A shotcrete optimization method and system based on DEM-CFD coupling

Abstract

The present invention provides a shotcrete optimization method and system based on DEM-CFD coupling. The method uses a discrete element method (DEM) and computational fluid dynamics (CFD) coupling calculation method to simulate the wet shotcrete spraying process. By changing the microscopic parameters of the particle-particle and particle-plane contact models, the particle rebound rate in the calculation is minimized to characterize the minimum rebound rate of the concrete. At the same time, based on the relationship between the microscopic parameters and the characteristic parameters of the concrete material established in indoor experiments, the macroscopic characteristic parameters of the wet shotcrete with the minimum rebound rate corresponding to the microscopic parameters with the minimum rebound rate are determined, thereby deepening the research on the rebound mechanism of wet shotcrete and realizing the effective optimization of concrete materials in wet shotcrete technology.

Description

Technical Field

[0001] The present invention belongs to the technical field related to concrete, and in particular relates to a shotcrete optimization method and system based on DEM-CFD coupling. 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] With the development of society and economy, the number and scale of railway and highway tunnels in my country have increased year by year. Lining support plays a key role in the safe construction and operation of tunnels.

[0004] Under the current development background of mechanization in tunnel engineering construction in my country, shotcrete technology has been widely used in the process of tunnel lining support. It uses pumping pressure to transport the finished concrete to the nozzle and mix it with the accelerator to form raw materials. It uses high-pressure air to spray it onto the tunnel surrounding rock, making the surrounding rock body a new structure with high strength and good stability, thereby achieving the purpose of tunnel surrounding rock support.

[0005] The concrete shotcrete process has evolved from dry shotcrete to damp shotcrete and finally to wet shotcrete. Currently, wet shotcrete holds a dominant position in tunnel support. While wet shotcrete significantly reduces the inherent problems of high rebound rate, high dust production, and low construction quality associated with dry shotcrete operations, the high rebound rate of wet shotcrete still persists due to a lack of in-depth theoretical research, seriously impacting the speed and quality of tunnel construction. Statistics show that concrete rebound rates during tunnel construction are generally greater than 12%, reaching as high as 30% at the tunnel top, resulting in cost losses of approximately 1 million yuan per kilometer.

[0006] Most domestic and foreign scholars analyze and simulate the jet flow field of wet shotcrete through theoretical calculations, indoor experiments, field experiments, computational fluid or discrete element numerical simulation methods. Since macroscopic experimental methods are difficult to deeply explore its internal motion mechanism, a single numerical calculation method is difficult to accurately describe the motion state and laws of wet shotcrete particle flow, and it is impossible to use theory to guide the rebound of concrete in actual construction. Summary of the Invention

[0007] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a shotcrete optimization method and system based on DEM-CFD coupling, which adopts a discrete element method (DEM) and computational fluid dynamics (CFD) coupling calculation method to simulate the wet shotcrete spraying process. By changing the microscopic parameters of the particle-particle and particle-plane contact models, the particle rebound rate in the calculation is minimized to characterize the minimum rebound rate of the concrete. At the same time, based on the relationship between the microscopic parameters and the characteristic parameters of the concrete material established in indoor experiments, the macroscopic characteristic parameters of the wet shotcrete with the minimum rebound rate corresponding to the microscopic parameters when the particle rebound rate is minimized are determined, thereby deepening the research on the rebound mechanism of wet shotcrete and realizing effective optimization of concrete materials in wet shotcrete technology.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: a shotcrete optimization method based on DEM-CFD coupling, comprising:

[0009] Obtain the geometric parameters, motion parameters, and jet parameters of shotcrete, and establish a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters;

[0010] Obtain the characteristic parameters of the mixed concrete material and calibrate the microscopic parameters of the particle-particle and particle-plane contact models under the concrete particle calculation model based on the concrete characteristic parameters;

[0011] According to the jet parameters of shotcrete, a high-pressure air flow field calculation model is established based on CFD;

[0012] The concrete particle calculation model is coupled with the high-pressure air flow field calculation model for simulation, and the microscopic parameters of the concrete particle calculation model are changed to optimize the characteristic parameters of the concrete material.

[0013] A second aspect of the present invention provides a shotcrete optimization system based on DEM-CFD coupling, which is characterized by comprising:

[0014] Concrete particle calculation model construction module: obtains the geometric parameters, motion parameters, and jet parameters of shotcrete, and establishes a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters;

[0015] Concrete particle calibration module: obtains the characteristic parameters of the mixed concrete material and calibrates the microscopic parameters of the particle-particle and particle-plane contact models in the concrete particle calculation model based on the concrete characteristic parameters;

[0016] High-pressure air flow field calculation model construction module: establishes a high-pressure air flow field calculation model based on CFD according to the jet parameters of shotcrete;

[0017] Simulation and optimization module: The concrete particle calculation model is coupled with the high-pressure air flow field calculation model for simulation, the microscopic parameters of the concrete particle calculation model are changed, and the characteristic parameters of the concrete material are optimized.

[0018] A third aspect of the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above method.

[0019] A fourth aspect of the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions complete the steps of the above method when executed by the processor.

[0020] One or more of the above technical solutions have the following beneficial effects:

[0021] 1. The present invention aims at the shotcrete process and establishes a fluid-solid coupling calculation model of high-speed and high-pressure air and concrete particles, which provides a numerical simulation basis for the mechanism research of the shotcrete process.

[0022] 2. The present invention effectively simulates the motion and mechanical characteristics of concrete particles under the action of high-speed and high-pressure air during shotcrete operations, overcoming the problems of difficult operation and poor test accuracy of macro-test methods.

[0023] 3. The present invention simultaneously considers multiple sets of characteristic parameter combinations of concrete materials to achieve the optimal effect of reducing the rebound rate of concrete, overcoming the problems of difficulty in controlling variables in macro-testing methods and high cost of test materials.

[0024] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0026] Figure 1 This is the calculation process for simulating the concrete spraying process in the first embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a simulation of a single concrete particle and a high-pressure air system in Example 1 of the present invention;

[0028] Figure 3 Schematic diagram of the grid division of the fluid system in the first embodiment of the present invention;

[0029] Figure 4It is a schematic diagram of the calculation model in Example 1 of the present invention.

[0030] Description of the drawings: 1. High-pressure air system; 2. Concrete particles; 3. Nozzle; 4. Jet zone DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0033] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0034] Example 1

[0035] like Figures 1-4 As shown, this embodiment discloses a shotcrete optimization method based on DEM-CFD coupling, including:

[0036] Obtain the geometric parameters, motion parameters, and jet parameters of shotcrete, and establish a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters;

[0037] Obtain the characteristic parameters of the mixed concrete material and calibrate the microscopic parameters of the particle-particle and particle-plane contact models under the concrete particle calculation model based on the concrete characteristic parameters;

[0038] According to the jet parameters of shotcrete, a high-pressure air flow field calculation model is established based on CFD;

[0039] The concrete particle calculation model is coupled with the high-pressure air flow field calculation model for simulation, and the microscopic parameters of the concrete particle calculation model are changed to optimize the characteristic parameters of the concrete material.

[0040] In this embodiment, step 1: obtaining characteristic parameters of fresh concrete.

[0041] Conduct concrete tests on fresh concrete required on site or in the laboratory to obtain the corresponding concrete material characteristic parameters, including: density, particle size distribution, workability (fluidity, water retention, cohesion), strength, etc.

[0042] Step 2: Obtain the parameters of the particle-fluid system in shotcrete.

[0043] The concrete spraying process at the construction site or in the laboratory is filmed with high-speed camera equipment, and the high-speed camera video is analyzed to obtain parameters such as the geometric shape, initial velocity v0, and flight trajectory of the concrete particles during the spraying process. At the same time, the working air pressure P of the wet spraying machine, the spraying distance L from the nozzle to the working surface, and the jet area morphology are recorded.

[0044] Step 3: Establish the solid phase and fluid phase control equations.

[0045] Step 3-1: Establish the fluid phase (high-pressure air) control equation based on the CFD method, wherein the fluid phase control equation includes the continuity equation and the momentum equation. The continuity equation is expressed as:

[0046]

[0047] Among them, ρ f is the high-pressure air density, u is the average gas velocity, and ε is the gas void ratio.

[0048] The momentum equation is expressed as:

[0049]

[0050] Where p is the gas pressure, F f-p is the interaction force between air and particles, and τ is the gas stress tensor.

[0051] Step 3-2: Establish the solid phase (concrete particles) motion control equation based on the DEM method, where the solid phase control equation includes the translation equation and the rotation equation. The particle translation equation is expressed as:

[0052]

[0053] Among them, m i is the mass of particle i, v i is the horizontal velocity of particle i, f c,ij is the contact force between particles, f d,ij is the viscous resistance between particles.

[0054] The particle rotation equation is expressed as:

[0055]

[0056] Among them, ω i is the angular velocity of particle i, T ij is the tangential moment between particles, M ij is the rolling friction torque between particles.

[0057] It can be found that there is an interaction force between air and particles in the established solid phase and fluid phase control equations. Through this interaction force, the DEM and CFD methods can be coupled for calculation.

[0058] Step 4: High-precision fluid-structure interaction simulation of the concrete spraying process.

[0059] Step 4-1, generate a concrete particle calculation model.

[0060] Based on the concrete particle density ρ, gradation parameters, initial velocity v0, and other parameters obtained in Steps 1 and 2, the particle radius r, particle number N, and initial velocity v0 are determined. This is used to establish a particle calculation model for shotcrete within the DEM model. Actual shotcrete is simplified into individual particles. Each particle must first have a specified radius r. A finite number of particles, N, must also be generated. To simulate the concrete spraying process, the generated particles cannot remain stationary; an initial velocity v0 must be specified to set the concrete particles in motion.

[0061] According to the characteristic tests of concrete materials required on site or in the laboratory, the fluidity, water retention, cohesion, strength and other parameters of concrete are obtained, and the contact model of the concrete particle model and the microscopic parameters of the contact model such as friction coefficient μ, elastic modulus E, stiffness k, and bond strength σ are calibrated.

[0062] Calibration of the contact model of the concrete particle model: Numerical simulation of indoor concrete property tests is carried out through DEM. First, a suitable contact model of concrete particles is selected, and the mesoscopic parameters in the contact model are changed so that the fluidity, cohesion and other properties exhibited by the simulated concrete particles are consistent with the properties obtained from the actual indoor concrete property tests. At this time, the mesoscopic parameters in the contact model are the mesoscopic parameters of the calibrated concrete particle model.

[0063] Step 4-2: Generate a high-speed and high-pressure air flow field calculation model.

[0064] Based on the working wind pressure P, injection distance L, and jet area morphological parameters in step 2, select the flow field calculation model, divide the appropriate fluid calculation grid, determine the air flow field inlet wind pressure p0, and set the flow field outlet pressure to 100kPa (standard atmospheric pressure).

[0065] Step 4-3: Generate the working surface model.

[0066] Based on the jet flow field shape and size, the working surface model is established and the particle-plane contact microscopic parameters are calibrated, including the friction coefficient μ ball-facet , elastic modulus E ball-facet , stiffness k ball-facet , bonding strength σ ball-facetThe particle-particle contact microscopic parameters are calibrated, including friction coefficient μ, elastic modulus E, stiffness k, and bonding strength σ.

[0067] Calibration process of microscopic parameters of contact between particles and planes: numerical simulation of concrete particles hitting the wall and rebounding is carried out through DEM. First, a suitable contact model of concrete particles and the working surface is selected, and the microscopic parameters in the contact model are changed so that the particle model can produce bonding and rebound effects, the rebound rate is controlled within 20%, and the mass spatial distribution shape on the working surface is consistent with reality. At this time, the microscopic parameters in the contact model are the microscopic parameters for calibrating the concrete particles and the working surface.

[0068] Working surface model: A plane on which the sprayed concrete particles adhere or rebound after impact, used to simulate the wall of the tunnel.

[0069] Calibration of microscopic parameters of particle-particle contact: Numerical simulation of indoor concrete property tests is carried out through DEM. First, a suitable contact model of concrete particles is selected, and the microscopic parameters in the contact model are changed so that the fluidity, cohesion and other properties exhibited by the simulated concrete particles are consistent with the properties obtained from the actual indoor concrete property tests. At this time, the microscopic parameters in the contact model are the microscopic parameters of the calibrated concrete particle model.

[0070] Step 5: Reduce the scale of the particle-fluid system and shorten the calculation time.

[0071] The coarse-graining method is used to modify the shotcrete particle-fluid system established in step 4, further enlarging the model particle size, reducing the calculation model scale, shortening the calculation time, and ensuring that the calculation effect of the coarse-grained model is the same as that of the original model. The coarse-graining method is derived based on the energy conservation of the impulse theorem. The magnitude of the inter-particle force in the modified fluid-solid coupling model is:

[0072]

[0073] in, is the interaction force between air and particles after coarsening, is the interaction force between air and particles in the original system (coupling model before coarse-graining), and α is the size ratio of the coarse-grained particles to the original particles.

[0074] The magnitude of the force between particles and fluid:

[0075]

[0076] in, is the interaction force between particles after coarsening, is the interaction force between particles in the original system.

[0077] Step 6: Simulate the concrete spraying process.

[0078] Perform iterative simulation calculation on the concrete spraying process, and stop the calculation when the set number of iteration steps is reached, completing a concrete spraying simulation.

[0079] Specifically, DEM simulation calculations can provide information such as particle position, velocity, angular velocity, volume, etc. for CFD calculations, while CFD can provide information such as force and torque for discrete element calculations.

[0080] CFD first performs a flow field calculation for one time step, and DEM then starts the iterative calculation of the current time step. During this time step, DEM obtains CFD flow field information, including interphase forces such as drag force, and introduces the interphase forces into the particle motion calculation. After DEM completes one step of calculation, it passes the particle information and interphase forces back to the CFD module, and CFD performs the flow field calculation for the next time step.

[0081] Step 7: Obtain the microscopic parameters when the rebound rate is minimum.

[0082] Modify the inter-particle microscopic parameters, update the concrete particle model, repeat step 6 to simulate the concrete spraying process, and obtain the microscopic parameters of the concrete particle model with the minimum rebound rate.

[0083] Step 8: Optimize fresh concrete materials.

[0084] Based on the particle microscopic parameters with the minimum rebound rate obtained in step 7, the properties of fresh concrete are optimized through indoor experiments, such as changing the concrete mix ratio and admixtures, and spraying tests are carried out on the fresh concrete to obtain the corresponding minimum rebound rate. Based on this, an optimized design method for the spraying concrete process is realized.

[0085] Example 2

[0086] A shotcrete optimization system based on DEM-CFD coupling, comprising:

[0087] Concrete particle calculation model construction module: obtains the geometric parameters, motion parameters, and jet parameters of shotcrete, and establishes a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters;

[0088] Concrete particle calibration module: obtains the characteristic parameters of the mixed concrete material and calibrates the microscopic parameters of the particle-particle and particle-plane contact models in the concrete particle calculation model based on the concrete characteristic parameters;

[0089] High-pressure air flow field calculation model construction module: establishes a high-pressure air flow field calculation model based on CFD according to the jet parameters of shotcrete;

[0090] Simulation and optimization module: The concrete particle calculation model is coupled with the high-pressure air flow field calculation model for simulation, the microscopic parameters of the concrete particle calculation model are changed, and the characteristic parameters of the concrete material are optimized.

[0091] Example 3

[0092] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0093] Example 4

[0094] The purpose of this embodiment is to provide a computer-readable storage medium.

[0095] A computer readable storage medium having a computer program stored thereon. The program is executed by a processor.

[0096] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0097] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0098] 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 shotcrete optimization method based on DEM-CFD coupling, characterized in that: include: Obtain the geometric parameters, motion parameters, and jet parameters of shotcrete, and establish a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters; Obtain the characteristic parameters of the mixed concrete material and calibrate the microscopic parameters of the particle-particle and particle-plane contact models under the concrete particle calculation model based on the concrete characteristic parameters; According to the jet parameters of shotcrete, a high-pressure air flow field calculation model is established based on CFD; The concrete particle calculation model is coupled with the high-pressure air flow field calculation model to simulate and calculate, change the microscopic parameters of the concrete particle calculation model, and optimize the concrete material characteristic parameters; The concrete spraying process at the construction site or in the laboratory is filmed with high-speed camera equipment to obtain the geometric parameters, motion parameters of the sprayed concrete, and the jet parameters of the wet spraying machine. The geometric parameters include the size and shape of the sprayed concrete particles, the motion parameters include the flight trajectory and initial velocity of the sprayed concrete, and the jet parameters include the working air pressure of the wet spraying machine, the spraying distance from the nozzle to the working surface, and the shape of the jet area. Among them, the microscopic parameters of the particle-plane contact model include friction coefficient, elastic modulus, stiffness, and bonding strength; the microscopic parameters of the particle-particle contact include: friction coefficient, elastic modulus, stiffness, and bonding strength; the interaction force between air and particles in the coupling calculation model corrected by the coarse-graining method is the product of the interaction force between air and particles in the original coupling calculation model and the cube of the particle size ratio of the coarse-grained particles to the original particles; the interaction force between the coarse-grained particles in the corrected coupling calculation model is equal to the interaction force between the particles in the original coupling calculation model.

2. The optimization method for shotcrete based on DEM-CFD coupling according to claim 1, characterized in that: Conduct concrete tests on fresh concrete required on site or in the laboratory to obtain concrete material characteristic parameters, including density, particle size distribution, fluidity, water retention, cohesion, and strength.

3. The optimization method for shotcrete based on DEM-CFD coupling according to claim 1, characterized in that: The coupled calculation model is modified by a coarse-grained method based on the impulse theorem and energy conservation.

4. The optimization method for shotcrete based on DEM-CFD coupling according to claim 1, characterized in that: In the coupled calculation simulation of shotcrete, the microscopic parameters of the concrete particle calculation model corresponding to the minimum rebound rate are obtained, and the concrete material characteristic parameters are optimized through the microscopic parameters.

5. A shotcrete optimization system based on DEM-CFD coupling, characterized in that: include: Concrete particle calculation model construction module: obtains the geometric parameters, motion parameters, and jet parameters of shotcrete, and establishes a concrete particle calculation model based on the DEM according to the geometric parameters and motion parameters; Concrete particle calibration module: obtains the characteristic parameters of the mixed concrete material and calibrates the microscopic parameters of the particle-particle and particle-plane contact models in the concrete particle calculation model based on the concrete characteristic parameters; High-pressure air flow field calculation model construction module: establishes a high-pressure air flow field calculation model based on CFD according to the jet parameters of shotcrete; Simulation and optimization module: Couples the concrete particle calculation model with the high-pressure air flow field calculation model to perform calculation simulation, changes the microscopic parameters of the concrete particle calculation model, and optimizes the concrete material characteristic parameters; The concrete spraying process at the construction site or in the laboratory is filmed with high-speed camera equipment to obtain the geometric parameters, motion parameters of the sprayed concrete, and the jet parameters of the wet spraying machine. The geometric parameters include the size and shape of the sprayed concrete particles, the motion parameters include the flight trajectory and initial velocity of the sprayed concrete, and the jet parameters include the working air pressure of the wet spraying machine, the spraying distance from the nozzle to the working surface, and the shape of the jet area. Among them, the microscopic parameters of the particle-plane contact model include friction coefficient, elastic modulus, stiffness, and bonding strength; the microscopic parameters of the particle-particle contact include: friction coefficient, elastic modulus, stiffness, and bonding strength; the interaction force between air and particles in the coupling calculation model corrected by the coarse-graining method is the product of the interaction force between air and particles in the original coupling calculation model and the cube of the particle size ratio of the coarse-grained particles to the original particles; the interaction force between the coarse-grained particles in the corrected coupling calculation model is equal to the interaction force between the particles in the original coupling calculation model.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the shotcrete optimization method based on DEM-CFD coupling as claimed in any one of claims 1 to 4 are implemented.

7. A processing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the shotcrete optimization method based on DEM-CFD coupling according to any one of claims 1 to 4 are implemented.

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