Method and system for obtaining a rebound mechanism of a shotcrete

Abstract

The application relates to a method and system for obtaining a rebound mechanism of sprayed concrete, and the method comprises the following steps: determining concrete raw materials and a mixing proportion of the raw materials, performing a hydration heat test to obtain a test result; inputting the test result of the hydration heat test into a Krstulovic-Dabic hydration kinetics model to obtain a hydration reaction rate fitting curve of the concrete and corresponding hydration kinetics parameters; simulating a rebound process of the sprayed concrete through a numerical simulation model to obtain a rebound rate numerical simulation result; and fusing the hydration reaction rate fitting curve, the hydration kinetics parameters and the rebound rate numerical simulation result to obtain a rebound mechanism of the sprayed concrete. Compared with the prior art, the application analyzes from macroscopic phenomena and microscopic mechanisms, and comprehensively evaluates influence factors of a concrete rebound process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete construction technology, and in particular to a method and system for obtaining the rebound mechanism of sprayed concrete. BACKGROUND

[0002] As a key material in modern support engineering, the high rebound rate of sprayed concrete has been a difficult problem to solve in construction. A high rebound rate not only leads to material waste, but more importantly, it affects the initial stability of the support structure. With the expansion of engineering scale and the improvement of technical requirements, traditional sprayed concrete materials have been unable to meet the needs of modern engineering. Therefore, it is an urgent need to study the rebound performance of sprayed concrete.

[0003] Due to the difficulty of exploring the internal motion mechanism by macroscopic experimental methods, a single numerical calculation method is insufficient to accurately describe the motion state and law of wet sprayed concrete particle flow. Therefore, a CFD-DEM coupling simulation method is used to simulate the rebound process of sprayed concrete, in which CFD is used to simulate high-pressure air and DEM is used to simulate concrete particles. Previous studies have explored this aspect, such as patent application CN116244782A, which discloses an optimization method and system for sprayed concrete based on DEM-CFD coupling. The discrete element (DEM) and computational fluid dynamics (CFD) coupling calculation method is used to simulate the wet sprayed concrete spraying process. By changing the mesoscopic parameters of the particle-particle and particle-plane contact model, the particle rebound rate in the calculation is minimized to represent the minimum rebound rate of the concrete. At the same time, based on the relationship between the mesoscopic parameters and the material characteristics parameters established by the indoor test, the mesoscopic parameters corresponding to the minimum rebound rate of the wet sprayed concrete at the minimum rebound rate of the particles are determined, thereby deeply studying the rebound mechanism of the wet sprayed concrete and effectively optimizing the concrete material in the wet spraying technology. However, there are still limitations in using numerical simulation methods to predict the rebound amount of concrete in actual construction. SUMMARY

[0004] The purpose of the present application is to overcome the defects of the prior art and provide a method and system for obtaining the rebound mechanism of sprayed concrete.

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

[0006] A method for obtaining the rebound mechanism of sprayed concrete, comprising the following steps:

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

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

[0009] The rebound process of the shotcrete is simulated by a numerical simulation model to obtain a numerical simulation result of a rebound rate;

[0010] The rebound mechanism of the shotcrete is obtained by fusing the fitting curve of the hydration reaction rate, the hydration kinetics parameters and the numerical simulation result of the rebound rate;

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

[0012] The gas phase control equation, the particle phase control equation and the two-phase coupling model are established;

[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] Further, 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, and u g is the velocity;

[0017] The momentum conservation equation is:

[0018]

[0019] In the formula, g is the gravitational acceleration, p is the pressure, and S is the momentum exchange amount between the gas and solid two phases;

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

[0021]

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

[0023] Further, the particle phase control equation is solved by DEM, the force between the mutually contacting particles is calculated by using the Hertz Mindlin with bonding model, and the motion parameters of the particles are solved, the force between the mutually contacting particles includes the gravity, the particle-particle contact force, the particle-wall contact force and the particle-gas interaction force, and the particle phase control equation is:

[0024]

[0025]

[0026] where 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 velocity, 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] where f n，ij is the normal contact force, f t，ij is the tangential contact force;

[0030] The damping force is:

[0031]

[0032] where, is the normal damping force, is the tangential damping force.

[0033] Further, the coupling calculation of the two-phase coupling model is as follows:

[0034] The gas flow field data in the calculation domain is calculated by using FLUENT;

[0035] The particle force is calculated by using EDEM according to the gas flow field data and particle collision information, and the particle position and motion information is updated;

[0036] The gas flow field data is recalculated according to the momentum exchange amount provided by EDEM, and the coupling calculation is completed.

[0037] Further, the wet shotcrete particle flow jet flow field includes a wet shotcrete nozzle, an external material jet flow field, and a sprayed wall surface.

[0038] Further, the numerical simulation result is analyzed from the particle flow velocity field of the particle flow jet flow field to determine the effective spraying range.

[0039] Further, the rebound ratio numerical simulation result is obtained by dividing the mass of the particles outside the effective spraying range on the sprayed wall within the spraying stabilization time by the total mass of the particles passing through the wet spraying machine nozzle.

[0040] Further, rebound ratio tests are performed on the concrete raw materials, and the test results of the rebound ratio tests are used to obtain the rebound ratio of the concrete through a rebound ratio calculation formula, so as to verify the accuracy of the numerical simulation by comparing the rebound ratio numerical simulation result.

[0041] Further, the rebound ratio calculation formula is as follows:

[0042] n=W1 / (W1+W2)×100%

[0043] In the formula, n is the rebound ratio, W1 is the mass of the collected concrete under the sprayed wall, and W1+W2 is the total mass of the sprayed concrete.

[0044] According to another aspect of the present application, a CFD-DEM-based rebound mechanism acquisition system for sprayed concrete is provided, comprising:

[0045] A test module is configured to determine the concrete raw materials and the mix proportion of the raw materials, perform a hydration heat test, and obtain test results.

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

[0047] A numerical simulation module is configured to simulate the rebound process of the sprayed concrete through a numerical simulation model to obtain a rebound ratio numerical simulation result.

[0048] A fusion acquisition module is configured to fuse the hydration reaction rate fitting curve, the hydration kinetics parameters, and the rebound ratio numerical simulation result to obtain a rebound mechanism of the sprayed concrete.

[0049] The numerical simulation model is constructed according to the following steps:

[0050] A gas phase control equation, a particle phase control equation, and a two-phase coupling model are established.

[0051] A wet sprayed concrete 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 application has the following beneficial effects:

[0053] 1.The present application simulates the rebound process of sprayed concrete by a CFD-DEM numerical simulation model, and obtains the hydration reaction rate fitting curve and the corresponding hydration kinetics parameters of the concrete by a Krstulovic-Dabic hydration kinetics model, comprehensively evaluates the influencing factors of the rebound process of the concrete from the macroscopic phenomena and the microscopic mechanism, and obtains a more accurate rebound mechanism of the sprayed concrete.

[0054] 2.The present application constructs a numerical simulation model by a gas phase control equation, a particle phase control equation and a two-phase coupling model, analyzes the numerical simulation results from the particle flow velocity field of the particle flow jet flow field of the wet sprayed concrete, and provides a reference for the CFD-DEM numerical simulation method in the research on the rebound process and mechanism of the sprayed concrete. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A flowchart of a method for obtaining a rebound mechanism of sprayed concrete is provided in the present application.

[0056] Figure 2 Figures of the hydration reaction rate fitting curves of different groups of concrete are provided, wherein (2a) is a figure of the hydration reaction rate fitting curve of the CC group, (2b) is a figure of the hydration reaction rate fitting curve of the SF-5 group, and (2c) is a figure of the hydration reaction rate fitting curve of the ES5 group.

[0057] Figure 3 A CFD numerical model is provided, wherein (3a) is a schematic diagram of a wet spraying machine nozzle, an air inlet and a jet flow field outlet, and (3b) is a schematic diagram of a sprayed wall surface.

[0058] Figure 4 A DEM numerical model is provided.

[0059] Figure 5 A schematic diagram of an effective spraying range is provided.

[0060] Figure 6 A particle cross-sectional view at different distances from a spraying port is provided. DETAILED DESCRIPTION

[0061] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and detailed implementation modes and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0062] English abbreviations involved:

[0063] Computational Fluid Dynamics: CFD

[0064] Digital Elevation Model, DEM

[0065] Embodiment 1

[0066] The embodiment provides an acquisition method of a rebound mechanism of sprayed concrete based on CFD-DEM, as shown in the figure, comprising the following steps: Figure 1

[0067] S1, determine the concrete raw materials and the mix proportion of the raw materials, perform a hydration heat test, and obtain test results.

[0068] Determine the concrete raw materials, design the mix proportion, and determine a test scheme; the specific mix proportion is shown in the following table:

[0069] Table 1: Mix proportion design of cementitious materials

[0070]

[0071] The test scheme in the embodiment includes a rebound rate test scheme and a hydration heat test scheme; for the rebound rate test scheme, a field test mode is selected, a concrete spraying machine is selected, the same spraying hand sprays on the same working surface, and the same water, cement sandstone and mix proportion are used; for the hydration heat test scheme, a C80 micro calorimeter 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℃.

[0072] S2, input the test results of the hydration heat test into the Krstulovic-Dabic hydration kinetics model to obtain a hydration reaction rate fitting curve of the concrete and corresponding hydration kinetics parameters. c c

[0073] The rebound test results are brought into a rebound amount calculation formula to obtain the rebound rates of different groups; the rebound rate calculation formula is as shown below:

[0074] n = W1 / (W1+W2) x 100%

[0075] In the formula, n is the rebound rate, W1 is the mass of the collected concrete below the sprayed wall surface, and W1+W2 is the total mass of the sprayed concrete.

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

[0077] Table 2: Hydration kinetics parameters of each working condition​​​

[0078]

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

[0080] S3, the rebound process of the sprayed concrete is simulated by a numerical simulation model, and a rebound rate numerical simulation result is obtained.

[0081] In the numerical simulation process, the CFD reflects the high-pressure gas flow, which is solved by the software FLUENT, and the DEM reflects the concrete particles, which is solved by the software EDEM.

[0082] The gas phase control equation, the particle phase control equation and the two-phase coupling model are established;

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

[0084]

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

[0086] The momentum conservation equation is:

[0087]

[0088] In the formula, g is the acceleration of gravity, p is the pressure, and S is the momentum exchange between the gas and solid phases.

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

[0090]

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

[0092] The DEM is used to solve the particle phase control equation, the force between the mutually contacting particles is calculated by using the particle contact model, and the motion parameters of the particles are solved, the force between the mutually contacting particles includes the gravity, the particle-particle contact force, the particle-wall contact force and the particle-gas interaction force, and the control equation of the particle i at any time t is:

[0093]

[0094]

[0095] where 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 velocity, t is time, g is the gravitational acceleration, I i is the moment of inertia, ω i is the angular velocity, T ij is the torque.

[0096] The contact force is:

[0097] f contact，ij = f n，ij + f t，ij

[0098] where f n，ij is the normal contact force, f t，ij is the tangential contact force.

[0099] The damping force is:

[0100]

[0101] where f is the normal damping force, f is the tangential damping force.

[0102] For the two-phase coupling model, FLUENT first calculates the gas flow field data in the calculation domain, and then hands over the simulation control to EDEM; EDEM calculates the force on the particles according to 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 handed back to FLUENT, which recalculates the gas flow field data according to the momentum exchange provided by EDEM, thereby completing one coupling calculation.

[0103] The wet shotcrete particle flow jet flow field is established, and the CFD numerical model is as shown in Figure 3 , including a wet shotcrete machine nozzle, a material external jet flow field, and a sprayed wall surface, Figure 3 , (3a) is a schematic view of a wet shotcrete machine nozzle, an air inlet, and a jet flow field outlet, Figure 3 , (3b) is a schematic view of a sprayed wall surface. The DEM numerical model is as shown in Figure 4 .

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

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

[0106] The numerical simulation results are analyzed in terms of the particle flow velocity field of the particle flow jet flow field to determine the effective spraying range.

[0107] Regarding the particle flow velocity field, it is specified during the research that the area of a circle with a radius of 9 times the nozzle outlet radius is the effective spraying range as shown in Figure 5 For the cross-sectional view of the particle velocity in the particle size jet area, two circles are defined with the intersection of the jet axis and the cross-section as the origin. The inner circle represents the outlet area of the nozzle, and the outer circle represents twice the outlet area of the nozzle as shown in Figure 6 .

[0108] The numerical simulation results of the rebound rate are obtained by dividing the mass of the particles on the sprayed wall outside the effective spraying range within the spraying stabilization time by the mass of all particles passing through the wet sprayer nozzle.

[0109] S4, the rebound mechanism of the sprayed concrete is obtained by combining the hydration reaction rate fitting curve, the hydration kinetics parameters, and the numerical simulation results of the rebound rate.

[0110] The rebound rate of the concrete obtained from the test results of the rebound rate test through the rebound rate calculation formula is compared with the numerical simulation results of the rebound rate 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 5-10%, which can better reflect the accuracy of the numerical simulation.

[0111] Table 3 Comparison of rebound rates from tests and numerical simulations

[0112]

[0113] According to the hydration reaction rate fitting curve of different groups and the corresponding hydration kinetics parameters, the relationship between the hydration heat and the bonding performance is obtained, and the rebound mechanism is obtained. From Figure 2It can be seen that the Krstulovic-Dabic hydration kinetics model can better reflect the hydration reaction process of the six groups of composite cementitious gel systems, especially the NG→I process. At the same time, it also reflects that the hydration of the three groups of composite cementitious 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 the n value of CC. The increase of silica ash content reduces the effective cementitious components of the gel system and increases the hydration resistance. The K' x value of SF-5 is greater than the K' x value of other groups. The hydration rate of the three stages is the highest. This is because the addition of silica ash increases the CH crystallization nucleation process and the number of crystal seeds, which together increase the CH hydration product, further promote the hydration of tricalcium silicate, and accelerate the hydration process. Comparing α2-α1, ES5 is significantly smaller than SF-5, indicating that the early strength reducing shrinkage agent can significantly weaken the I stage effect, reduce the transition time, and increase the hydration rate. Compared with CC, the hydration resistance of ES5 is reduced and the hydration rate of the three stages is increased, resulting in an increase in the 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 rate of SF-5 and ES5 groups is lower than that of CC group. Because the K' x value of SF-5 and ES5 groups is greater than that of CC group, the hydration rate of the three stages is greater than that of CC group, the hydration reaction rate is increased, and the rebound rate is smaller. The rebound rate of ES5 group is lower than that of SF-5 group, because the hydration resistance of SF-5 group is smaller than that of ES5 group, and the I stage time is short, so the hydration rate is increased.

[0114] Example 2

[0115] The embodiment provides a system for obtaining the rebound mechanism of sprayed concrete based on CFD-DEM, comprising:

[0116] A test module is configured to determine concrete raw materials and a mix proportion of the raw materials, perform a hydration heat test, and obtain a test result.

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

[0118] A numerical simulation module is configured to simulate a rebound process of the sprayed concrete by using a numerical simulation model, and obtain a numerical simulation result of the rebound rate.

[0119] A fusion obtaining module is configured to fuse the hydration reaction rate fitting curve, the hydration kinetics parameters, and the numerical simulation result of the rebound rate, and obtain a rebound mechanism of the sprayed concrete.

[0120] The construction of the numerical simulation model is as follows:

[0121] The gas phase control equation, the particle phase control equation and the two-phase coupling model are established.

[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 above detailed description of the preferred embodiments of the present application. It should be understood that those skilled in the art without the need for creative labor can make many modifications and changes according to the concept of the present application. Therefore, any technical solution that can be obtained by logical analysis, reasoning or limited experiment on the basis of the prior art by those skilled in the art according to the concept of the present application shall be within the scope of protection determined by the claims.

Claims

1. A method for obtaining a rebound mechanism of a shotcrete, characterized by, The method comprises the following steps: determining concrete raw materials and mix proportions of the raw materials, performing a hydration heat test to obtain test results; inputting the test results of the hydration heat test into a Krstulovic-Dabic hydration kinetics model to obtain a fitting curve of a hydration reaction rate of the concrete and corresponding hydration kinetics parameters; simulating a rebound process of the shotcrete by a numerical simulation model to obtain a numerical simulation result of a rebound rate; fusing the fitting curve of the hydration reaction rate, the hydration kinetics parameters and the numerical simulation result of the rebound rate to obtain a rebound mechanism of the shotcrete; wherein, the construction steps of the numerical simulation model comprise: establishing a gas phase control equation, a particle phase control equation and a two-phase coupling model; establishing a wet shotcrete particle flow jet flow field according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

2. The method of claim 1, wherein the method further comprises: The gas phase control equation adopts a Navier-Stokes equation in CFD, and the gas phase mass equation is: Where, ρ g is the gas density, t is the time, u g for speed; The momentum conservation equation is: wherein, g is the gravity acceleration, p is the pressure, and S is the momentum exchange amount between the gas-solid two phases; The calculation formula of the momentum exchange amount is: where ΔV is the unit volume, f drag,i is the drag force.

3. The method of claim 1, wherein the method further comprises: The particle phase control equation is solved by DEM, the force between the mutually contacting particles is calculated by using a Hertz Mindlin with bonding model, and the motion parameters of the particles are solved, wherein the force between the mutually contacting particles comprises gravity, particle-particle contact force, particle-wall contact force and particle-gas interaction force. where f contact,ij is the contact force, f damp,ij is the damping force, m i is the mass of the particle i, v i is the velocity, t is the time, g 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 where f n,ij is the normal contact force, f t,ij is the tangential contact force; The damping force is: wherein is the normal damping force, is the tangential damping force.

4. The method of claim 1, wherein the method further comprises: The coupling calculation steps of the two-phase coupling model are as follows: the airflow flow field data in the calculation domain are calculated by using FLUENT; the particle force is calculated according to the airflow flow field data and the particle collision information by using EDEM, and the particle position and motion information are updated; the airflow flow field data are recalculated according to the momentum exchange amount provided by EDEM, and the coupling calculation is completed.

5. The method of claim 1, wherein the method further comprises: The wet shotcrete particle flow jet flow field comprises a wet shotcrete nozzle, an external jet flow field of the material and a sprayed wall surface.

6. The method of claim 5, wherein the method further comprises: The numerical simulation result is analyzed from the particle flow velocity field of the particle flow jet flow field to determine an effective spraying range.

7. The method of claim 6, wherein the method further comprises: The numerical simulation calculation result of the rebound rate is obtained by dividing the particle mass outside the effective spraying range on the sprayed wall surface within a stable spraying time by the total particle mass passing through the wet shotcrete nozzle.

8. The method of claim 1, wherein the method further comprises: determining a rebound value of the sprayed concrete; and determining a rebound mechanism of the sprayed concrete based on the rebound value. The rebound rate test is performed on the concrete raw materials, the rebound rate of the concrete is obtained by a rebound rate calculation formula from the test results of the rebound rate test, and the accuracy of the numerical simulation is verified by comparing the rebound rate with the numerical simulation result of the rebound rate.

9. The method of claim 8, wherein the method further comprises: The rebound rate calculation formula is as follows: n = W1 / (W1+W2) x 100% wherein, n is the rebound rate, W1 is the mass of the collected concrete under the sprayed wall surface, and W1+W2 is the total mass of the sprayed concrete.

10. A system for acquiring a shotcrete rebound mechanism, characterized by The method comprises the following steps: a test module for determining concrete raw materials and mix proportions of the raw materials, performing a hydration heat test to obtain test results; a hydration kinetics module, configured to input test results of the hydration heat test into a Krstulovic-Dabic hydration kinetics model to obtain a fitting curve of a hydration reaction rate of the concrete and corresponding hydration kinetics parameters; a numerical simulation module, configured to simulate a rebound process of the shotcrete by a numerical simulation model to obtain a numerical simulation result of a rebound rate; a fusion obtaining module, configured to fuse the fitting curve of the hydration reaction rate, the hydration kinetics parameters and the numerical simulation result of the rebound rate to obtain a rebound mechanism of the shotcrete. The numerical simulation model is constructed in the following steps: establishing a gas phase control equation, a particle phase control equation and a two-phase coupling model; establishing a wet shotcrete particle flow jet flow field according to the gas phase control equation, the particle phase control equation and the two-phase coupling model.

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