Method and system for acquiring springback mechanism of sprayed concrete

Abstract

The invention relates to a shotcrete springback mechanism acquisition method and system, and the method comprises the following steps: determining concrete raw materials and a mix proportion of the raw materials, and carrying out a hydration heat test to obtain a test result; inputting a test result of the hydration heat test into the Krstulovic-Dabic hydration kinetic model to obtain a hydration reaction rate fitting curve of the concrete and corresponding hydration kinetic parameters; the springback process of the sprayed concrete is simulated through the numerical simulation model, and a springback rate numerical simulation result is obtained; and fusing the hydration reaction rate fitting curve, the hydration kinetic parameters and the springback rate numerical simulation result to obtain the springback mechanism of the sprayed concrete. Compared with the prior art, the method has the advantages that analysis is carried out from two aspects of a macroscopic phenomenon and a microcosmic mechanism, and the influence factors of the concrete rebound process are comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete construction technology, and in particular to a method and system for acquiring the rebound mechanism of shotcrete. Background Art

[0002] As a key material in modern support engineering, shotcrete has always been difficult to solve in construction due to its high rebound rate. Too high a rebound rate will not only lead to material waste, but more importantly, it will affect the initial stability of the support structure. With the expansion of project scale and the improvement of technical requirements, traditional shotcrete materials can no longer meet the needs of modern engineering. Therefore, studying the rebound performance of shotcrete has become an urgent need.

[0003] Since it is difficult to explore the internal motion mechanism by macroscopic experimental methods, a single numerical calculation method is not enough to accurately describe the motion state and law of wet shotcrete particle flow. Therefore, a CFD-DEM coupling simulation method is used to simulate the rebound process of shotcrete, in which CFD is used to simulate high-pressure air and DEM is used to simulate concrete particles. Former researchers have explored this aspect. For example, patent application CN116244782A discloses a shotcrete optimization method and system based on DEM-CFD coupling, which uses discrete element (DEM) and computational fluid dynamics (CFD) coupling calculation methods to simulate the spraying process of wet shotcrete. By changing the microscopic parameters of the particle-particle and particle-plane contact model, the particle rebound rate in the calculation is minimized to characterize the minimum rebound rate of concrete. At the same time, based on the relationship between the microscopic parameters and the concrete material characteristic parameters established by indoor tests, the macroscopic characteristic parameters of wet shotcrete with the minimum rebound rate corresponding to the microscopic parameters when the particle rebound rate is the smallest 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. However, the use of numerical simulation methods to predict the rebound amount of concrete in actual construction still has limitations. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method and system for obtaining the rebound mechanism of shotcrete.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for obtaining the rebound mechanism of shotcrete comprises the following steps:

[0007] Determine the concrete raw materials and the mix ratio of the raw materials, conduct a hydration heat test, and obtain the test results;

[0008] The test results of the hydration heat test are input into the Krstulovic-Dabic hydration kinetic model to obtain a fitting curve of the hydration reaction rate of the concrete and corresponding hydration kinetic parameters;

[0009] The rebound process of shotcrete is simulated by numerical simulation model, and the numerical simulation results of rebound rate are obtained.

[0010] The rebound mechanism of shotcrete is obtained by integrating the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results;

[0011] The steps of constructing the numerical simulation model include:

[0012] Establish gas phase control equation, particle phase control equation and two-phase coupling model;

[0013] The wet shotcrete particle flow jet flow field is established according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

[0014] Furthermore, the gas phase control equation adopts the Navier-Stokes equation in CFD, and the gas phase mass equation is:

[0015]

[0016] In the formula, ρ g is the gas density, t is the time, u g for speed;

[0017] The momentum conservation equation is:

[0018]

[0019] In the formula, g is the acceleration due to gravity, p is the pressure, and S is the momentum exchange between the gas and solid phases;

[0020] The calculation formula of the momentum exchange amount is:

[0021]

[0022] In the formula, ΔV is the unit volume, f drag，i For drag force.

[0023] Furthermore, DEM is used to solve the particle phase control equation, the Hertz Mindlin with bonding model is used to calculate the forces between the contacting particles, and the motion parameters of the particles are solved. The forces between the contacting particles include gravity, particle-particle contact force, particle-wall contact force and particle-gas interaction force. The particle phase control equation is:

[0024]

[0025]

[0026] In the formula, f contact，ij is the contact force, f damp，ij is the damping force, m i is the mass of particle i, v i is the speed, t is the time, g is the gravitational acceleration, I i is the moment of inertia, ω i is the angular velocity, T ij is the torque;

[0027] The contact force is:

[0028] f contact，ij =f n，ij +f t，ij

[0029] In the formula, f n，ij is the normal contact force, f t，ij is the tangential contact force;

[0030] The damping force is:

[0031]

[0032] In the formula, is the normal damping force, is the tangential damping force.

[0033] Furthermore, the steps of coupling calculation of the two-phase coupling model are as follows:

[0034] Use FLUENT to calculate the airflow field data in the calculation domain;

[0035] Utilizing EDEM to calculate particle forces based on the airflow field data and particle collision information, and to update particle position and motion information;

[0036] The airflow field data is recalculated according to the momentum exchange provided by EDEM to complete the coupling calculation.

[0037] Furthermore, the wet spraying concrete particle flow jet flow field includes a wet spraying machine nozzle, a material external jet flow field and a sprayed wall surface.

[0038] Furthermore, the numerical simulation results are analyzed from the aspect of the particle flow velocity field of the particle flow jet flow field to determine the effective injection range.

[0039] Furthermore, the numerical simulation calculation result of the rebound rate is obtained by dividing the mass of particles on the sprayed wall surface outside the effective spraying range during the spraying stabilization time by the mass of all particles passing through the wet spraying machine nozzle.

[0040] Furthermore, a rebound rate test is performed on the concrete raw material, and the test result of the rebound rate test is used to obtain the rebound rate of the concrete through a rebound rate calculation formula, which is compared with the rebound rate numerical simulation result to verify the accuracy of the numerical simulation.

[0041] Furthermore, the rebound rate calculation formula is as follows:

[0042] n=W 1 / (W 1 +W 2 )×100%

[0043] Where n is the rebound rate, W 1 is the mass of concrete collected below the sprayed wall, W 1 +W 2 is the total mass of shotcrete.

[0044] According to another aspect of the present invention, a system for acquiring the rebound mechanism of shotcrete based on CFD-DEM is provided, comprising:

[0045] A test module, used to determine the concrete raw materials and the mix ratio of the raw materials, conduct a hydration heat test, and obtain test results;

[0046] A hydration kinetics module is used to input the test results of the hydration heat test into the Krstulovic-Dabic hydration kinetics model to obtain a fitting curve of the hydration reaction rate of the concrete and corresponding hydration kinetics parameters;

[0047] Numerical simulation module, used to simulate the rebound process of shotcrete through a numerical simulation model to obtain the numerical simulation result of the rebound rate;

[0048] A fusion acquisition module is used to fuse the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results to obtain the rebound mechanism of shotcrete;

[0049] The steps of constructing the numerical simulation model are as follows:

[0050] Establish gas phase control equation, particle phase control equation and two-phase coupling model;

[0051] The wet shotcrete particle flow jet flow field is established according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention simulates the rebound process of shotcrete by using a CFD-DEM numerical simulation model, and obtains the fitting curve of the hydration reaction rate of concrete and the corresponding hydration kinetic parameters by using the Krstulovic-Dabic hydration kinetic model. The influencing factors of the concrete rebound process are comprehensively evaluated from both macroscopic phenomena and microscopic mechanisms, and a more accurate rebound mechanism of shotcrete is obtained.

[0054] 2. The present invention constructs a numerical simulation model through the gas phase control equation, the particle phase control equation and the two-phase coupling model, and analyzes the numerical simulation results from the particle flow velocity field of the particle flow jet flow field through the wet shotcrete particle flow jet flow field, which provides a reference for the study of the CFD-DEM numerical simulation method in the rebound process and mechanism of shotcrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic flow chart of a method for obtaining the rebound mechanism of shotcrete proposed by the present invention;

[0056] Figure 2 The hydration reaction rate fitting curves of different groups of concrete, among which (2a) is the hydration reaction rate fitting curve of CC group, (2b) is the hydration reaction rate fitting curve of SF-5 group, and (2c) is the hydration reaction rate fitting curve of ES5 group;

[0057] Figure 3 Schematic diagram of CFD numerical model, where (3a) is a schematic diagram of the wet spraying machine nozzle, air inlet and jet flow field outlet, and (3b) is a schematic diagram of the sprayed wall surface;

[0058] Figure 4 This is a schematic diagram of the DEM numerical model;

[0059] Figure 5 It is a schematic diagram of the effective injection range;

[0060] Figure 6 Cross-sectional diagram of particles at different distances from the injection nozzle. DETAILED DESCRIPTION

[0061] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0062] Abbreviations involved:

[0063] Computational Fluid Dynamics, CFD

[0064] Digital Elevation Model: Digital Elevation Model, DEM

[0065] Example 1

[0066] This embodiment provides a method for obtaining the rebound mechanism of shotcrete based on CFD-DEM. Figure 1 As shown, the following steps are included:

[0067] S1. Determine the mix ratio of concrete raw materials and raw materials, conduct hydration heat test, and obtain test results.

[0068] Determine the raw materials of concrete, design the mix ratio, and determine the test plan; the specific mix ratio is shown in the following table:

[0069] Table 1 Cementitious material mix design

[0070]

[0071] The test schemes in this embodiment include a rebound rate test scheme and a hydration heat test scheme; for the rebound rate test scheme, a field test method is selected, a concrete spraying machine is selected, and the same sprayer is used on the same working surface to perform the spraying operation with the same water, cement, sand and gravel, and the same mix ratio; for the hydration heat test scheme, a C80 microcalorimeter is used to measure the hydration heat of different groups, the water-binder ratio of the test is 0.44, and the test temperature is 25°C;

[0072] S2. Input the test results of the hydration heat test into Krstulovi c -Dabi c The hydration kinetics model is used to obtain the fitting curve of the concrete hydration reaction rate and the corresponding hydration kinetics parameters.

[0073] Substitute the rebound test results into the rebound calculation formula to obtain the rebound rate of different groups; the rebound rate calculation formula is as follows:

[0074] n=W 1 / (W 1 +W 2 )×100%

[0075] Where n is the rebound rate, W 1 is the mass of concrete collected below the sprayed wall, W 1 +W 2 is the total mass of shotcrete.

[0076] For the hydration heat test, the data of hydration heat release in different time periods are obtained. The test results of the hydration heat test are input into the Krstulovic-Dabic hydration kinetic model to obtain the hydration reaction rate fitting curves of different groups of concrete, such as Figure 2 The corresponding hydration kinetic parameters are shown in the following table.

[0077] Table 2 Hydration kinetic parameters under various working conditions

[0078]

[0079] Figure 2 (2a) is the fitting curve of the hydration reaction rate of the CC group. Figure 2 (2b) is the fitting curve of the hydration reaction rate of the SF-5 group. Figure 2 (2c) is the fitting curve of the hydration reaction rate of the ES5 group.

[0080] S3. The rebound process of shotcrete is simulated by a numerical simulation model to obtain the numerical simulation results of the rebound rate.

[0081] During the numerical simulation, CFD reflects the high-pressure airflow and is solved by the software FLUENT, and DEM reflects the concrete particles and is solved by the software EDEM.

[0082] Establish gas phase control equation, particle phase control equation and two-phase coupling model;

[0083] The gas phase control equation adopts the Navier-Stokes equation in CFD, and the gas phase mass equation is:

[0084]

[0085] In the formula, ρ g is the gas density, t is the time, u g for speed;

[0086] The momentum conservation equation is:

[0087]

[0088] Where g is the acceleration due to gravity, p is the pressure, and S is the momentum exchange between the gas and solid phases.

[0089] Although the force exerted by the gas on the particles is very complex, under the working conditions simulated in this embodiment, only the momentum exchange caused by the drag force is considered. Therefore, the calculation formula for the momentum exchange amount is:

[0090]

[0091] In the formula, ΔV is the unit volume, f drag，i For drag force.

[0092] DEM is used to solve the particle phase control equation, and the particle contact model is used to calculate the forces between particles in contact with each other, and the motion parameters of the particles are solved. The forces between particles in contact with each other include gravity, particle-particle contact force, particle-wall contact force and particle-gas interaction force. The control equation of particle i at any time t is:

[0093]

[0094]

[0095] In the formula, f contact，ij is the contact force, f damp，ij is the damping force, m i is the mass of particle i, v i is the speed, t is the time, g is the gravitational acceleration, I i is the moment of inertia, ω i is the angular velocity, T ij For torque.

[0096] The contact force is:

[0097] f contact，ij =f n，ij +f t，ij

[0098] In the formula, f n，ij is the normal contact force, f t，ij is the tangential contact force;

[0099] The damping force is:

[0100]

[0101] In the formula, is the normal damping force, is the tangential damping force.

[0102] For the two-phase coupling model, FLUENT first calculates the airflow field data in the calculation domain, and then hands over the simulation control to EDEM; EDEM calculates the particle force based on the flow field data and particle collision information, and updates the particle position and motion information; after the particle information is updated, the simulation control is returned to FLUENT, which recalculates the airflow field data based on the momentum exchange provided by EDEM, thus completing a coupling calculation.

[0103] Establish the wet shotcrete particle flow jet flow field, CFD numerical model as follows Figure 3 As shown, it includes the wet spraying machine nozzle, the external jet flow field of the material and the sprayed wall surface. Figure 3 (3a) is a schematic diagram of the wet spraying machine nozzle, air inlet and jet flow field outlet. Figure 3 (3b) is a schematic diagram of the sprayed wall. Figure 4 shown.

[0104] The air inlet boundary condition is selected as the pressure inlet, and the air flow direction is perpendicular to the nozzle inlet section. The inlet wind pressure is set at the inlet and the on-site working wind pressure is set at the outlet. The jet flow field outlet boundary condition selects the pressure outlet. The nozzle internal wall and the sprayed wall conditions are both selected as no-slip conditions and stationary walls.

[0105] The Hertz Mindlin with bonding model is used to simulate the process of particles adhering to adjacent units under the influence of interaction forces. The initial velocity of aggregate particles at the inlet of the wet spraying machine nozzle is set.

[0106] The numerical simulation results are analyzed from the perspective of the particle flow velocity field of the particle flow jet flow field to determine the effective injection range.

[0107] Regarding the particle flow velocity field, it was stipulated in the research process that the radius is 9 times the nozzle outlet radius, and the circular area is the effective injection range. Figure 5 As shown; for the cross-sectional view of the particle velocity in the particle diameter jet region, two circles are defined with the intersection of the jet axis and the cross-sectional view as the origin. The inner circle represents the nozzle outlet area, and the outer circle represents twice the nozzle outlet area, as shown in Figure 6 shown.

[0108] The numerical simulation calculation result of the rebound rate is obtained by dividing the mass of particles on the sprayed wall outside the effective spraying range during the spraying stabilization time by the mass of all particles passing through the wet spraying machine nozzle.

[0109] S4. The rebound mechanism of shotcrete is obtained by integrating the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results.

[0110] The concrete rebound rate obtained by the rebound rate calculation formula from the test results of the rebound rate test is compared with the rebound rate numerical simulation results to verify the accuracy of the numerical simulation. As shown in the following table, the error between the numerical simulation and the test data is within the range of 5-10%, which can better reflect the accuracy of the numerical simulation.

[0111] Table 3 Comparison results of test and numerical simulation rebound rate

[0112]

[0113] According to the hydration reaction rate fitting curves of different groups and the corresponding hydration kinetic parameters, the relationship between hydration heat and bonding performance is obtained, and then the rebound mechanism is obtained. Figure 2It can be seen that the Krstulovic-Dabic hydration kinetic model can better reflect the hydration reaction process of the six groups of composite gel systems, especially the NG→I process. It also reflects that the hydration of the three groups of composite gel systems is not a single reaction, but a complex continuous reaction. The fitting results are shown in Table 2. The n value of SF-5 is greater than that of CC. The increase in the amount of silica fume reduces the effective gelling component of the gelling system and increases the hydration resistance. K' of SF-5 x Both are greater than K' of other groups x The hydration rate of the three stages is the highest. This is because the addition of silica fume accelerates the CH crystallization nucleation process, and the number of seed crystals increases accordingly. The two together lead to an increase in CH hydration products, further promoting the hydration of tricalcium silicate and accelerating the hydration process. 2 -α 1 , ES5 is significantly smaller than SF-5, indicating that the early strength shrinkage reducer can significantly weaken the I stage effect compared with silica fume, which reduces the transition time and increases the hydration rate. Compared with CC, ES5 has reduced hydration resistance and increased hydration rates in the three stages, resulting in an increase in its hydration reaction rate. The faster the early hydration reaction rate of concrete, the earlier the cementitious material can wrap the particles, the greater the overall cohesion, and the smaller the rebound. As can be seen from Table 3, the rebound rates of SF-5 and ES5 groups are lower than those of CC group, because K' of SF-5 group and ES5 group x The values ​​are all greater than those of the CC group, corresponding to the hydration rates in the three stages being greater than those of the CC group. The increased hydration reaction rate results in a smaller rebound rate. The rebound rate of the ES5 group is lower than that of the SF-5 group, because the hydration resistance of the SF-5 group is smaller than that of the ES5 group, and the duration of stage I is short, so the hydration rate increases.

[0114] Example 2

[0115] This embodiment provides a system for acquiring the rebound mechanism of shotcrete based on CFD-DEM, including:

[0116] A test module, used to determine the concrete raw materials and the mix ratio of the raw materials, conduct a hydration heat test, and obtain test results;

[0117] A hydration kinetics module is used to input the test results of the hydration heat test into the Krstulovic-Dabic hydration kinetics model to obtain a fitting curve of the hydration reaction rate of the concrete and corresponding hydration kinetics parameters;

[0118] Numerical simulation module, used to simulate the rebound process of shotcrete through a numerical simulation model to obtain the numerical simulation result of the rebound rate;

[0119] A fusion acquisition module is used to fuse the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results to obtain the rebound mechanism of shotcrete;

[0120] The steps of constructing the numerical simulation model are as follows:

[0121] Establish gas phase control equation, particle phase control equation and two-phase coupling model;

[0122] The wet shotcrete particle flow jet flow field is established according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

[0123] The rest is the same as in Example 1.

[0124] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for obtaining the rebound mechanism of shotcrete, characterized in that: The following steps are involved: Determine the concrete raw materials and the mix ratio of the raw materials, conduct a hydration heat test, and obtain the test results; The test results of the hydration heat test are input into the Krstulovic-Dabic hydration kinetic model to obtain a fitting curve of the hydration reaction rate of the concrete and corresponding hydration kinetic parameters; The rebound process of shotcrete is simulated by numerical simulation model, and the numerical simulation results of rebound rate are obtained. The rebound mechanism of shotcrete is obtained by integrating the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results; The steps of constructing the numerical simulation model include: Establish gas phase control equation, particle phase control equation and two-phase coupling model; The wet shotcrete particle flow jet flow field is established according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

2. The method for obtaining the rebound mechanism of shotcrete according to claim 1, characterized in that: The gas phase control equation adopts the Navier-Stokes equation in CFD, and the gas phase mass equation is: In the formula, ρ g is the gas density, t is the time, u g for speed; The momentum conservation equation is: In the formula, g is the acceleration due to gravity, p is the pressure, and S is the momentum exchange between the gas and solid phases; The calculation formula of the momentum exchange amount is: In the formula, ΔV is the unit volume, f drag,i For drag force.

3. The method for obtaining the rebound mechanism of shotcrete according to claim 1, characterized in that: DEM is used to solve the particle phase control equation, the Hertz Mindlin with bonding model is used to calculate the forces between the contacting particles, and the motion parameters of the particles are solved. The forces between the contacting particles include gravity, particle-particle contact force, particle-wall contact force and particle-gas interaction force. The particle phase control equation is: In the formula, f contact,ij is the contact force, f damp,ij is the damping force, m i is the mass of particle i, v i is the speed, t is the time, g is the gravitational acceleration, I i is the moment of inertia, ω i is the angular velocity, T ij is the torque; The contact force is: f contact,ij =f n,ij +f t,ij In the formula, f n,ij is the normal contact force, f t,ij is the tangential contact force; The damping force is: In the formula, is the normal damping force, is the tangential damping force.

4. The method for obtaining the rebound mechanism of shotcrete according to claim 1, characterized in that: The steps of coupling calculation of the two-phase coupling model are as follows: Use FLUENT to calculate the airflow field data in the calculation domain; Utilizing EDEM to calculate particle forces based on the airflow field data and particle collision information, and to update particle position and motion information; The airflow field data is recalculated according to the momentum exchange provided by EDEM to complete the coupling calculation.

5. The method for obtaining the rebound mechanism of shotcrete according to claim 1, characterized in that: The wet spraying concrete particle flow jet flow field comprises a wet spraying machine nozzle, a material external jet flow field and a sprayed wall surface.

6. The method for obtaining the rebound mechanism of shotcrete according to claim 5, characterized in that: The numerical simulation results are analyzed from the aspect of the particle flow velocity field of the particle flow jet flow field to determine the effective injection range.

7. The method for obtaining the rebound mechanism of shotcrete according to claim 6, characterized in that: The numerical simulation calculation result of the rebound rate is obtained by dividing the mass of particles on the sprayed wall outside the effective spraying range during the spraying stabilization time by the mass of all particles passing through the wet spraying machine nozzle.

8. The method for obtaining the rebound mechanism of shotcrete according to claim 1, characterized in that: A rebound rate test is performed on the concrete raw material, and the rebound rate of the concrete is obtained by using the test result of the rebound rate test through a rebound rate calculation formula, which is compared with the rebound rate numerical simulation result to verify the accuracy of the numerical simulation.

9. The method for obtaining the rebound mechanism of shotcrete according to claim 8, characterized in that: The springback rate calculation formula is as follows: n=W1 / (W1+W2)×100% Where n is the rebound rate, W1 is the mass of concrete collected below the sprayed wall, and W1+W2 is the total mass of the sprayed concrete.

10. A system for acquiring the rebound mechanism of shotcrete, characterized in that: include: A test module, used to determine the concrete raw materials and the mix ratio of the raw materials, conduct a hydration heat test, and obtain test results; A hydration kinetics module is used to input the test results of the hydration heat test into the Krstulovic-Dabic hydration kinetics model to obtain a fitting curve of the hydration reaction rate of the concrete and corresponding hydration kinetics parameters; Numerical simulation module, used to simulate the rebound process of shotcrete through a numerical simulation model to obtain the numerical simulation result of the rebound rate; A fusion acquisition module is used to fuse the hydration reaction rate fitting curve, hydration kinetic parameters and rebound rate numerical simulation results to obtain the rebound mechanism of shotcrete; The steps of constructing the numerical simulation model are as follows: Establish gas phase control equation, particle phase control equation and two-phase coupling model; The wet shotcrete particle flow jet flow field is established according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

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