multi-level calibration method for mesoscopic parameters applicable to discrete element linear contact bonding models. This method follows a hierarchical, progressive calibration logic of "single component—mixture—structure," and the overall process is divided into two main stages: "mesoscopic normal stiffness parameter calibration" and "mesoscopic tensile strength parameter calibration." During implementation, the discrete element model uses a flexible cluster model to simulate material particles, maintaining consistent particle size. The preferred mesoscopic particle arrangement is a tetragonal or hexagonal structure, and the mesoscopic contact constitutive model uses a linear contact bonding model.

[0057] Phase 1: Microscopic Normal Stiffness Parameter Calibration

[0058] This stage involves executing steps S1 to S5 to accurately obtain the mesoscopic normal stiffness parameters of each component material.

[0059] First, step S1 is executed to establish a discrete element uniaxial compression simulation model of a single-component material. In this model, the mesoscopic normal stiffness is set. With tangential stiffness Conversion factors between them, and micro tensile strength With shear strength The conversion factor between them is a fixed value. To ensure that the model does not experience unexpected fracture failure during the loading process of calculating the compressive resilient modulus, the micro-tensile strength needs to be converted to a fixed value. Set to the maximum value, which is usually 1e4 or a value of the same order of magnitude.

[0060] Perform step S2 to determine the actual value of the compressive resilient modulus of each component material. The reasonable range is determined, and several sets of mesoscopic normal stiffness with different gradients are preset. By running the simulation model, the simulated values ​​of the macroscopic compressive resilient modulus of each component material were calculated. And ensure that these simulated values ​​cover the range of the actual values ​​mentioned above. Subsequently, numerical regression analysis was used to establish simulated values ​​for the compressive resilient modulus of the single-component material. With microscopic normal stiffness The first mapping function between The functional relationship is denoted as .

[0061] Perform step S3 to establish a discrete element uniaxial compression simulation model of the mixture. The particle size, arrangement, and conversion factors for various stiffnesses and strengths in this model are consistent with those in step S1, and the mesoscopic tensile strength is also included. Set to a maximum value to eliminate interference from destructive behavior on the modulus calculation.

[0062] Execute step S4, using the first mapping function obtained in step S2. Based on the actual values ​​of the compressive resilient modulus of each component material The initial mesoscopic normal stiffness of each component is calculated in reverse. Using this initial value as a baseline, several sets of mesoscopic parameter combinations are generated within a certain range and substituted into the mixture model for calculation, yielding simulated values ​​of the macroscopic compressive resilient modulus of the mixture under different component combinations. By using multivariate numerical fitting, a simulated value for the compressive resilient modulus of the mixture was established. The second mapping function between the microscopic normal stiffness of each component , recorded as Finally, the actual value of the compressive resilient modulus of the mixture was used. and actual values ​​of each component material The mesoscopic normal stiffness of each component is then calibrated based on this second mapping function. In this process, the calibration principle prioritizes matching the macroscopic actual values ​​of the mixture. The main focus is on ensuring that the parameters of each component are within a reasonable range of physical values.

[0063] After completing the calibration of the mixture layer, proceed to step S5 for structural verification. Establish a discrete element load model of the simulated structure, and substitute the calibrated mesoscopic normal stiffness of each component from step S4 into the model to calculate the mechanical response of the structure. If there are deviations between the simulated and measured values, fine-tune the parameters. During fine-tuning, adhere to the principle of maintaining the stability of the mixture layer calibration results, making only minor corrections when necessary to meet accuracy requirements.

[0064] Phase Two: Detailed Calibration of Tensile Strength Parameters

[0065] Based on the fixed microscopic normal stiffness and related tangential stiffness determined in the first stage, this stage completes the calibration of the microscopic tensile strength parameters by executing steps S6 to S9.

[0066] Perform step S6 to establish discrete element splitting simulation models for each component material. At this point, the mesoscopic normal stiffness of each component material in the model directly adopts the values ​​after calibration in the first stage, and the conversion coefficients between stiffness and strength remain unchanged.

[0067] Perform step S7. Determine the actual values ​​of the macroscopic splitting strength of each component material. The reasonable range of values, and several sets of micro tensile strengths are preset. Simulation calculations were performed to obtain the corresponding simulated values ​​of macroscopic splitting strength. To ensure that the simulated values ​​cover the actual value range, simulated values ​​of the splitting strength of single-component materials are established through fitting. With micro tensile strength The third mapping function between This relationship is denoted as .

[0068] Perform step S8 to establish a discrete element splitting simulation model of the mixture. All settings in the model, including particle size, arrangement, conversion factor, and fixed stiffness parameters, are consistent with those in step S6.

[0069] Finally, execute step S9. Utilize the third mapping function. Actual values ​​of splitting strength of each component material The initial mesoscopic tensile strength of each component is calculated. Based on this, parameter combinations are generated, and a mixture splitting simulation is run to obtain the simulated macroscopic splitting strength of the mixture. Simulated values ​​of splitting strength of the mixture were established through multivariate fitting. The fourth mapping function between the microscopic tensile strength of each component , recorded as Ultimately, the actual splitting strength of the mixture is used. Combined with the actual values ​​of each component The microscopic tensile strength of each component is calibrated based on the fourth mapping function, thereby obtaining a complete set of microscopic parameters that satisfies both the physical meaning of individual components and the macroscopic mechanical properties of the mixture.

[0070] Example 2:

[0071] This embodiment uses a two-dimensional semi-rigid base asphalt pavement structure discrete element model as an example to verify the effectiveness of the method described in this invention. The research object covers three levels: single-component materials (SMA-13 ​​asphalt mortar, SUP-20 asphalt mortar, SUP-25 asphalt mortar, water-stabilized mortar, low-dose water-stabilized mortar, coarse aggregate), mixtures (SMA-13 ​​asphalt mixture, SUP-20 asphalt mixture, SUP-25 asphalt mixture, cement-stabilized crushed stone, low-dose cement-stabilized crushed stone), and semi-rigid base asphalt pavement structures (structural forms such as...). Figure 2 (As shown).

[0072] Phase 1: Microscopic Normal Stiffness Parameter Calibration

[0073] (1) Obtain the actual values ​​of macroscopic parameters

[0074] In this embodiment, the coarse aggregate used is limestone, and its actual compressive resilient modulus is determined. The compressive resilient modulus was 50 GPa. The actual values ​​of the compressive resilient modulus of each mortar and mixture, determined through indoor tests, are as follows:

[0075] SMA-13, SUP-20, and SUP-25 asphalt mortars have pressures of 853 MPa, 1067 MPa, and 1221 MPa, respectively.

[0076] The asphalt mixtures SMA-13, SUP-20, and SUP-25 have pressure ratings of 2657 MPa, 4516 MPa, and 3902 MPa, respectively.

[0077] The pressures of cement-stabilized crushed stone mortar and low-dosage cement-stabilized crushed stone mortar are 5763 MPa and 1191 MPa, respectively.

[0078] Cement-stabilized crushed stone and low-dose cement-stabilized crushed stone have pressures of 14293 MPa and 10355 MPa, respectively.

[0079] (2) Single-component first-stage calibration

[0080] A simulation model of the compressive resilient modulus of a single-component material was established using Discrete Element Method (PFC9.0) software. The diameter of the particle element (ball) was set to 0.75 mm, the microstructure adopted a hexagonal arrangement, and the contact constitutive model adopted a linear contact-bond model. The microstiffness ratio was kept constant. Microscopic strength ratio and micro tensile strength Set to a maximum value (e.g., 9999) to prevent breakage.

[0081] Preset mesoscopic normal stiffness For multiple gradient values ​​ranging from 1e8 N / m to 9e10 N / m, the corresponding simulated modulus values ​​were calculated. Establish a fitting equation through linear regression (e.g.) Figure 4 As shown in the figure, the mesoscopic normal stiffness of each single-component material was calculated in reverse, and the results are shown in Table 1.

[0082] Table 1. Conversion results of mesoscopic normal stiffness parameters for single-component materials

[0083]

[0084] (3) Second-stage calibration of the mixture

[0085] Establish a discrete element uniaxial compression simulation model of the mixture (e.g.) Figure 5 As shown in the figure, the model parameter settings are consistent with those of the single-component model. Based on the actual modulus values ​​of mortar and coarse aggregate, five predefined values ​​are selected and substituted into the model with fluctuations of up and down. Taking SMA-13 ​​asphalt mixture as an example, its calculation results and fitting data are shown in Table 2(a) and Table 2(b).

[0086] Table 2(a) Calculation results of compressive resilient modulus of the mixture (taking SMA-13 ​​asphalt mixture as an example)

[0087]

[0088] Table 2(b) Fitting equation for compressive resilient modulus of the mixture

[0089]

[0090] Based on the data in Tables 2(a) and 2(b), simulated values ​​of the compressive resilient modulus of the mixture were established. The fitting equations with the moduli of each component (R² is greater than 0.97) were used to calibrate the microscopic parameters.

[0091] (4) Third-level structural calibration (verification and fine-tuning)

[0092] A discrete element model of the semi-rigid base asphalt pavement structure was established. The measured values ​​of the mechanical response collected from the field test road were compared with the simulated values, and the results are shown in Table 3. Since the simulated values ​​are close to the measured values ​​and meet the accuracy requirements, no further significant fine-tuning was performed. The final determined mesoscopic normal stiffness of each component is shown in Table 4.

[0093] Table 3 Measured and simulated values ​​of pavement structural mechanical response

[0094]

[0095] Table 4. Mesoscopic normal stiffness values ​​of each component in the pavement structure

[0096]

[0097] Phase Two: Detailed Calibration of Tensile Strength Parameters

[0098] (1) Obtain the actual value of macro intensity

[0099] Determine the actual value of splitting strength of limestone coarse aggregate. The splitting tensile strength was 7 MPa. The actual values ​​of the splitting tensile strength of each component and the mixture are as follows:

[0100] The strengths of SMA-13, SUP-20, and SUP-25 asphalt mortars are 1.107 MPa, 0.946 MPa, and 0.652 MPa, respectively.

[0101] The asphalt mixtures SMA-13, SUP-20, and SUP-25 have pressure ratings of 1.353 MPa, 1.229 MPa, and 1.015 MPa, respectively.

[0102] The pressures of cement-stabilized crushed stone mortar and low-dosage cement-stabilized crushed stone mortar were 2.833 MPa and 1.671 MPa, respectively.

[0103] The pressures for cement-stabilized crushed stone and low-dose cement-stabilized crushed stone were 5.195 MPa and 3.092 MPa, respectively.

[0104] (2) Single-component first-stage calibration

[0105] Establish a simulation model for the splitting strength of single-component materials (such as...) Figure 6 (As shown). Substitute the previously calibrated mesoscopic normal stiffness into the model, keeping the conversion coefficients unchanged. Preset mesoscopic tensile strength. Calculate the simulated macroscopic splitting strength values ​​for multiple sets of values ​​ranging from 1e3N to 20e3N. And establish fitting equations (such as...) Figure 7 (As shown). The inverse calculation results are shown in Table 5.

[0106] Table 5. Conversion results of microscopic tensile strength parameters of single-component materials

[0107]

[0108] (3) Second-stage calibration of the mixture

[0109] Establish a simulation model for the splitting of the mixture (e.g.) Figure 8 (As shown). Based on the actual splitting strength of mortar and coarse aggregate, five sets of predefined values ​​were selected and substituted into the model. Taking SMA-13 ​​asphalt mixture as an example, the calculation results and fitting data are shown in Table 6(a) and Table 6(b).

[0110] Table 6(a) Calculation results of splitting strength of the mixture (taking SMA-13 ​​asphalt mixture as an example)

[0111]

[0112] Table 6(b) Fitting equation for splitting strength of mixture

[0113]

[0114] Based on the data in Tables 6(a) and 6(b), simulated values ​​of splitting strength of the mixture were established. The fitting equations for the strength of each component (R² is greater than 0.96) are used to finally complete the accurate calibration of the microscopic tensile strength of each component.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-level calibration method for mesoscopic parameters applicable to discrete element linear contact bonding models, characterized in that, Includes the following steps: Phase 1: Microscopic normal stiffness parameter calibration; A discrete element uniaxial compression simulation model of the single-component materials constituting the semi-rigid base asphalt pavement structure is established. The first mapping relationship between the mesoscopic normal stiffness and the macroscopic compressive resilient modulus of the single component is established by fitting calculation. The single-component materials include asphalt mortar, water-stabilized mortar and coarse aggregate. A discrete element uniaxial compression simulation model of the mixture in the semi-rigid base asphalt pavement structure is established. Using the first mapping relationship, a second mapping relationship between the mesoscopic normal stiffness of each component and the macroscopic compressive resilient modulus of the mixture is established, and the mesoscopic normal stiffness of each component is calibrated accordingly. The mixture includes asphalt mixture and cement-stabilized crushed stone. A discrete element structural load model of the semi-rigid base asphalt pavement structure is established. The structural mechanical response is calculated using the calibrated mesoscopic normal stiffness of each component. The mesoscopic normal stiffness is then fine-tuned based on the deviation between the simulated and measured values. Phase Two: Detailed calibration of tensile strength parameters; Based on the fixed calibrated mesoscopic normal stiffness, a discrete element splitting simulation model of the single-component material is established, and a third mapping relationship between mesoscopic tensile strength and single-component macroscopic splitting strength is established through fitting calculation. A discrete element splitting simulation model of the mixture is established. Using the third mapping relationship, a fourth mapping relationship is established between the microscopic tensile strength of each component and the macroscopic splitting strength of the mixture. Based on this, the microscopic tensile strength of each component is calibrated.

2. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that: In both the first and second stages of model building, the linear contact bonding model was used as the mesoscopic contact model. In the first stage, the mesoscopic normal stiffness is fixed. With tangential stiffness Conversion factors between them, and micro tensile strength With shear strength Conversion factors between them, and micro tensile strength Set to the maximum value to prevent model breakage; In the second stage, the conversion factor is fixed for each component material, and the mesoscopic normal stiffness of each component, which was calibrated in the first stage, is set.

3. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that: The single-component material simulation model, mixture simulation model, and structural load model established in the first and second stages are all flexible cluster models; the particle size in the models is consistent, and the micro-particle arrangement adopts a tetragonal or hexagonal arrangement structure.

4. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that, The specific steps for establishing the first mapping relationship include: Determine the actual values ​​of the compressive resilient modulus of each component material. The range of intervals; Preset several sets of mesoscopic normal stiffness Calculate the simulated values ​​of the macroscopic compressive resilience modulus of each component material. ; The preset sets of mesoscopic normal stiffness The value of makes the calculated simulated values ​​of several sets of macroscopic compressive resilient modulus [the value of ] so that the calculated [values ​​of ] are [the value of ], and the calculated [values ​​of ] are ... Covering the main part of the range; Simulated values ​​of the compressive and resilient modulus of each component material were established by fitting. With microscopic normal stiffness The functional relationship is used as the first mapping relationship.

5. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that, The specific steps for establishing the second mapping relationship and calibrating the mesoscopic normal stiffness include: Based on the actual values ​​of the compressive resilient modulus of each component material Using the first mapping relationship, several sets of mesoscopic normal stiffness are calculated. ; Using a discrete element uniaxial compression simulation model of the mixture, the simulated values ​​of the macroscopic compressive resilient modulus of the mixture under different component combinations are calculated. ; Simulated values ​​of the compressive resilient modulus of the mixture were established by fitting. The functional relationship between the normal stiffness of each component and the microscopic normal stiffness is used as the second mapping relationship; Using the actual value of the compressive resilient modulus of the mixture Actual values ​​of compressive resilient modulus of each component material The mesoscopic normal stiffness of each component material is calibrated based on the second mapping relationship.

6. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 5, characterized in that: The principle for calibrating the mesoscopic normal stiffness of each component material is: to match the actual value of the macroscopic compressive resilient modulus of the mixture. Prioritize this, and on this basis, constrain the mesoscopic normal stiffness of each component to be within a preset reasonable range.

7. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that, The specific steps for fine-tuning the microscopic normal stiffness include: The structural mechanical response is calculated using the mesoscopic normal stiffness of each component, which has been calibrated in the second mapping relationship. When there is a deviation between the simulated value of the structural mechanical response and the calibration result of the mixture layer, a fine-tuning operation is performed; The principle of fine-tuning is to prioritize maintaining the stability of the mixture layer calibration results and adjust the parameters while meeting the accuracy requirements.

8. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that, The specific steps for establishing the third mapping relationship include: Determine the actual values ​​of the splitting strength of each component material. The range of intervals; Preset several sets of micro tensile strength Calculate the simulated macroscopic splitting strength values ​​of each component material. ; The preset set of micro tensile strengths The value of makes the calculated sets of macroscopic splitting strength simulation values... Covering the main part of the range; Simulated values ​​of the splitting strength of each component material were established by fitting. With micro tensile strength The functional relationship is used as the third mapping relationship.

9. The method for multi-level calibration of mesoscopic parameters applicable to discrete element linear contact bonding models according to claim 1, characterized in that, The specific steps for establishing the fourth mapping relationship and calibrating the mesoscopic tensile strength include: Based on the actual values ​​of the splitting strength of each component material Several sets of mesoscopic tensile strengths are calculated using the third mapping relationship. ; A discrete element method (DEM) model of the mixture was used to calculate the simulated macroscopic splitting strength of the mixture under different component combinations. ; Simulated values ​​of the splitting strength of the mixture were established by fitting. The functional relationship between the tensile strength of each component and the microscopic tensile strength is used as the fourth mapping relationship; Using the actual value of the splitting strength of the mixture Actual values ​​of splitting strength of each component material The microscopic tensile strength of each component material is calibrated based on the fourth mapping relationship.

10. A multi-level calibration method for mesoscopic parameters of a discrete element linear contact bonding model according to claim 9, characterized in that: The principle for calibrating the microscopic tensile strength of each component material is: to match the actual value of the macroscopic splitting strength of the mixture. Prioritize, and on this basis, constrain the microscopic tensile strength of each component to be within a preset reasonable range.

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