Intelligent grouting simulation method and system based on multi-scale adaptive nesting solution

Abstract

The invention discloses an intelligent grouting simulation method and system based on multi-scale adaptive nesting solution, and the method comprises the steps: obtaining basic data and grouting material data of a target region, and carrying out the preprocessing; dividing the target area into a plurality of geological layers, describing the boundary, thickness and interlayer relationship of each layer through a three-dimensional model, and constructing a geological model under a macroscale; detail information of the shape, size distribution and pore structure of soil particles is obtained, and a soil body structure model under the micro-scale is further constructed; coupling the geologic model under the macro scale and the soil body structure model under the micro scale; when grouting simulation is carried out, the size of a geological model for simulation calculation is automatically determined according to the complexity of geological conditions. According to the method, nesting solution can be carried out on complex geological conditions (such as multiple layers of soil bodies, non-uniform permeability and different porosities).

Description

Technical Field

[0001] The present invention relates to the technical field of grouting simulation, and in particular to a grouting intelligent simulation method and system based on multi-scale adaptive nested solution. Background Art

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

[0003] Under complex geological conditions, grouting is an important means to repair fractured rock and improve the stability of surrounding rock. Numerical simulation of grouting can analyze the transmission and diffusion behavior of the grouting process in the geological body, and then optimize the grouting design, which plays an important guiding role in tunnel construction.

[0004] Most existing numerical simulation methods for grouting are based on single-scale models or lack the ability to dynamically adjust computational accuracy, thus failing to accurately reflect the complexity of material flow at different scales during the grouting process. High-precision simulations typically require extensive computing resources, making real-time application difficult for engineering projects and resulting in prohibitively high computational costs. Simulation accuracy cannot be dynamically adjusted based on the physical characteristics and engineering requirements of different regions, resulting in a lack of adaptability. Existing single-scale models struggle to simultaneously consider both microscopic material behavior and macroscopic construction effects during the grouting process, making it difficult to handle complex multi-scale coupling issues.

[0005] As engineering projects become increasingly complex and larger in scale, higher requirements are placed on the precise simulation of the grouting process. Existing numerical simulations for grouting often fail to automatically adjust mesh density and numerical algorithms based on formation characteristics and actual requirements, nor can they dynamically partition the solution area, making it difficult to accurately simulate the grouting process in practical applications. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes an intelligent grouting simulation method and system based on multi-scale adaptive nested solution. By performing adaptive nested solution on the grouting process at different scales, it can adapt to different geological conditions, realize accurate calculation of slurry flow, diffusion and mechanical properties, and realize accurate and efficient simulation of the grouting process.

[0007] In some embodiments, the following technical solutions are adopted:

[0008] A grouting intelligent simulation method based on multi-scale adaptive nested solution, comprising:

[0009] Obtain basic data and grouting material data of the target area and perform preprocessing;

[0010] The target area is divided into multiple geological layers. The boundaries, thickness, and interlayer relationships of each layer are described using a three-dimensional model to construct a geological model at a macroscale. Detailed information on soil particle morphology, size distribution, and pore structure is obtained to further construct a soil structure model at a microscale.

[0011] Coupling the geological model at the macro scale and the soil structure model at the micro scale;

[0012] When conducting grouting simulation, the scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions;

[0013] When performing simulation calculations on a macro-scale geological model, the macroscopic diffusion path and range of the grouting material are predicted based on the fluidity, viscosity and soil permeability data of the grouting material;

[0014] During the simulation process, when areas with uneven diffusion or sudden permeability changes are detected, the simulation data are transferred to the soil structure model at the microscale of the corresponding area;

[0015] When performing simulation calculations on the soil structure model at the micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model. The diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at the macro scale for data updating.

[0016] Furthermore, simulation calculations are performed on the geological model at a macro scale, specifically:

[0017] Set boundary conditions and initial parameters to construct the mass conservation equation for describing the diffusion and penetration of fluid in soil, the fluid dynamics equation for describing the flow behavior of slurry during the slurry process, and the permeation equation for describing the penetration process of slurry in the soil layer.

[0018] The geological model at the macro scale is divided into multiple discrete grid cells, and the mass conservation equation, fluid dynamics equation and permeability equation are discretized. The discretized equations are used to construct a linear equation system;

[0019] The linear equations are iteratively solved to obtain the pressure field, velocity field and concentration field at each time step.

[0020] Furthermore, simulation calculations are performed on the soil structure model at the micro scale, specifically:

[0021] Determining physical parameters of soil microstructure, including porosity, pore size distribution, interparticle spacing, and permeability;

[0022] Construct particle dynamics equations, particle drag force equations, inter-particle contact force equations, and fluid dynamics equations respectively;

[0023] The soil structure model at the micro scale is divided into multiple grid units, and the particle dynamics equation and the fluid dynamics equation are discretized;

[0024] For each time step, the fluid velocity field is calculated using the discrete fluid dynamics equations to update the fluid state. Based on the current state of the particles, the particle motion is updated using the discrete particle dynamics equations. Based on the updated particle motion, the drag force of the particles on the fluid is calculated, and the fluid velocity field is then updated.

[0025] Finally, the position and velocity of the particles at each time step, the velocity field and pressure field of the fluid, the permeability change of the slurry in the soil, and the interaction between the particles and the fluid are obtained.

[0026] Furthermore, the particle dynamics equation is specifically:

[0027]

[0028] Among them, m i is the mass of particle i, r i is the displacement of the particle, F i contact is the contact force between particles, F i drag is the drag force between the particle and the fluid, F i fluid is the buoyancy or other interaction force of the fluid on the particle;

[0029] The particle drag force equation is specifically:

[0030]

[0031] Where μ is the viscosity of the fluid, r i is the radius of the particle, v fluid is the velocity of the fluid, v i is the velocity of particle i;

[0032] The inter-particle contact force includes elastic force and friction force, where the elastic force is F elastic =kδ 3 / 2 ; Friction force is: F friction =μ f F normal ; where k is the contact stiffness, δ is the displacement between particles, μ f is the coefficient of friction, F normal is the normal force between particles;

[0033] The fluid dynamics equation is specifically:

[0034]

[0035] Among them, v is the fluid velocity field, p is the fluid pressure field, μ is the fluid viscosity, and f is the effect of external force on the fluid.

[0036] Furthermore, the scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions, specifically:

[0037] The coefficient of variation, the standard deviation of porosity and pore size distribution, the standard deviation of groundwater velocity and flow direction, the change of stress field gradient, and the degree of coupling of multi-physics fields were used as scoring indicators and standardized scores were given from 0 to 1;

[0038] The scores of each scoring indicator are weighted and summed to obtain the total score;

[0039] When the total score is not greater than the set threshold, the geological model at the macro scale is selected for simulation calculation; when the total score is greater than the set threshold, the soil structure model at the micro scale is selected for simulation calculation.

[0040] Furthermore, during the simulation process, when an area with uneven diffusion or a sudden change in permeability coefficient is detected, the simulation data is transferred to the soil structure model at the microscale of the corresponding area; the method for determining the area of ​​uneven diffusion is:

[0041] When the difference between the pressure in a certain area and the average pressure in the surrounding area exceeds the set threshold, the area is considered to be unevenly diffused.

[0042] When the difference between the slurry flow velocity in a certain area and the average slurry flow velocity in the surrounding area, and the ratio of the difference to the average flow velocity in the surrounding area exceed a set threshold range, the area is considered to be unevenly diffused.

[0043] Furthermore, during the simulation process, when areas with uneven diffusion or permeability mutations are detected, the simulation data is transferred to the soil structure model at the microscale of the corresponding area; the method for determining the area with permeability mutations is:

[0044] When the permeability coefficient fluctuation of a certain area exceeds the set range, it is considered that a permeability mutation has occurred in the area;

[0045] Alternatively, when the rate of change of the permeability coefficient in a certain area exceeds a set threshold, it is considered that a permeability mutation has occurred in the area.

[0046] In other embodiments, the following technical solutions are adopted:

[0047] A grouting intelligent simulation system based on multi-scale adaptive nested solution, comprising:

[0048] Data acquisition module, used to obtain basic data and grouting material data of the target area and perform preprocessing;

[0049] The model building module is used to divide the target area into multiple geological layers, describe the boundaries, thickness, and interlayer relationships of each layer through a three-dimensional model, and construct a geological model at a macro scale. It also obtains detailed information on the morphology, size distribution, and pore structure of soil particles, and further constructs a soil structure model at a micro scale.

[0050] Model coupling module, used to couple the geological model at the macro scale and the soil structure model at the micro scale;

[0051] The simulation calculation module is used to automatically determine the scale of the geological model for simulation calculation according to the complexity of the geological conditions when performing grouting simulation; when performing simulation calculations on the geological model at the macro scale, the macro diffusion path and range of the grouting material are predicted based on the fluidity, viscosity and soil permeability coefficient data of the grouting material; during the simulation process, when areas with uneven diffusion or sudden changes in permeability are detected, the simulation data are transferred to the soil structure model at the micro scale of the corresponding area; when performing simulation calculations on the soil structure model at the micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model, and the diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at the macro scale for data updating.

[0052] In other embodiments, the following technical solutions are adopted:

[0053] A terminal device includes a processor and a memory, the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for the processor to load and execute the above-mentioned grouting intelligent simulation method based on multi-scale adaptive nested solution.

[0054] In other embodiments, the following technical solutions are adopted:

[0055] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device to implement the above-mentioned grouting intelligent simulation method based on multi-scale adaptive nested solution.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) In the process of simulating grouting, the present invention performs multi-scale modeling, combines multi-level models at macro and micro scales, decomposes the geological structure in detail, and reproduces key details such as soil layers, soil particles and pore structures with higher accuracy; through multi-scale modeling, the scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions; complex geological conditions (such as multi-layer soil, uneven permeability and different porosity, etc.) can be nested and solved; avoid the phenomenon of reduced accuracy or data deviation under complex geological structures, make the simulation results more accurate in terms of pressure field, diffusion path, solidification state, etc., and provide reliable data support for grouting decision-making in high-risk geological environments.

[0058] (2) The multi-scale adaptive nested solution method of the present invention can quickly respond to parameter changes, such as adjusting grouting pressure and slurry viscosity, to optimize the expansion path of material penetration and transfer data between different levels. Through iterative solution and adaptive adjustment, collaborative optimization between different scales is achieved. With the help of parallel computing technology and hierarchical solution strategy, the amount of calculation can be effectively reduced, the allocation of computing resources can be optimized, and the accuracy requirements can be guaranteed. It can perform efficient calculations on large-scale soil layer models, significantly improve the simulation speed while meeting the project schedule requirements, and meet the calculation requirements in different engineering scenarios.

[0059] (3) The present invention simulates and calculates the soil structure model at the micro scale, which can accurately depict the diffusion and flow process of the grouting material in the soil pores, including the pore filling rate, slurry penetration path, and the mechanical interaction between particles. It can also reflect the impact of the slurry injection process on the soil microstructure (such as particle rearrangement and soil stress changes), providing guidance for grouting optimization. In particular, in areas with complex geological conditions or uneven pore distribution, it can provide more accurate predictions of local flow behavior. In addition, the calculation results of the soil structure model at the micro scale (such as pore permeability changes and filling efficiency) can be fed back to the geological model at the macro scale to update the parameters of the macro model in real time, making the simulation of the entire system more adaptive and consistent.

[0060] Other features and advantages of additional aspects of the present invention will be given in part in the following description and in part will become obvious from the following description or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the intelligent grouting simulation method based on multi-scale adaptive nested solution in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0063] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0064] Example 1

[0065] In one or more embodiments, a grouting intelligent simulation method based on multi-scale adaptive nested solution is disclosed, combined with Figure 1 , specifically including the following process:

[0066] S101: Obtain basic data and grouting material data of the target area and perform preprocessing.

[0067] In this embodiment, basic data of the target geological environment are collected and input, including parameters such as soil layer structure, porosity, and permeability coefficient; and physical and chemical parameters of the grouting material are collected and input, including parameters such as slurry viscosity, density, curing time, and fluidity.

[0068] After data collection is completed, the data is cleaned and calibrated as necessary to ensure its consistency and accuracy. The main preprocessing processes include: handling outliers and filling missing values, and standardizing data from different sources.

[0069] The system verifies the calibrated data multiple times to ensure that the input data meets the requirements of the actual geological environment and material properties, and accurately constructs a grouting model that conforms to the real environment.

[0070] S102: Divide the target area into multiple geological layers, describe the boundaries, thickness, and interlayer relationships of each layer through a three-dimensional model, and construct a geological model at a macro scale; obtain detailed information on the soil particle morphology, size distribution, and pore structure, and further construct a soil structure model at a micro scale (soil structure spatial geometric model).

[0071] In this embodiment, on a macro scale, the system divides the target area into multiple geological layers, describes the boundaries, thicknesses, and interlayer relationships of each layer through a three-dimensional model, and constructs a geological model on a macro scale. The model contains information such as the distribution, thickness, porosity, and permeability of different types of soil layers (sand, clay, silt, etc.). The model needs to define the location of the grouting source, the layout of the grouting pipes, and the fluid pressure and flow rate during grouting. The physical properties of the grouting material (rheology, viscosity, density, etc.) need to be set according to the type of slurry used to ensure that the diffusion, flow, and infiltration process of the slurry in the soil can be simulated. At the same time, the following physical parameters need to be set:

[0072] ① Permeability (porosity, permeability coefficient, etc.): Set the corresponding permeability data according to the soil type. These data can be obtained from exploration or measured through experiments.

[0073] ② Fluidity of grouting materials: By defining the rheological model of the slurry, describing the non-Newtonian fluid characteristics of the slurry (the specific choice of model needs to be determined according to the properties of the slurry itself), the flow and diffusion process of the slurry is simulated.

[0074] ③ Pressure boundary conditions: Set the pressure conditions at the grouting point, which is generally a conventional constant injection pressure or time-varying pressure.

[0075] ④ Boundary conditions and initial conditions: Set the soil boundary conditions (ground pressure, groundwater level, etc.), and set the static water pressure, original permeability coefficient, etc. of the soil layer at the initial moment.

[0076] Based on the geological model at the macro scale, the macroscopic diffusion path and range of the grouting material can be predicted through fluid dynamics modeling of the macroscopic diffusion process according to data such as the fluidity, viscosity and soil permeability coefficient of the grouting material. At the same time, by considering macroscopic factors that affect the diffusion of the grouting material, such as topographic changes and groundwater flow, the diffusion range and flow trend of the grouting material in the overall geological environment can be quickly obtained, providing boundary conditions and initial states for subsequent microscopic models. The specific modeling and solution process is explained in detail later.

[0077] At the microscopic scale, by using scanning electron microscopy (SEM) or X-ray computed tomography (XCT) on geological samples, detailed information on soil particle morphology, size distribution, and pore structure is obtained to construct a soil structure model at the microscopic scale. At the same time, the physical parameters of the soil microstructure are determined, mainly including:

[0078] ① Porosity (φ): The ratio of the volume of voids in the soil to the total volume, which affects the fluidity of the fluid.

[0079] ②Pore size distribution: Pores of different sizes will affect the flow behavior of fluid in the soil layer.

[0080] ③ Particle spacing: The gap between particles affects the resistance to fluid flow.

[0081] ④ Permeability (K): Estimate the permeability of the slurry based on the pore structure of the soil, which can be calculated using Darcy's law. Where q is the seepage flow rate, K is the permeability coefficient of the soil, and h is the pressure head.

[0082] Based on a microscopic soil structure model, the process of how the grouting material enters and fills the pores is considered, particularly the influence of viscosity and fluidity on pore filling. The diffusion and flow of slurry within the microstructure can be simulated based on different pore sizes and porosities. Since slurry injection may produce subtle mechanical effects on the soil, such as particle rearrangement and changes in soil stress, integrated mechanical analysis can accurately calculate the impact of the grouting material on the microscopic soil structure. The specific modeling and solution process is detailed later.

[0083] S103: Couple the geological model at the macro scale and the soil structure model at the micro scale.

[0084] Based on the flow field calculations of the macro-scale geological model, this embodiment embeds a micro-scale soil structure model for key areas (such as diffusion boundaries and areas with significant permeability changes). The micro-model then refines the solution for these key areas. Through a data transfer interface, the coupled model uses the flow results of the macro-model as boundary conditions for the micro-model and feeds the results of the micro-model back to the macro-model to update the overall permeability field.

[0085] S104: When performing grouting simulation, the scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions.

[0086] In this embodiment, a comprehensive scoring method is used to quantitatively judge the complexity of geological conditions and select the geological model scale accordingly.

[0087] Specifically, the coefficient of variation, the standard deviation of porosity and pore size distribution, the standard deviation of groundwater velocity and flow direction changes, the change of stress field gradient, and the degree of coupling of multiple physical fields were selected as scoring indicators. The soil layer heterogeneity, pore structure complexity, water flow complexity, stress field complexity, and physical process interaction complexity were standardized and scored from 0 to 1 respectively; then they were weighted and summed to obtain the total score; finally, the model scale was selected based on the total score.

[0088] The calculation method of each scoring indicator is as follows:

[0089] ① Soil layer heterogeneity: Scored using the coefficient of variation (CV); CV = σ / μ, where σ is the standard deviation of the soil layer parameter and μ is the mean of the soil layer parameter. The CV is scored using the following standardized method: CV < 0.2, simple, score 0; 0.2 ≤ CV < 0.5, moderate, score 0.5; CV ≥ 0.5, complex, score 1.

[0090] ② Pore structure complexity: Scored based on the porosity and standard deviation of pore size distribution; std(porosity) < 0.1, simple, score 0; 0.1 ≤ std(porosity) < 0.3, moderate, score 0.5; std(porosity) ≥ 0.3, complex, score 1. Note: std stands for standard deviation.

[0091] ③ Flow complexity: Scored based on the standard deviation of groundwater velocity and flow direction. If std(velocity) < 0.1, the system is simple and scored 0; if 0.1 ≤ std(velocity) < 0.3, the system is moderate and scored 0.5; and if std(velocity) ≥ 0.3, the system is complex and scored 1.

[0092] ④ Stress field complexity: score based on the changes in stress field gradient. Simple, score 0; medium, score 0.5; Complex, rated 1.

[0093] ⑤ Complexity of physical process interactions: If multi-physics field coupling is strong, the score is high; otherwise, the score is low. Low coupling (no coupling or weak coupling) is scored 0; medium coupling is scored 0.5; and strong coupling is scored 1.

[0094] The weighted sum of the scores of each evaluation factor is used to obtain the total score:

[0095] S total =w1·S heterogeneity +w2·S porosity +w3·S waterflow +w4·S stress +w5·S interaction ;

[0096] Among them, w1, w2, w3, w4, w5 are the weights of each factor (which can be set according to actual conditions), S heterogeneity , S porosity , S waterflow , S stress , S interaction They are the scoring values ​​of soil heterogeneity, pore structure complexity, water flow complexity, stress field complexity and physical process interaction complexity.

[0097] According to the total score S total , set a threshold Tmacro To choose the model scale:

[0098] If S total ≤T macro , choose a geological model at a macro scale, where the geological conditions are relatively homogeneous and the accuracy requirements are lower.

[0099] If S total >T macro , continue to evaluate whether it is necessary to select a soil structure model at the micro scale according to the accuracy requirements. If the accuracy requirements are high (such as complex soil structure and important particle interaction), select the soil structure model at the micro scale.

[0100] S105: When performing simulation calculations on a macro-scale geological model, the macroscopic diffusion path and range of the grouting material are predicted based on the fluidity, viscosity, and soil permeability data of the grouting material;

[0101] In this embodiment, the process of performing simulation calculations on a geological model at a macro scale is specifically as follows:

[0102] S1051: Set boundary conditions and initial parameters to construct the mass conservation equation for describing the diffusion and penetration of fluid in soil, the fluid dynamics equation for describing the flow behavior of slurry during the slurry process, and the permeation equation for describing the penetration process of slurry in the soil layer.

[0103] In this embodiment, the grouting process is essentially a fluid injection problem, so it is necessary to solve the mass conservation equation that describes the diffusion and penetration of the fluid in the soil. The mass conservation equation can be described using the following equation:

[0104]

[0105] Where θ is the concentration of the grouting liquid in the soil, v is the slurry flow velocity, D is the diffusion coefficient of the soil, and t is time.

[0106] During the grouting process, the flow behavior of the slurry is usually described by fluid mechanics equations. Especially when considering the characteristics of non-Newtonian fluids, the Navier-Stokes equations are used to describe the flow of slurry in soil:

[0107]

[0108] Among them, ρ is the fluid (slurry) density, v is the fluid velocity field, p is the fluid pressure field, μ is the fluid viscosity, and f is the effect of external force (such as gravity) on the fluid.

[0109] For non-Newtonian fluids, rheological properties are typically expressed using a rheological model: τ = η(γ)·γ, where τ is the shear stress, γ is the shear rate, and η(γ) is the shear viscosity of the fluid. This rheological model accurately reflects the flow characteristics of the slurry in different areas, particularly in areas with uneven geological conditions, helping to predict diffusion paths and filling effects.

[0110] Infiltration equation: The infiltration process in soil is usually described by Darcy's law:

[0111]

[0112] Among them, q is the infiltration flow rate per unit area, K is the permeability coefficient of the soil layer, and h is the hydraulic head.

[0113] S1052: Divide the geological model at the macroscale into multiple discrete grid cells, discretize the mass conservation equation, fluid dynamics equation, and permeability equation, and construct a linear equation system based on the discretized equations.

[0114] Specifically, each grid node represents a calculation point, the boundaries of the grid cells are connected through nodes, and the physical quantity within each cell is approximated by the interpolation function (linear interpolation) between the nodes.

[0115] The mass conservation equation is discretized in space and time respectively. The specific discretization process is prior art and will not be described in detail.

[0116] Discretize the fluid dynamics equations: Using the finite element method, the velocity field v and the pressure field p can be obtained by the shape function N i and unknown quantities at discrete nodes.

[0117] For the velocity field, the general form after discretization is:

[0118]

[0119] For the pressure field, the general form after discretization is:

[0120]

[0121] Where Ω represents the computational region, which is usually the entire simulation domain or a local region of a finite element, ρ is the fluid density, v is the fluid velocity field, which describes the motion state of the fluid at each point in space, and N i is a shape function, which represents the weight function based on the node position interpolation in the finite element and is used for discretization. is the gradient operator, which represents the rate of change in space, p is the pressure field, which represents the pressure of the fluid, μ is the viscosity of the fluid, K is the permeability, is the divergence operator, which describes the net outflow of a source or sink of a field (such as the spatial variation of pressure) in a given area, and f is the external volume force (unit: N / m 3 ), which can be external forces such as gravity and ground stress applied to the soil, p is the pressure field, h is the pressure head, q is the flow vector, is the gradient of the water head.

[0122] Discretize the permeability equation: The finite element method is also used to discretize the permeability equation. For Darcy's law, the changes in flow rate q and pressure head h within each finite element are usually integrated and discretized into nodes. The following discretized equation is obtained:

[0123]

[0124] Among them, N i is the shape function.

[0125] S1053: Iteratively solve the linear equations to obtain the pressure field, velocity field, and concentration field at each time step.

[0126] The discretized set of equations is constructed into a large linear system of equations, typically in the form of A·x=b, where A is the stiffness matrix representing the interaction between the soil and the slurry, x is the unknown vector to be solved (including physical quantities such as flow velocity and pressure), and b is the known quantity (such as external load and injection pressure). This linear system of equations is solved using an iterative method (conjugate gradient method) to obtain the pressure field, flow velocity field, and concentration field at each time step.

[0127] In this embodiment, the detailed calculation steps of the conjugate gradient method are as follows:

[0128] ① Initialization: Set the initial solution x0, calculate the initial residual r0 = b-Ax0, and set the initial search direction p0 = r0.

[0129] ② Iterative calculation: In the kth iteration, calculate the step factor: Update solution x k+1 =x k +α k p k , update the residual r k+1 =r k -α k Ap k , calculate the coefficients for the new directions: Update search direction p k+1 =r k+1 +β k p k .

[0130] ③Convergence judgment: If r k+1If the value is less than the preset threshold, the iteration is stopped.

[0131] At each time step, after solving the linear equations, the pressure field p, velocity field v, concentration field θ, etc. are updated. The final results obtained through numerical solution include: pressure field, velocity field, diffusion path and concentration field.

[0132] S106: During the simulation process, when an area with uneven diffusion or sudden permeability change is detected, the simulation data is transferred to the soil structure model at the micro scale of the corresponding area.

[0133] In this embodiment, at a macroscopic scale, the flow simulation of the grouting material in the soil layer is first performed to obtain the pressure field and velocity field of the grouting liquid. By analyzing the changes in these fields, it can be determined whether the diffusion is uniform: ① The pressure field of the slurry is usually higher near the injection point, and the pressure gradually decreases away from the injection point. If the pressure mutation in a certain area is large, it means that the diffusion in this area is uneven, and the permeability of the slurry in this area has changed. ② The velocity field of the slurry can provide information about the diffusion speed of the slurry. In the soil layer, the severity of the flow velocity change can reflect the uniformity of the diffusion. If the flow velocity in some areas is significantly faster (possibly a low permeability area), or the flow velocity in some areas is slower (possibly a high permeability area), it means that the diffusion of the grouting material is uneven.

[0134] By monitoring changes in the soil's permeability coefficient (porosity, permeability, and other parameters) and combining this with the fluidity and viscosity of the grouting fluid, the permeability behavior of the grout in different soil layers can be calculated. A sudden change in the permeability coefficient indicates a significant change in the soil's permeability. If the permeability coefficient of a particular area varies significantly compared to adjacent areas, or if the permeability coefficient significantly increases or decreases, this may be due to changes in the soil's structure (such as cracks or changes in porosity), leading to deviations in the grouting fluid's permeability.

[0135] Specifically, the method for determining the area of ​​uneven diffusion is as follows:

[0136] ① Pressure difference: set a threshold range, such as [-20%, +20%]; when the pressure P of a certain area is different from the average pressure of the surrounding area, The ratio of the difference to the average pressure of the surrounding area When the threshold range is exceeded, the area is considered to have uneven diffusion.

[0137] ② Flow velocity difference: In the flow velocity field analysis, when the ratio of the difference between the slurry flow velocity in a certain area and the average slurry flow velocity in the surrounding area to the average slurry flow velocity in the surrounding area exceeds the set threshold range, such as [-20%, +20%], the area is considered to have uneven diffusion.

[0138] The method for determining the region of permeability mutation is:

[0139] ① Change in permeability coefficient: By setting a change range of the permeability coefficient (the change in the permeability coefficient exceeds ±15%), when the permeability coefficient of a certain area fluctuates beyond this range, it can be considered that the permeability of the area has undergone a significant change.

[0140] ② Local permeability change rate: This defines the rate of change of the local permeability coefficient (the rate of change of the permeability coefficient per unit time). When the change rate exceeds a certain threshold (the specific threshold can be adjusted according to different projects and soil characteristics), the permeability of the area is considered to have changed significantly.

[0141] S107: When performing simulation calculations on the soil structure model at the micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model. The diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at the macro scale for data update.

[0142] In this embodiment, the process of performing simulation calculation on the soil structure model at the micro scale is specifically as follows:

[0143] S1071: Determine the physical parameters of the soil microstructure; physical parameters include: porosity, pore size distribution, interparticle spacing, and permeability.

[0144] S1072: Construct the particle dynamics equation, particle drag force equation, inter-particle contact force equation and fluid dynamics equation respectively.

[0145] Specifically, at the microscale, the modeling of fluid mechanics and particle interaction can choose the discrete element method (DEM) combined with the Navier-Stokes equations to describe the flow of grouting fluid between soil particles and the interaction between particles. The interaction between particles and fluid is modeled through the resistance term.

[0146] (1) Particle dynamics equation (Euler-Lagrange equation): The motion of each particle can be described by the Euler-Lagrange equation. For particle i, the force equation is:

[0147]

[0148] Among them, m i is the mass of particle i, r i is the displacement of the particle, F contact i is the contact force between particles (such as elastic force, friction force), F drag i is the drag force between the particle and the fluid, Ffluid i It is the buoyancy or other interaction force of the fluid on the particles.

[0149] (2) Drag force (Stokes Drag Force): Under low Reynolds number conditions (when the grouting fluid flows in small pores, the flow velocity is usually low), the interaction between particles and fluid can be described by Stokes' law. For a single particle, the drag force is:

[0150]

[0151] Where μ is the viscosity of the fluid, r i is the radius of the particle, v fluid is the velocity of the fluid, v i is the velocity of particle i.

[0152] (3) Interparticle contact force: The interparticle contact force is usually composed of elastic force and friction force, which is described by the Hertz contact model and the Coulomb friction model.

[0153] ① Elastic force (Hertz model): F elastic =kδ 3 / 2 , where k is the contact stiffness and δ is the displacement between particles (overlap).

[0154] ②Friction (Coulomb friction model): F friction =μ f F normal , where μ f is the coefficient of friction, F normal is the normal force between particles.

[0155] (4) Fluid dynamics equation: Navier-Stokes equation

[0156] Assume that the soil is composed of several spherical particles, and the physical properties of the particles such as radius, mass, stiffness and friction coefficient are set. For each particle i, its mass m i , radius r i and initial velocity v i is known.

[0157] The movement of particles affects the flow of fluids, and the flow of fluids also affects the movement of particles. Therefore, the velocity field of the fluid needs to be modified according to the movement of particles. The influence of particles is introduced by modifying the Navier-Stokes equations of the fluid. For example, the particle drag force term F drag This will affect the velocity field of the fluid.

[0158] S1073: Divide the soil structure model at the microscale into multiple grid cells and discretize the particle dynamics equations and fluid dynamics equations.

[0159] In this embodiment, the discrete element method (DEM) is used to simulate the motion of the particles, and the motion state of each particle at each time step is solved.

[0160] S1074: For each time step, the fluid velocity field is calculated by discrete fluid dynamics equations to update the state of the fluid; according to the current state of the particles, the motion of the particles is updated by discrete particle dynamics equations; based on the updated particle motion, the drag force of the particles on the fluid is calculated, and then the velocity field of the fluid is updated; the iterative update of the particle and fluid states is repeated until the convergence condition is reached or the time ends.

[0161] S1075: Finally, the position and velocity of the particles at each time step, the velocity field and pressure field of the fluid, the permeability change of the slurry in the soil, and the interaction between the particles and the fluid are obtained.

[0162] Finally, the following simulation results can be output:

[0163] (1) Particle position and velocity: Output the motion trajectory, velocity, acceleration, etc. of the particle at each time step.

[0164] (2) Fluid velocity field and pressure field: The fluid velocity field v and pressure field p at each time step are obtained through numerical simulation, and the flow and diffusion behavior of the fluid are analyzed.

[0165] (3) Permeability analysis: Calculate the permeability changes of the slurry in the soil at different time steps, which can be calculated using Darcy's law.

[0166] (4) Analysis of the interaction between particles and fluids: Based on the motion state of the particles, analyze the impact of the particles on the fluid, including the motion resistance of the particles, the effect of the drag force on the fluid, etc.

[0167] S108: During the simulation process, the system automatically adjusts parameters such as curing time, grouting pressure, and slurry viscosity. Grouting parameter adjustments are automatically optimized in real time based on on-site conditions. The system automatically performs accuracy checks after each solution cycle and dynamically switches the solution scale based on accuracy requirements. During the feedback iteration process, the system ensures that the solution meets the predetermined error control requirements by gradually converging the solution error.

[0168] Through continuous feedback iteration between the macro and micro scales, model parameters (such as fluidity, pressure, and diffusion coefficient) are continuously adjusted, allowing the results of the two-level model to gradually approach the actual grouting process. During the nested solution process, intelligent algorithms (such as genetic algorithms and particle swarm optimization algorithms) are used to adjust the solution parameters to ensure the accuracy of the results.

[0169] Taking the parameter adjustment method in particle swarm optimization (PSO) as an example, the specific process is as follows:

[0170] (1) Initialize the particle swarm: Randomly initialize the particle swarm. The position of each particle represents a set of parameter values, and the velocity is used to adjust the position of the particle.

[0171] (2) Calculate fitness: For each particle, use the model to simulate and calculate the error between the current solution and the actual data (i.e., fitness).

[0172] (3) Update particle speed and position: ① Each particle updates its speed: v i t+1 =w·v i t +c1·r1·(p best,i -x i t )+c2·r2.(g best -x i t ), where v i t is the current velocity of the particle, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers, p best,i is the particle's personal best position, g best is the global optimal position. ② Update the particle position: x i t+1 =x i t +v i t+1 .

[0173] (4) Iterative update: Repeatedly calculate the fitness and update the position and velocity of the particle until the stopping criterion is met (such as the fitness reaches a certain threshold or the maximum number of iterations is reached).

[0174] S109: The grouting simulation process and results are presented in a rich and intuitive manner through a graphical interface, showing the three-dimensional structure of the soil layer, the three-dimensional dynamics of slurry diffusion, the flow field distribution diagram, the diffusion contour diagram, the pore filling rate diagram, the pressure field distribution diagram, and the parameter change trend diagram. At the same time, a detailed grouting project report is automatically generated based on the simulation results, including grouting parameters, diffusion effect, soil layer structure, flow field distribution and other contents.

[0175] S110: By analyzing simulation results and user input, it identifies potential issues and optimization opportunities during the grouting process and provides users with precise improvement recommendations. This module uses dynamic evaluation and data-driven methods to comprehensively optimize multiple aspects, including pressure, diffusion range, grouting uniformity, and material consumption. Combined with historical data comparison and intelligent algorithm recommendations, it helps engineers adjust grouting strategies in a timely manner, achieving more efficient and economical grouting operations.

[0176] Example 2

[0177] In one or more embodiments, a grouting intelligent simulation system based on multi-scale adaptive nested solution is disclosed, comprising:

[0178] Data acquisition module, used to obtain basic data and grouting material data of the target area and perform preprocessing;

[0179] The model building module is used to divide the target area into multiple geological layers, describe the boundaries, thickness, and interlayer relationships of each layer through a three-dimensional model, and construct a geological model at a macro scale. It also obtains detailed information on the morphology, size distribution, and pore structure of soil particles, and further constructs a soil structure model at a micro scale.

[0180] Model coupling module, used to couple the geological model at the macro scale and the soil structure model at the micro scale;

[0181] The simulation calculation module is used to automatically determine the scale of the geological model for simulation calculation according to the complexity of the geological conditions when performing grouting simulation; when performing simulation calculations on the geological model at the macro scale, the macro diffusion path and range of the grouting material are predicted based on the fluidity, viscosity and soil permeability coefficient data of the grouting material; during the simulation process, when areas with uneven diffusion or sudden changes in permeability are detected, the simulation data are transferred to the soil structure model at the micro scale of the corresponding area; when performing simulation calculations on the soil structure model at the micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model, and the diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at the macro scale for data updating.

[0182] The specific implementation of each of the above modules is the same as that in Example 1 and will not be described in detail.

[0183] Example 3

[0184] In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the grouting intelligent simulation method based on multi-scale adaptive nested solution described in Example 1.

[0185] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0186] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0187] During implementation, each step of the above method may be completed by an integrated logic circuit of hardware in a processor or by instructions in the form of software.

[0188] Example 4

[0189] In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device to implement the grouting intelligent simulation method based on multi-scale adaptive nested solution described in Example 1.

[0190] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A grouting intelligent simulation method based on multi-scale adaptive nested solution, characterized in that: include: Obtain basic data and grouting material data of the target area and perform preprocessing; Divide the target area into multiple geological layers, describe the boundaries, thickness and inter-layer relationships of each layer through a three-dimensional model, and construct a geological model at a macro scale; obtain detailed information on the morphology, size distribution and pore structure of soil particles, and further construct a soil structure model at a micro scale; Coupling the geological model at the macro scale and the soil structure model at the micro scale; When grouting simulation is performed, the scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions; When performing simulation calculations on a geological model at a macroscopic scale, the macroscopic diffusion path and range of the grouting material are predicted based on the fluidity, viscosity and soil permeability data of the grouting material; During the simulation process, when areas with uneven diffusion or sudden permeability changes are detected, the simulation data are transferred to the soil structure model at the microscale of the corresponding area; When performing simulation calculations on the soil structure model at the micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model. The diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at the macro scale for data updating.

2. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 1, characterized in that: The simulation calculation is carried out on the geological model at the macro scale, specifically: Set boundary conditions and initial parameters, and construct the mass conservation equation for describing the diffusion and penetration of fluid in soil, the fluid dynamics equation for describing the flow behavior of slurry in the slurry process, and the permeability equation for describing the permeation process of slurry in the soil layer; The geological model at the macroscopic scale is divided into multiple discrete grid units, the mass conservation equation, fluid dynamics equation and permeability equation are discretized, and the discretized equations are used to construct a linear equation system; The linear equations are iteratively solved to obtain the pressure field, velocity field and concentration field at each time step.

3. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 1, characterized in that: The simulation calculation is carried out on the soil structure model at the micro scale, specifically: Determining physical parameters of soil microstructure, including porosity, pore size distribution, interparticle spacing, and permeability; The particle dynamics equation, particle drag force equation, inter-particle contact force equation and fluid dynamics equation are constructed respectively; The soil structure model at the microscopic scale is divided into multiple grid units, and the particle dynamics equation and the fluid dynamics equation are discretized; For each time step, the fluid velocity field is calculated by discrete fluid dynamics equations to update the state of the fluid; according to the current state of the particles, the movement of the particles is updated by discrete particle dynamics equations; based on the updated particle movement, the drag force of the particles on the fluid is calculated, and then the velocity field of the fluid is updated; Finally, the position and velocity of the particles at each time step, the velocity field and pressure field of the fluid, the permeability change of the slurry in the soil, and the interaction between the particles and the fluid are obtained.

4. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 3 is characterized in that: The particle dynamics equation is specifically: Among them, m i is the mass of particle i, r i is the displacement of the particle, F i contact is the contact force between particles, F i drag is the drag force between the particle and the fluid, F i fluid is the buoyancy or other interaction force of the fluid on the particle; The particle drag force equation is specifically: Where μ is the viscosity of the fluid, r i is the radius of the particle, v fluid is the velocity of the fluid, v i is the velocity of particle i; The inter-particle contact force includes elastic force and friction force, where the elastic force is F elastic = kδ 3 / 2 ; Friction force: F friction =μ f F normal ; where k is the contact stiffness, δ is the displacement between particles, μ f is the friction coefficient, F normal is the normal force between particles; The fluid dynamics equation is specifically: Among them, v is the fluid velocity field, p is the fluid pressure field, μ is the fluid viscosity, and f is the effect of external force on the fluid.

5. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 1, characterized in that: The scale of the geological model for simulation calculation is automatically determined according to the complexity of the geological conditions, specifically: The coefficient of variation, standard deviation of porosity and pore size distribution, standard deviation of groundwater velocity and flow direction, change of stress field gradient, and degree of coupling of multi-physics fields were used as scoring indicators and standardized scores were given from 0 to 1; The scores of each scoring indicator are weighted and summed to obtain the total score; When the total score is not greater than the set threshold, the geological model at the macro scale is selected for simulation calculation; when the total score is greater than the set threshold, the soil structure model at the micro scale is selected for simulation calculation.

6. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 1, characterized in that: During the simulation process, when an area with uneven diffusion or sudden change in permeability coefficient is detected, the simulation data is transmitted to the soil structure model at the microscopic scale of the corresponding area; the method for determining the area with uneven diffusion is: When the difference between the pressure in a certain area and the average pressure in the surrounding area, and the ratio of the average pressure in the surrounding area exceeds the set threshold range, the area is considered to be unevenly diffused; When the difference between the slurry flow velocity in a certain area and the average slurry flow velocity in the surrounding area, and the ratio of the average flow velocity in the surrounding area to the average flow velocity in the surrounding area exceed the set threshold range, the area is considered to be unevenly diffused.

7. The grouting intelligent simulation method based on multi-scale adaptive nested solution according to claim 1, characterized in that: During the simulation process, when an area with uneven diffusion or permeability mutation is detected, the simulation data is transmitted to the soil structure model at the microscopic scale of the corresponding area; the method for determining the area with permeability mutation is: When the permeability coefficient fluctuation of a certain area exceeds the set range of variation, it is considered that a permeability mutation has occurred in the area; Alternatively, when the rate of change of the permeability coefficient of a certain area exceeds a set threshold, it is considered that a permeability mutation has occurred in the area.

8. A grouting intelligent simulation system based on multi-scale adaptive nested solution, characterized in that: include: Data acquisition module, used to acquire basic data and grouting material data of the target area and perform preprocessing; The model building module is used to divide the target area into multiple geological layers, describe the boundaries, thickness and inter-layer relationships of each layer through a three-dimensional model, and build a geological model at a macro scale; obtain detailed information on the morphology, size distribution and pore structure of soil particles, and further build a soil structure model at a micro scale; Model coupling module, used to couple the geological model at the macro scale and the soil structure model at the micro scale; A simulation calculation module is used to automatically determine the scale of the geological model for simulation calculation according to the complexity of geological conditions when performing grouting simulation; When performing simulation calculations on a geological model at a macro scale, the macroscopic diffusion path and range of the grouting material are predicted based on the fluidity, viscosity and soil permeability coefficient data of the grouting material; during the simulation process, when areas with uneven diffusion or sudden changes in permeability are detected, the simulation data are transmitted to the soil structure model at a micro scale of the corresponding area; when performing simulation calculations on a soil structure model at a micro scale, the flow results of the geological model are used as the boundary conditions of the soil structure model, and the diffusion and flow of the slurry in the soil structure model are simulated according to different pore sizes and porosities, and the simulation results are fed back to the geological model at a macro scale for data updating.

9. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the grouting intelligent simulation method based on multi-scale adaptive nested solution as described in any one of claims 1-7.

10. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the grouting intelligent simulation method based on multi-scale adaptive nested solution as described in any one of claims 1-7.

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