Numerical simulation method and system for cleaning wet concrete by rotating airflow

Abstract

The invention relates to the technical field of computational fluid mechanics, and discloses a numerical simulation method and system for sweeping wet concrete by rotating airflow, a discrete element and computational fluid mechanics coupling calculation method is adopted to simulate the wet concrete sweeping operation process, firstly, Design-Expert software is utilized to carry out parameter calibration on wet concrete particles, optimal parameters are determined, and the optimal parameters are calculated; secondly, modeling is conducted on a nozzle structure and a cleaning area, the two models are combined and then subjected to grid division, and grid files are imported into Fluent and EDEM to set parameters; by means of the method, the motion state of wet concrete particles in rotating airflow can be efficiently simulated, the motion state of particle swarms in the whole sweeping process can be displayed in real time, the interaction mechanism of the particles and fluid can be further studied from the macroscopic and microscopic angles, and the method has the advantages of being high in practicability and wide in application range. The problems that the manual cleaning efficiency is low, and the existing simulation technology cannot give consideration to coupling of the rotating domain and the three-phase flow are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of computational fluid dynamics, and particularly to a numerical simulation method and system for cleaning wet concrete with rotating air flow. Background Technique

[0002] Regarding the problem of cleaning particles after water jet breaks the road surface, manual methods are still widely used at present to ensure the road surface is clean. However, this method has obvious defects: the labor intensity of workers is high, the cleaning efficiency is low, it is difficult to reach the ideal operation level, which not only does not meet the high standards of modern highway maintenance, but also hinders the cost reduction and efficiency improvement of maintenance work. During the process of cleaning concrete particles, existing technologies mostly rely on linear jet air flow for cleaning, lacking in-depth exploration and utilization of the rotational characteristics of air flow, thus unable to fully exert the potential of rotational air flow in improving cleaning efficiency. In addition, when attempting to conduct numerical simulation through the coupling of Fluent and EDEM, many technical limitations are faced. Specifically, although the VOF interface supports three-phase flow coupling, due to the limitations of the interface itself, it cannot simulate complex scenarios that simultaneously include a static domain and a rotational domain; while the DDPM interface is suitable for rotational domain simulation, but has limited support for three-phase flow, especially difficult to accurately capture and represent key characteristics such as the adhesion of particles under the action of liquid. Therefore, to overcome the above technical problems, there is an urgent need to develop a new numerical simulation method and system for cleaning wet concrete with rotating air flow to achieve more efficient and accurate particle cleaning simulation and optimization. Summary of the Invention

[0003] (I) Technical Problems to be Solved Aiming at the deficiencies of the existing technology, the present invention provides a numerical simulation method and system for cleaning wet concrete with rotating air flow, which has the advantages of efficient cleaning, accurate simulation, and adaptability to complex scenarios, and solves the problems of low efficiency of manual cleaning and the inability of existing simulation technologies to take into account the coupling of the rotational domain and three-phase flow.

[0004] (II) Technical Solutions To achieve the above object, the present invention provides the following technical solutions: A numerical simulation method for cleaning wet concrete with rotating air flow, comprising the following steps: Step 1, Establishment and parameter calibration of the wet concrete particle model: Establish the discrete element software EDEM module. Based on the discrete element software EDEM, this module constructs a particle model using actual material characteristic parameters, calibrates the mesoscopic parameters through a series of experiments, and selects the Hertz-Mindlin with JKR contact model to simulate the adhesion and agglomeration effects between particles; Step 2. Modeling and meshing of the cleaning operation flow field: Establish the Fluent module of computational fluid dynamics software. This module acquires the rotating airflow field, constructs the nozzle and cleaning area models, merges them, performs meshing, and imports into Fluent for fluid dynamics settings. Meanwhile, wet concrete particles are generated in EDEM to prepare for fluid-structure interaction calculation; Step 3. Initialization and execution of fluid-structure interaction calculation: Establish a coupling calculation module. This module opens the coupling interfaces in EDEM and Fluent respectively, reads the DDPM coupling interface, starts the calculation after completing the initialization operation, and realizes the numerical simulation of cleaning wet concrete with rotating airflow; Step 4. Model optimization: Establish a model optimization module. This module further adjusts the model parameters according to the preliminary simulation results, and the parameters include changing the airflow velocity and particle size distribution; Step 5. Post-processing and analysis: Establish a post-processing and analysis module. This module is responsible for extracting the simulation calculation results of particle motion trajectories and cleaning efficiency from EDEM and Fluent, performing data analysis on the extracted calculation results, and evaluating the cleaning effect.

[0005] Preferably, for the acquisition of actual material property parameters in Step 1: Measure the key material properties such as Poisson's ratio, density, shear modulus, and particle size distribution of wet concrete particles.

[0006] Preferably, for the construction of the particle model in Step 1: Establish the geometric model and physical properties of concrete particles in EDEM based on the acquired material property parameters.

[0007] Preferably, the process of parameter calibration in Step 1 is as follows: a. Design a repose angle test using Design-Expert software to measure the repose angle under different parameters; b. Perform a Plackett-Burman test to screen out the parameters that have a significant impact on the repose angle; c. Conduct a steepest ascent test on the screened significant influencing factors to quickly approach the target response region; d. Design a Box-Behnken test to further calibrate and optimize the sensitivity coefficients; e. Determine the optimal set of particle mesoscopic parameters through comparison between actual tests and simulation results.

[0008] Preferably, for the application of the Hertz-Mindlin with JKR contact model: Select this model to simulate the adhesion and agglomeration phenomena between particles, incorporate the effects of electrostatic force and moisture content in the model, and calculate the bonding force and normal elastic contact force between different particles according to the provided calculation formulas.

[0009] Preferably, the calculation formula for the bonding force between different concrete particles in the Hertz-Mindlin with JKR contact model is as follows:

[0010] In the formula, represents the surface energy of particle 1, represents the surface energy of particle 2; represents the boundary energy between particle 1 and particle 2. When the particle materials are the same, the boundary energy is = 0, that is, = = , so the cohesive force between the same particles is 2 ; Among them, the wet particle normal elastic contact force FJKR and the normal overlap are respectively:

[0011]

[0012] In the formula, is the surface energy of the particle, is the equivalent elastic modulus, is the contact surface radius after the collision of the two particles, is the equivalent contact radius.

[0013] Preferably, the specific steps of the cleaning operation flow field modeling and mesh generation in the second step are as follows: S1. Obtain the rotating airflow field data and construct a circular cleaning area and nozzle model; S2. Perform parameter modeling on the incident angle, necking length, nozzle height, and number of nozzles of the nozzle; S3. Combine the cleaning area and nozzle model into a complete cleaning model and perform high-quality mesh generation; S4. Import the mesh into Fluent, set the gravitational acceleration, transient solver, Realizable k-epsilon turbulence model, and describe the motion of the fluid phase by setting the fluid-structure interaction control equation; S5. Set the time step, number of iterations, and configure the boundary conditions, including velocity inlet, pressure outlet, interface, and sliding mesh; S6. Establish a circular flow field area corresponding to Fluent in EDEM, set the particle factory to generate the wet concrete particles in the first step, and describe the motion state of the particles by setting the fluid-structure interaction control equation.

[0014] S7. Configure the gravity setting in the environment module and enter the solution interface to set the simulation time step.

[0015] Preferably, the fluid-structure interaction control equations in step 2 include a fluid-phase control equation and a solid-phase control equation. The fluid-phase control equation includes a continuity equation and a momentum equation, and the solid-phase control equation includes a particle-phase control equation, which is specifically divided into: (1) In Fluent, the motion of the fluid phase is described by setting the continuity equation and the momentum equation; (2) In EDEM, the motion state of the particles is described by setting the particle motion control equation.

[0016] Preferably, the continuity equation is:

[0017] In the formula, is the liquid density, and t is the time; is the Hamiltonian operator, v = (u, v, w) is the velocity vector of the liquid, representing the velocity components in the x, y, and z directions respectively; The momentum conservation equations in the x, y, and z directions are respectively expressed as:

[0018]

[0019]

[0020] In the formula, , , respectively represent the liquid density, pressure, and dynamic viscosity, , , are the components of gravity in the x, y, and z directions, is the Hamiltonian operator; The particle motion control equation is:

[0021]

[0022] In the formula, mi, ui, Ii, are respectively the mass, velocity, moment of inertia, and angular velocity of particle i, and are the normal contact force and tangential contact force between particle i and particle j, is the torque of particle i.

[0023] A numerical simulation system for wet concrete cleaning with rotating air flow, characterized in that: the system includes a discrete element software EDEM module, a computational fluid dynamics software Fluent module, a coupling calculation module, a model optimization module, a post-processing and analysis module.

[0024] Compared with the prior art, the present invention provides a numerical simulation method and system for wet concrete cleaning with rotating air flow, having the following beneficial effects: 1. By adopting the Hertz-Mindlin with JKR contact model and optimizing the mesoscopic parameters of the particles inside the model, the present invention can better simulate the adhesion and agglomeration effects between particles due to electrostatic force and moisture content by using this contact model. At the same time, the Design-Expert software is used to calibrate the mesoscopic parameters of the surface energy and static friction coefficient of the particles in combination with the Plackett-Burman test, the steepest ascent test and the Box-Behnken test, which can further improve the accuracy of the simulation, making the simulation results of the system of the present invention closer to the actual working conditions, providing a reliable theoretical basis for the optimization of subsequent wet concrete cleaning operations, and finally achieving the beneficial effect of accurately simulating the adhesion and agglomeration behavior of wet concrete particles.

[0025] 2. By establishing a rotating air flow cleaning model and adopting the Realizable k-epsilon turbulence model, the present invention comprehensively considers the key parameters of the nozzle incident angle, contraction length, and nozzle height in the cleaning model, and sets reasonable boundary conditions and turbulence models through the Fluent software, which can accurately simulate the flow characteristics of the rotating air flow in the cleaning area. This method helps to deeply analyze the cleaning mechanism of the air flow on wet concrete particles, provides a scientific basis for optimizing the design and operation parameters of the cleaning equipment, thereby improving the cleaning efficiency and quality, and achieving the beneficial effect of efficiently simulating the flow field distribution in the cleaning operation.

[0026] 3. By realizing the fluid-structure interaction simulation of EDEM and Fluent, through coupling the discrete element method (DEM) and computational fluid dynamics (CFD), and considering the interaction between the fluid phase and the solid phase at the same time, the present invention can comprehensively simulate the movement behavior of wet concrete particles in the rotating air flow and the flow characteristics of the air flow. This coupling simulation method can more realistically reflect the physical phenomena in the cleaning process, provides a powerful tool for in-depth study of the particle movement law, air flow distribution law and the interaction between the two in the cleaning process, helps to further optimize the cleaning process, reduce energy consumption, improve the cleaning effect, and make the method of the present invention achieve the beneficial effect of comprehensively analyzing the wet concrete cleaning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the numerical simulation of wet concrete cleaning with rotating air flow according to the present invention.

[0028] Figure 2 This is the simulation calculation model and mesh division diagram of the wet concrete particles cleaned by the present invention.

[0029] Figure 3 This is the simplified model diagram of Embodiment 1 of the present invention.

[0030] Figure 4 This is the simplified model diagram of Embodiment 2 of the present invention.

[0031] Figure 5 This is the simplified model diagram of Embodiment 3 of the present invention.

[0032] Figure 6 This is the simplified model diagram of Embodiment 4 of the present invention. Figure 7 This is the air velocity distribution under the action of multiple nozzles and the particle distribution diagram under the action of multiple nozzles in Embodiment 4 of the present invention. Detailed implementation manners Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Please refer to Figure 1 , a numerical simulation method for cleaning wet concrete by rotating air flow, including the following specific steps: Step 1: Obtain the material property parameters of the Poisson's ratio, density, shear modulus, and particle gradation of the actual wet concrete particles. Based on the discrete element software EDEM, establish a concrete particle model. Use Design-Expert software to calibrate the mesoscopic parameters such as surface energy, static friction coefficient, rolling friction coefficient, and collision recovery coefficient of the established concrete particle model through the angle of repose test; design a Plackett-Burman test to screen out the significant factors affecting the angle of repose, conduct a steepest ascent test on the selected factors to make it approach the target response area faster, and design a Box-Behnken test to calibrate and optimize the sensitivity coefficient, so as to determine a set of optimal parameters of the wet concrete particles through actual test comparison; In Step 1, the interaction model between particles is selected as the Hertz-Mindlin with JKR contact model, which can better simulate the obvious adhesion and agglomeration effects between particles due to electrostatic force and moisture content; In Step 1, the bonding force between different concrete particles in the Hertz-Mindlin with JKR contact model can be expressed as:

[0034] In the formula, represents the surface energy of particle 1, represents the surface energy of particle 2, represents the boundary energy between particle 1 and particle 2. When the particle materials are the same, the boundary energy is = 0, that is = = , so the cohesive force between the same particles is 2 ; Among them, the normal elastic contact force FJKR and the normal overlap of the wet particles are respectively:

[0035]

[0036] In the formula, is the surface energy of the particle, is the equivalent elastic modulus, is the contact surface radius after the collision of the two particles, is the equivalent contact radius; The advantages are as follows: By adopting the Hertz-Mindlin with JKR contact model and optimizing the mesoscopic parameters of the particles inside the model, by using this contact model, the adhesion and agglomeration effects generated by electrostatic force and moisture content between particles can be better simulated. At the same time, the Design-Expert software is used to calibrate the mesoscopic parameters of the surface energy and static friction coefficient of the particles in combination with the Plackett-Burman test, the steepest ascent test and the Box-Behnken test, which can further improve the accuracy of the simulation, make the simulation results of the system of the present invention closer to the actual working conditions, provide a reliable theoretical basis for the optimization of subsequent wet concrete cleaning operations, and finally achieve the beneficial effect of accurately simulating the adhesion and agglomeration behavior of wet concrete particles.

[0037] Step 2: Obtain the rotational airflow field during the cleaning operation, and separately model the nozzle model and the cleaning area model. The cleaning area is a circular flow field model with a radius of 250 mm and a height of 20 mm. When constructing the nozzle model, mainly model parameters such as the incident angle of the nozzle, the length of the reduced diameter, the height of the nozzle, and the number of nozzles. Combine the two independent models to obtain a complete cleaning model, and perform mesh division on the model; import the mesh into Fluent, set the gravitational acceleration, select transient solution, choose the Realizable k-epsilon model for the turbulence model, set the time step and the number of iterations, and set the boundary conditions for the overall model. Among them, the nozzle inlet is set as a velocity inlet, the surroundings of the cleaning area are set as a pressure outlet, and the outlet pressure is the standard atmospheric pressure. Set the contact surface between the nozzle and the cleaning area as an interface surface, and set the movement of the nozzle as a sliding mesh. Set the airflow velocity and the rotational speed of the nozzle according to the actual situation; import the mesh into EDEM, establish a particle factory in the circular flow field to generate the wet concrete particles in Step 1, and set the number of generated particles. Set the gravity in the environment module, and enter the solution interface to set the time step.

[0038] In Step 2, the fluid-solid coupling control equations include the fluid phase control equation and the solid phase control equation. The fluid phase control equation includes the continuity equation and the momentum equation, and the solid phase control equation includes the particle phase control equation. Among them, the continuity equation is:

[0039] In the formula, is the liquid density, and t is the time; is the Hamiltonian operator, v = (u, v, w) is the velocity vector of the liquid, representing the velocity components in the x, y, and z directions respectively, The momentum conservation equations in the x, y, and z directions are respectively expressed as:

[0040]

[0041]

[0042] In the formula, 、 、 respectively represent the liquid density, pressure, and dynamic viscosity, 、 、 are the components of gravity in the x, y, and z directions, is the Hamiltonian operator; Among them, the particle motion control equation is:

[0043]

[0044] In the formula, mi, ui, Ii, are respectively the mass, velocity, moment of inertia and angular velocity of particle i, and are the normal contact force and tangential contact force between particle i and particle j, is the torque of particle i.

[0045] The advantages are as follows: By establishing a rotating air flow cleaning model and adopting the Realizable k-epsilon turbulence model, key parameters such as the incident angle of the nozzle, the length of the reduced section, and the height of the nozzle are comprehensively considered in the cleaning model. By setting reasonable boundary conditions and turbulence models through Fluent software, the flow characteristics of the rotating air flow in the cleaning area can be accurately simulated. This method helps to deeply analyze the cleaning mechanism of the air flow on wet concrete particles, provides a scientific basis for optimizing the design and operation parameters of the cleaning equipment, thereby improving the cleaning efficiency and quality, and achieving the beneficial effect of efficiently simulating the flow field distribution in the cleaning operation.

[0046] Step 3: Open the coupling interface in EDEM; read the DDPM coupling interface in Fluent, perform initialization operations, and start the calculation. The specific process is as follows: (1) Coupling interface configuration: Activate the fluid-structure interaction interface in EDEM, load the DDPM coupling interface in Fluent, and complete the necessary initialization settings.

[0047] (2) Calculation startup: After ensuring that all settings are correct, start the coupling calculation, monitor key indicators during the calculation process, such as convergence and stability. Finally, adjust the model parameters or restart the calculation as needed until satisfactory simulation results are obtained.

[0048] The advantages are as follows: By realizing the fluid-structure interaction simulation between EDEM and Fluent, by coupling the discrete element method (DEM) and computational fluid dynamics (CFD), and considering the interaction between the fluid phase and the solid phase at the same time, the motion behavior of wet concrete particles in the rotating air flow and the flow characteristics of the air flow can be comprehensively simulated. This coupling simulation method can more realistically reflect the physical phenomena in the cleaning process, provides a powerful tool for deeply studying the particle motion law, air flow distribution law and their interaction in the cleaning process, helps to further optimize the cleaning process, reduce energy consumption, improve the cleaning effect, and enables the method of the present invention to achieve the beneficial effect of comprehensively analyzing the wet concrete cleaning process.

[0049] According to the method and system of the present invention, the embodiments are practiced as follows: Embodiment 1: Simplified model of single air flow cleaning single particle Objective: To provide a simplified model for the operation of single-airflow cleaning of single particles; Steps: A. Particle model construction: In EDEM, construct a single wet concrete particle model based on actual material property parameters and conduct parameter calibration; B. Flow field modeling: In Fluent, obtain the single-airflow field and construct a simplified nozzle and cleaning area model; C. Mesh generation and setting: Generate a mesh for the model, import it into Fluent, and set boundary conditions (velocity inlet, pressure outlet); D. Coupled calculation: Generate a single particle in EDEM, start the fluid-structure interaction calculation, and monitor the particle motion trajectory and cleaning efficiency.

[0050] Advantages: Example 1 can verify the feasibility of the method of the present invention under the simplest model and provide a basis for subsequent complex models; Example 2: Simplified model of multi-airflow cleaning of single particles Objective: To provide a simplified model for the operation of multi-airflow cleaning of single particles; Steps: A. Particle model construction: Similar to Example 1, construct a single wet concrete particle model; B. Flow field modeling: In Fluent, obtain the multi-airflow field and consider the case of multiple air inlets; C. Mesh generation and setting: Generate a mesh for the complex flow field, import it into Fluent, and set the corresponding boundary conditions; D. Coupled calculation: Generate a single particle, start the fluid-structure interaction calculation, and analyze the particle motion and cleaning effect under multi-airflow conditions.

[0051] Advantages: Demonstrate the flexibility and accuracy of the method of the present invention in dealing with multi-airflow situations; Example 3: Simplified model of single-airflow cleaning of multi-particles Objective: To provide a simplified model for the operation of single-airflow cleaning of multi-particles; Steps: A. Particle model construction: In EDEM, construct multiple wet concrete particle models and conduct parameter calibration; B. Flow field modeling: In Fluent, obtain the single-airflow field and construct the corresponding cleaning area model; C. Mesh generation and setting: Generate a mesh for the model, import it into Fluent, and set boundary conditions; D. Coupled calculation: Generate multiple particles in EDEM, start the fluid-structure interaction calculation, and analyze the interaction between particles and the overall cleaning effect.

[0052] Advantages: Prove the effectiveness of the method of the present invention in dealing with a large number of particles, and can simulate the adhesion and agglomeration phenomena between particles; Implementation 4: Simplified model of multi-airflow cleaning of multi-particles Objective: Provide a simplified model of the operation of multi-airflow cleaning of multi-particles; Steps: A. Particle model construction: Construct multiple wet concrete particle models in EDEM and calibrate detailed parameters; B. Flow field modeling: Obtain the multi-airflow field in Fluent, considering complex airflow distribution and interaction; C. Mesh generation and setting: Conduct fine mesh generation for the complex flow field, import it into Fluent, and set detailed boundary conditions; D. Coupled calculation: Generate multiple particles in EDEM, start the fluid-structure interaction calculation, and comprehensively analyze the cleaning effect and particle dynamic behavior under the conditions of multi-airflow and multi-particles.

[0053] Advantages: Comprehensively demonstrate the powerful ability and high-precision simulation effect of the present invention in dealing with complex scenarios.

[0054] Compare and analyze Examples 1-4 as follows in the table:

[0055] The following information is obtained from the above table: For the numerical simulation method of rotary airflow cleaning of wet concrete of the present invention, its specific effects are as follows: In terms of cleaning efficiency, taking the scenario of multi-airflow cleaning of multi-particles in Example 4 as an example, the cooperative operation of multi-airflows significantly shortens the cleaning time, and the airflow and particle distribution are as Figure 7 shown. Compared with manual cleaning, it can greatly improve the work efficiency. In terms of simulation accuracy, with the help of advanced discrete element software EDEM and computational fluid dynamics software Fluent, combined with reasonable model construction and parameter setting, it can accurately simulate the complex interaction between the rotary airflow and wet concrete particles, fully considering the adhesion and agglomeration phenomena between particles due to actual situations. Compared with the existing simulation technologies, it is closer to the actual cleaning process. In terms of scenario adaptability, from different examples of single-airflow single-particle to multi-airflow multi-particle, the information obtained is that this method can effectively solve the problem that the existing simulation technologies are difficult to take into account the coupling of the rotary domain and three-phase flow, making it better adapt to the requirements of various complex actual cleaning scenarios.

[0056] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A numerical simulation method for cleaning wet concrete with a swirling airflow, characterized in that: The following steps are involved: Step 1: Establishment of wet concrete particle model and parameter calibration: Establish the discrete element software EDEM module. Based on the discrete element software EDEM, this module uses the actual material characteristic parameters to build the particle model, and calibrates the microscopic parameters through a series of experiments. The Hertz-Mindlin with JKR contact model is used to simulate the adhesion and agglomeration between particles. Step 2: Modeling and meshing of the cleaning flow field: Establish a fluid dynamics software Fluent module, which obtains the rotating airflow flow field, builds the nozzle and cleaning area models, merges them, performs meshing, and imports them into Fluent for fluid dynamics settings. At the same time, wet concrete particles are generated in EDEM to prepare for fluid-solid coupling calculations. Step 3: Initialization and execution of fluid-solid coupling calculation: Establish a coupling calculation module, which opens the coupling interface in EDEM and Fluent respectively, reads the DDPM coupling interface, and starts the calculation after completing the initialization operation to realize the numerical simulation of rotating airflow sweeping wet concrete; Step 4: Model optimization: Establish a model optimization module, which further adjusts the model parameters based on the preliminary simulation results, including changing the air flow velocity and particle size distribution; Step 5. Post-processing and analysis: Establish a post-processing and analysis module, which is responsible for extracting the simulation calculation results of particle motion trajectory and cleaning efficiency from EDEM and Fluent, performing data analysis on the extracted calculation results, and evaluating the cleaning effect.

2. A numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1, characterized in that: Acquisition of actual material characteristic parameters in the step 1: measuring the key material characteristics of the wet concrete particles, such as Poisson's ratio, density, shear modulus and particle grading.

3. A numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1, characterized in that: The construction of the particle model in step 1: establishing the geometric model and physical properties of concrete particles in EDEM based on the acquired material characteristic parameters.

4. The numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1 is characterized in that: The process of parameter calibration in step 1 is as follows: a. Design the stacking angle test using Design-Expert software and measure the stacking angle under different parameters; b. Perform the Plackett-Burman test to screen out the parameters that have a significant impact on the stacking angle; c. Carry out the steepest climbing test on the screened obvious influencing factors to quickly approach the target response area; d. Design Box-Behnken test to further calibrate and optimize the sensitivity coefficient; e. Determine the optimal set of particle microscopic parameters by comparing actual test results with simulation results.

5. The numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1, characterized in that: Application of the Hertz-Mindlin with JKR contact model: This model is selected to simulate the adhesion and agglomeration phenomena between particles, the effects of electrostatic force and moisture content are combined in the model, and the adhesion force and normal elastic contact force between different particles are calculated according to the provided calculation formula.

6. A numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 5, characterized in that: The formula for calculating the bond force between different concrete particles in the Hertz-Mindlin with JKR contact model is: In the formula, represents the surface energy of particle 1, represents the surface energy of particle 2; represents the boundary energy between particle 1 and particle 2. When the particle materials are the same, the boundary energy is =0, that is = = , so the cohesive force between the same particles is 2 ; Among them, the normal elastic contact force FJKR and the normal overlap of the wet particles are They are: In the formula, is the surface energy of the particle, is the equivalent elastic modulus, is the contact surface radius after the two particles collide, is the equivalent contact radius.

7. The numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1, characterized in that: The specific steps of flow field modeling and meshing of the cleaning operation in step 2 are as follows: S1. Obtain the flow field data of the rotating airflow and construct a circular cleaning area and nozzle model; S2, parameter modeling of the nozzle incident angle, necking length, nozzle height and number of nozzles; S3, merging the cleaning area and nozzle model into a complete cleaning model and performing high-quality meshing; S4. Import the mesh into Fluent, set the gravity acceleration, transient solver, Realizable k-epsilon turbulence model, and describe the motion of the fluid phase by setting the fluid-solid coupling control equation; S5, set the time step, number of iterations, and configure boundary conditions, including velocity inlet, pressure outlet, interaction surface, and sliding mesh; S6. Establish a circular flow field area corresponding to Fluent in EDEM, set up a particle factory to generate the wet concrete particles in step 1, and describe the motion state of the particles by setting the fluid-solid coupling control equation; S7. Configure the gravity settings in the environment module and enter the solution interface to set the simulation time step.

8. The numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 1, characterized in that: The fluid-solid coupling control equation in step 2 includes a fluid phase control equation and a solid phase control equation. The fluid phase control equation includes a continuous phase equation and a momentum equation. The solid phase control equation includes a particle phase control equation, which is specifically divided into: (1) In Fluent, the motion of the fluid phase is described by setting the continuous phase equation and momentum equation; (2) In EDEM, the motion state of particles is described by setting the particle motion control equation.

9. A numerical simulation method for cleaning wet concrete with a swirling airflow according to claim 8, characterized in that: The continuous phase equation is: In the formula, is the liquid density, t is the time; is the Hamiltonian operator, v=(u,v,w)is the velocity vector of the liquid, representing the velocity components in the x, y, and z directions respectively; The momentum conservation equations in the x, y, and z directions are expressed as: In the formula, , , represent the liquid density, pressure and dynamic viscosity respectively, , , are the components of gravity in the x, y, and z directions, is the Hamiltonian operator; The particle motion control equation is: In the formula, mi, ui, Ii, are the mass, velocity, moment of inertia and angular velocity of particle i, respectively. and are the normal contact force and tangential contact force between particle i and particle j, is the torque of particle i.

10. A numerical simulation system for cleaning wet concrete with a rotating airflow, characterized in that: The system includes a discrete element software EDEM module, a fluid dynamics software Fluent module, a coupling calculation module, a model optimization module, and a post-processing and analysis module.

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