Slurry permeation discrete element fluid-solid coupling optimization simulation method based on pore morphology characteristics

Abstract

The invention provides a slurry permeation discrete element fluid-solid coupling optimization simulation method based on pore morphological characteristics, which comprises the following steps of: obtaining spatial geometric information of sand through a CT (Computed Tomography) scanning method and a digital three-dimensional reconstruction technology, and establishing an STL (Standard Template Library) three-dimensional graphic file of a sand stratum; and importing the STL file into a computational fluid dynamics-discrete element coupling program to establish a pore flow field grid and a discrete element wall boundary, and establishing a discrete element fluid-solid coupling calculation model, thereby realizing the calculation demand of restoring the formation pore morphology characteristics. In order to reduce the calculation amount and improve the calculation efficiency, the total number of slurry particles calculated in real time is reduced in a discrete element module through a method of circularly inserting belt speed particles and deleting filter-off particles. By utilizing the method, original size simulation can be carried out according to the actual shape of an actual stratum and the actual size of actual slurry particles, and a new thought is provided for the research method of the shield tunnel excavation face slurry permeation micromechanism.

Description

Technical Field

[0001] The invention relates to the fields of three-dimensional digital core reconstruction, multi-porous structure seepage flow field analysis, shield tunnel excavation face stability analysis and mud penetration discrete element fluid-solid coupling numerical simulation. Background Art

[0002] The slurry shield method is the main method for tunnel construction in water-rich strata in my country. The quality of the mud film is the key to the stability of the excavation face. Once the excavation face is unstable, it will pose a serious threat to the safety of personnel and property in the tunnel. The research method of mud film formation, from a macroscopic perspective, usually uses mud penetration column tests to qualitatively analyze the relationship between mud and strata. However, the mechanism of mud film formation is complex, and the microscopic mechanism research based on this method is not in-depth; from a microscopic perspective, CFD-DEM (computational fluid dynamics-discrete element) coupling method is usually used to simulate slurry flow from the perspective of studying the interaction between particles and fluids. This method can capture the dynamic change trend of mud particles and analyze the entire process of mud particle penetration and film formation. However, the commonly used method at present is to simulate the sand particles of the stratum as spherical particles, ignoring the flow field changes caused by the difference in sand morphology. At the same time, the computing resource conditions severely limit the number of particles calculated.

[0003] With the advancement of technology, scanning electron microscope (SEM) and X-ray tomography (CT scanning) are gradually being used to analyze the pore characteristics of rock and soil. Unlike SEM, which only observes the two-dimensional structural characteristics of the sample surface, CT scanning, as a non-destructive testing technology, can observe the internal characteristics of the sample. A large number of studies have shown that CT scanning technology is one of the most reasonable and accurate methods for characterizing the microscopic pore structure characteristics of rock and soil. Summary of the invention

[0004] The present invention aims to optimize the existing computational fluid dynamics-discrete element coupling model based on CT scanning technology to achieve the purpose of characterizing pore morphology and improving calculation rate.

[0005] Technical solution:

[0006] A discrete element fluid-solid coupling optimization simulation method for mud penetration based on pore morphology characteristics obtains the spatial geometric information of sand through CT scanning method and digital three-dimensional reconstruction technology, and establishes an STL three-dimensional graphic file of the sand layer; the STL three-dimensional graphic file is imported into the computational fluid dynamics-discrete element coupling program to establish the pore flow field grid and discrete element wall boundary, and establish a discrete element fluid-solid coupling calculation model; in the discrete element module, the total number of mud particles in real-time calculation is reduced by cyclically inserting speed particles and deleting filtration loss particles.

[0007] The method includes the following four steps:

[0008] Step 1: Pre-treat the sand sample and perform three-dimensional reconstruction using CT scanning technology;

[0009] Step 2: pre-treat the slurry and use instruments to measure the rheological parameters of the slurry;

[0010] Step 3: Based on the actual formation and slurry conditions, a discrete element fluid-solid coupling calculation model of mud penetration is established using the computational fluid dynamics-discrete element coupling program;

[0011] Step 4: Use the CFD-DEM coupling procedure to insert velocity particles and delete filtration particles in the DEM module at the coupled time step.

[0012] Beneficial effects of the present invention:

[0013] The present invention optimizes the conventional computational fluid dynamics-discrete element coupling model based on the rock and soil pore network established by CT scanning, thereby achieving the purpose of characterizing the pore morphology and improving the calculation rate. The method of the present invention can perform full-scale simulation based on the real morphology of the actual stratum and the real size of the actual slurry particles, providing a new idea for the research method of the microscopic mechanism of slurry penetration in the excavation face of a shield tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart of the discrete element fluid-solid coupling optimization simulation method for mud penetration based on pore morphology characteristics.

[0015] Figure 2 Schematic diagram of the computational fluid dynamics-discrete element method (CFD-DEM) coupling method.

[0016] Figure 3 Schematic diagram of the mud suspension shear model (“Model 2”).

[0017] Figure 4 Schematic diagram of cyclically placing and deleting particles with speed. DETAILED DESCRIPTION

[0018] The technical solution provided by the present application will be further described below in conjunction with specific embodiments and accompanying drawings. The advantages and features of the present application will become more apparent with the following description.

[0019] like Figure 1 Detailed description shown:

[0020] Step 1: Pre-treat the sand sample and reconstruct it in three dimensions using CT scanning technology.

[0021] Step 1.1: Screen the sand and measure its density. According to the grading and porosity requirements, prepare the sand sample by layered compaction to eventually form a cylindrical sample with a diameter of D and a height of H.

[0022] Step 1.2 performs CT scanning and image processing on the prepared sand sample. The image processing includes extracting the range of the sand layer by a threshold segmentation method based on a watershed algorithm. The complement of the range of the sand layer is the pore area.

[0023] Step 2: Pre-treat the slurry and use instruments to measure the rheological parameters of the slurry.

[0024] Step 2.1: Prepare a slurry with a concentration of n using bentonite according to the required mass fraction of the slurry.

[0025] Step 2.2 After the bentonite is completely hydrated, the particle size distribution of the mud particles is measured using a laser particle size analyzer, and the rheological parameters of the slurry, such as viscosity, shear stress, and shear rate, are measured using a rheometer.

[0026] Step 3: According to the actual formation and slurry conditions, a discrete element fluid-solid coupling calculation model of mud penetration (referred to as "Model 1") is established using the computational fluid dynamics-discrete element coupling program (referred to as "Software 1"), such as Figure 2 shown.

[0027] The computational fluid dynamics-discrete element coupling program ("Software 1") contains two subordinate libraries, namely the PISO (Pressure Implicit with Splitting of Operators) algorithm solver ("Software 2") and the discrete element solver based on Newton's third law ("Software 3"). "Software 1" exchanges the data calculated by "Software 2" and "Software 3" at a fixed time step.

[0028] The discrete element fluid-solid coupling calculation model ("Model 1") includes a computational fluid dynamics model and a discrete element model.

[0029] Step 3.1 Generate an STL three-dimensional image file of sand for subsequent model building;

[0030] 3.1.1 After the three-dimensional reconstruction of the sand sample is completed, the representative units of the cube are cut out and exported as STL three-dimensional image files in ASCII encoding standard.

[0031] 3.1.2 Optimize the triangular mesh of the acquired STL three-dimensional image file, including deleting overlapping vertices and faces, simplifying the mesh, reducing the number of neighbors of the face, and repairing holes to meet the file import requirements.

[0032] Step 3.2: Perform computational fluid dynamics simulation in the PISO algorithm solver of the subordinate library of "Software 1" (referred to as "Software 2"), establish a computational fluid dynamics model, calibrate the physical and mechanical parameters, and set the boundary conditions of the flow field, including the following:

[0033] 3.2.1 Generate a specified hexahedral mesh through commands, and fit the generated hexahedral mesh with the topological structure of the STL three-dimensional image file through commands based on the STL three-dimensional image file to generate a pore fluid mesh of the formation.

[0034] 3.2.2 Considering that mud is a non-Newtonian fluid, in the process of model simplification, the liquid phase of the slurry is simulated as a pore fluid grid, described by Newtonian fluid, and the solid phase of the slurry is simulated as discrete element particles. Since particles will disturb the fluid and cause the fluid viscosity to increase, it is necessary to establish a mud suspension shear model ("Model 2") for parameter calibration, such as Figure 3 As shown in the figure, the flow field grid has periodic boundaries on both sides and fixed velocity boundaries at the top and bottom. s , elastic modulus E s and the density of the fluid unit ρ w Then, by changing the viscosity μ of the fluid unit w , calculate the shear force on the fluid, and determine the final viscosity of the fluid unit when the force result meets the measured rheological parameters (shear stress, shear rate, etc.).

[0035] 3.2.3 In the computational fluid dynamics model of the discrete element fluid-solid coupling calculation model (“Model 1”), the required pressure difference boundaries are set at the slurry inlet and outlet, and the remaining boundaries are set as non-slip boundaries to meet the requirements.

[0036] Step 3.3: Perform discrete element simulation in the discrete element solver based on Newton's third law ("software three") of the slave library of the computational fluid dynamics-discrete element coupling program ("software one") to establish a discrete element model, specifically:

[0037] The STL three-dimensional image file is imported into the discrete element model in the form of a wall to restrict the movement of particles. Since bentonite can form a stable suspension in water and exhibits colloidal properties, the DLVO (Derjaguin-Landau-Verwey-Overbeek) contact model based on the diffuse electron layer theory is added to the simulated solid phase. It is believed that electrostatic repulsion and van der Waals attraction exist before the particles come into contact, and their size is related to the distance between the particle surfaces.

[0038] The van der Waals force can be expressed as:

[0039]

[0040] in:

[0041] D is the distance from the outer surface of the particle (unit: m);

[0042] A is the Hamaker constant, usually 7.8×10 -20 J;

[0043] R i is the radius of the ith particle (unit: m);

[0044] The electrostatic repulsion can be expressed as:

[0045]

[0046] in:

[0047] κ is the reciprocal of the Debye length, which is 4.136×10 6 m -1 ;

[0048] Z is the interaction constant (unit: J / m) and can be expressed as:

[0049]

[0050] in:

[0051] ψ 0 is the particle surface potential, and its value is related to the zeta potential.

[0052] After completing the parameter selection of "Model 1", set the coupling step of computational fluid dynamics and discrete element in "Software 1" to exchange flow field and particle data during coupling.

[0053] Step 4: Using the computational fluid dynamics-discrete element coupling program ("Software 1"), insert the velocity particles and delete the filtration particles in the discrete element module at the coupling time step.

[0054] A particle delivery area (referred to as "area one") is set on the upper part of the wall established by the STL file, a particle velocity detection area (referred to as "area two") is set between the STL file and "area one", and a particle deletion area (referred to as "area three") is set at the lower part of the STL file. Figure 4 shown.

[0055] Each time the computational fluid dynamics and discrete data are exchanged, the average velocity of the particles in "region two" is detected, and the particles in "region three" are deleted.

[0056] Each time the computational fluid dynamics and discrete metadata are exchanged, the number of particles in "area one" is detected. When the number of particles is less than 0.1% of the number of particles released at a single time, particles are released in "area one" again, and the speed of particles released in "area one" is set to the average speed of particles detected in "area two".

[0057] The above description is only a description of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any changes or modifications made by any person skilled in the art based on the above disclosed technical contents shall be deemed as equivalent effective embodiments and shall fall within the scope of protection of the technical solution of the present application.

Claims

1. A slurry permeability discrete element fluid-solid coupling optimization simulation method based on pore morphology characteristics, characterized in that: The spatial geometric information of sand is obtained through CT scanning method and digital three-dimensional reconstruction technology, and the STL three-dimensional graphic file of the sand layer is established; the STL three-dimensional graphic file is imported into the computational fluid dynamics-discrete element coupling program to establish the pore flow field grid and discrete element wall boundary, and establish a discrete element fluid-solid coupling calculation model; in the discrete element module, the total number of mud particles in real-time calculation is reduced by cyclically inserting speed particles and deleting filtration particles.

2. The method according to claim 1, characterized in that Includes steps: Step 1: Pre-treat the sand sample and perform three-dimensional reconstruction using CT scanning technology; Step 2: pre-treat the slurry and use instruments to measure the rheological parameters of the slurry; Step 3: A discrete element fluid-solid coupling calculation model of mud penetration is established using a computational fluid dynamics-discrete element coupling program according to actual formation and slurry conditions; the discrete element fluid-solid coupling calculation model includes two models, namely a computational fluid dynamics model and a discrete element model; Step 4: Use the CFD-DEM coupling procedure to insert velocity particles and delete filtration particles in the DEM module at the coupled time step.

3. The method according to claim 2, characterized in that The step 1 comprises: Step 1.1: Screen the sand and measure its density, and prepare the sand sample by layered compaction according to the gradation and porosity requirements; Step 1.2 performs CT scanning and image processing on the prepared sand sample. The image processing includes extracting the range of the sand layer by a threshold segmentation method based on a watershed algorithm. The complement of the range of the sand layer is the pore area.

4. The method according to claim 2, characterized in that The step 2 comprises: Step 2.1: preparing mud using bentonite according to the required mass fraction of the mud; Step 2.2 After the bentonite is completely hydrated, the particle size distribution of the slurry particles is measured using a laser particle size analyzer, and the rheological parameters of the slurry are measured using a rheometer.

5. The method according to claim 2, characterized in that Step 3: The computational fluid dynamics-discrete element coupling program contains two subordinate libraries, namely the PISO (Pressure Implicit with Splitting of Operators) algorithm solver and the discrete element solver based on Newton's third law. The computational fluid dynamics-discrete element coupling program exchanges the data calculated by the PISO algorithm solver and the discrete element solver at a fixed time step; Among them, the discrete element fluid-solid coupling calculation model includes two models, namely the computational fluid dynamics model and the discrete element model; The step 3 comprises: Step 3.1 Generate an STL three-dimensional image file of sand; 3.1.1 After completing the three-dimensional reconstruction, the sand sample is exported as an STL three-dimensional image file with ASCII coding standard; 3.1.2 Optimize the triangular mesh of the acquired STL three-dimensional image file, including deleting overlapping vertices and faces, simplifying the mesh, reducing the number of neighbors of the face, and repairing holes; Step 3.2: Perform computational fluid dynamics simulation in the PISO algorithm solver to establish a computational fluid dynamics model; Step 3.3 performs discrete element simulation in a discrete element solver based on Newton's third law to establish a discrete element model.

6. The method according to claim 5, characterized in that In step 3.2, a pore fluid grid of the formation is generated, and reasonable physical and mechanical parameters of the fluid grid are set in combination with the rheological parameters of the slurry measured in step 2, and boundary conditions of the flow field are set, which specifically include the following: 3.2.1 Generate a specified hexahedral mesh through commands, and fit the generated hexahedral mesh with the topological structure of the STL three-dimensional image file through commands based on the STL three-dimensional image file to generate a pore fluid mesh of the formation; 3.2.2 Establish the shear model of mud suspension for parameter calibration. After determining the particle density ρ s , elastic modulus E s and the density of the fluid unit ρ w Then, by changing the viscosity μ of the fluid unit w , calculate the shear force on the fluid, and determine the final fluid unit viscosity when the force result meets the measured rheological parameters; 3.2.3 The required pressure difference boundaries are set at the inlet and outlet of the slurry, and the remaining boundaries are set as non-slip boundaries to meet the requirements.

7. The method according to claim 5, characterized in that In step 3.3: The STL three-dimensional image file is imported into the discrete element model in the form of a wall to restrict the movement of particles; The DLVO (Derjaguin-Landau-Verwey-Overbeek) contact model based on the diffuse electron layer theory is added to the simulated solid phase, which assumes that electrostatic repulsion and van der Waals attraction exist before particles come into contact. The van der Waals force is expressed as: in: D is the distance from the outer surface of the particle, unit: m; A is the Hamaker constant, usually 7.8×10 -20 J; R i is the radius of the ith particle, unit: m; The electrostatic repulsion is expressed as: in: κ is the reciprocal of the Debye length, which is 4.136×10 6 m -1 ; Z is the interaction constant, unit: J / m, expressed as: in: ψ0 is the particle surface potential, and its value is related to the zeta potential.

8. The method according to claim 2, characterized in that Step 4 includes: Step 4.1 Set a particle delivery area, i.e., "area 1", on the upper part of the wall created by the STL file, set a particle velocity detection area, i.e., "area 2", between the STL file and "area 1", and set a particle deletion area, i.e., "area 3", on the lower part of the STL file; Step 4.2: Each time the computational fluid dynamics and discrete metadata are exchanged, the average velocity of the particles in "region 2" is detected, and the particles in "region 3" are deleted; Step 4.3: Each time the computational fluid dynamics and discrete metadata are exchanged, the number of particles in "area one" is detected. When the number of particles is less than 0.1% of the number of particles released at a single time, particles are released in "area one" again, and the speed of the particles released in "area one" is set to the average speed of the particles detected in "area two".

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