A DEM-CFD-based concrete robot arm operation simulation optimization method and system

Abstract

The present invention provides a DEM-CFD-based simulation optimization method and system for concrete manipulator operations. The method uses a discrete element method (DEM) and computational fluid dynamics (CFD) coupled calculation method to simulate the operation process of a wet spraying manipulator. By changing the initial conditions of the fluid model, the particle rebound rate in the calculation is minimized, that is, the concrete rebound rate is minimized under different wet spraying manipulator operating parameters. The wet spraying manipulator operating parameters with the minimum concrete rebound rate are used as a guide to achieve optimized control of the wet spraying concrete manipulator operation.

Description

Technical Field

[0001] The present invention belongs to the technical field related to concrete robot arm operations, and in particular relates to a DEM-CFD-based concrete robot arm operation simulation optimization method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Since the beginning of the 21st century, my country's tunnel engineering has entered a new phase of development and construction. Recent data indicates that my country has become the world leader in both the number and length of tunnels. As intercity high-speed rail construction expands into regions with complex and varied topography, my country's tunnel construction is increasingly characterized by high standards, large lengths, large cross-sections, deep burial depths, and complex geology. Manual construction, constrained by environmental conditions and plagued by numerous operational issues, no longer meets current tunnel construction standards. Consequently, the need for mechanized, digital, and intelligent tunnel construction equipment is imperative.

[0004] With the increasing use of wet-spraying trolleys in tunneling projects, the demand for continuous improvement in construction quality and efficiency is increasing to meet the requirements of today's tunnel construction. The wet-spraying concrete manipulator arm is the core component of the wet-spraying trolley, controlling its operational quality and efficiency. A wet-spraying concrete manipulator arm generally has the following eight degrees of freedom: boom pitch, boom extension, arm pitch, waist rotation, horizontal arm swing, horizontal arm extension, gun barrel posture adjustment, and wrist rotation. Using these eight degrees of freedom, the manipulator arm coordinates the determination of spray position, spray angle, and spray path, achieving the "optimal process principle" based on different construction conditions and circumstances: first, ensuring that the nozzle is perpendicular to the work surface; second, maintaining a distance of approximately 1 meter between the nozzle and the work surface.

[0005] Compared to manual shotcrete construction, the robotic arm can freely operate in all directions and maintain the optimal spraying angle and distance for a long time. This greatly shortens the construction time, improves work efficiency, effectively reduces the concrete rebound rate, and enhances on-site work results. Furthermore, the reduced concrete rebound rate reduces dust generation on the construction site, effectively improving the construction environment and alleviating damage to worker health.

[0006] However, my country's research on wet spraying concrete robotic arms started relatively late, and there is a lack of in-depth research on the mechanisms of concrete spraying flow field, incident angle, working wind pressure, spraying distance, and robotic arm motion trajectory in wet spraying robotic arms. It is impossible to accurately describe the impact of the above factors on the rebound rate of concrete, resulting in poor spraying quality, high rebound rate, material waste, and other problems. Even when the working wind pressure is large, weak rock blocks often fall and concrete peeling occurs after impacting the working surface. Summary of the Invention

[0007] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a DEM-CFD-based simulation optimization method for concrete robot arm operations, which uses a discrete element method (DEM) and computational fluid dynamics (CFD) coupling calculation method to simulate the wet spraying robot arm operation process. By changing the initial conditions of the fluid model, the particle rebound rate in the calculation is minimized, that is, the concrete rebound rate under different wet spraying robot arm operating parameters is minimized. The wet spraying robot arm operating parameters with the minimum concrete rebound rate are used as a guide to achieve optimized control of the wet spraying concrete robot arm operation.

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

[0009] Obtain the material characteristic parameters of wet shotcrete and establish a concrete particle model based on discrete element method;

[0010] Obtain the concrete jet flow field under the operation of the wet shotcrete robot arm, and establish a high-pressure air computational fluid model based on computational fluid dynamics;

[0011] Couple the concrete particle model with the high-pressure air computational fluid model;

[0012] The initial conditions of the high-pressure air computational fluid model were changed to simulate the operation process of the wet shotcrete robot arm under different working parameters.

[0013] A second aspect of the present invention provides a DEM-CFD-based wet shotcrete robot arm operation control system, comprising:

[0014] Concrete particle model building module: obtains the material characteristic parameters of wet shotcrete and builds a concrete particle model based on discrete elements;

[0015] High-pressure air computational fluid model building module: obtains the concrete jet flow field under the operation of the wet shotcrete robot arm, and establishes a high-pressure air computational fluid model based on computational fluid dynamics;

[0016] Model coupling module: couples the concrete particle model with the high-pressure air computational fluid model;

[0017] Robotic arm operation simulation module: changes the initial conditions of the high-pressure air computational fluid model to simulate the wet shotcrete robot arm operation process under different working parameters.

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

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

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

[0021] (1) Based on the actual wet shotcrete jet flow field, a high-precision simulation model of a large-scale particle-fluid system was established, which provided a numerical theoretical model basis for studying the operation of wet shotcrete robotic arms.

[0022] (2) By changing the initial conditions of the fluid calculation model to simulate the operation process of the wet shotcrete robot under different working parameters, it provides a way to carry out the numerical simulation research method of the wet shotcrete robot.

[0023] (3) It can synchronously set different working parameters of the wet spraying concrete robot arm during operation, and at the same time consider the comprehensive influence of various working parameters on the rebound rate of concrete, overcome the disadvantage of traditional tests that only consider a single working parameter, reduce the cost of trial and error, and improve the efficiency of theoretical research and equipment development.

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

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

[0026] Figure 1 This is a calculation flow chart for simulating the operation process of a wet shotcrete robot arm in the first embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a wet shotcrete robot arm in Example 1 of the present invention;

[0028] Figure 3 Schematic diagram of the motion trajectory of the wet spraying robot arm in the first embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the calculation model of the spray gun construction in the first embodiment of the present invention;

[0030] Figure 5 This is the fitting curve of the concrete mass changing with time in Example 1 of the present invention.

[0031] Description of the drawings: 1. Arm, 2. Arm, 3. Spray head, 4. Base, 5. Connecting rod, 6. Rotary motor, 7. Spray gun movement trajectory, 8. Nozzle movement trajectory, 9. Working surface, 10. Spray gun, 11. Concrete bundle DETAILED DESCRIPTION

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0033] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0034] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0035] Example 1

[0036] like Figures 1-4 As shown, this embodiment discloses a DEM-CFD-based concrete robot arm operation simulation optimization method, including:

[0037] Step 1: Obtain the material characteristic parameters of wet shotcrete and establish a concrete particle model based on discrete element method;

[0038] Step 2: Obtain the concrete jet flow field under the operation of the wet shotcrete robot arm and establish a high-pressure air computational fluid model based on computational fluid dynamics;

[0039] Step 3: Couple the concrete particle model with the high-pressure air computational fluid model;

[0040] Step 4: Change the initial conditions of the high-pressure air computational fluid model to simulate the operation process of the wet shotcrete robot arm under different working parameters.

[0041] In step 1 of this example, indoor concrete property testing was conducted to obtain the material properties of the wet-sprayed concrete required for on-site operations. These properties included fluidity, water retention, cohesion, density ρ, and strength, and the concrete particle size distribution was recorded. Based on these concrete property parameters, a concrete particle model was generated in the DEM module, and the inter-particle contact model and microscopic parameters corresponding to the macroscopic material properties were calibrated.

[0042] Based on the obtained wet shotcrete material characteristic parameters, the particle radius r and particle number N in the concrete calculation model are determined, and the microscopic parameters of the concrete particle model are calibrated, including the particle contact model, the friction coefficient μ, elastic modulus E, stiffness k, and bond strength σ between particles.

[0043] Calibration process of the mesoscopic parameters of the concrete particle model: Numerical simulation of indoor concrete property tests is carried out through DEM. First, a suitable contact model of concrete particles is selected, and the mesoscopic parameters in the contact model are changed so that the fluidity, cohesion and other properties exhibited by the simulated concrete particles are consistent with the properties obtained from the actual indoor concrete property tests. At this time, the mesoscopic parameters in the contact model are the mesoscopic parameters of the calibrated concrete particle model.

[0044] In this embodiment, the method further includes: establishing a working surface in the DEM module and calibrating the microscopic parameters of the particles and the working surface, wherein the parameters include: the contact model between the particles and the working surface, the friction coefficient μ ball-facet , elastic modulus E ball-facet , stiffness k ball-facet , bonding strength σ ball-facet Etc., so that the particle model can produce bonding and rebound effects, and the mass spatial distribution on the working surface is consistent with reality.

[0045] Calibration process of the microscopic parameters of particles and working surface: numerical simulation of concrete particles hitting the wall and rebounding is carried out through DEM. First, a suitable contact model of concrete particles and working surface is selected, and the microscopic parameters in the contact model are changed so that the particle model can produce bonding and rebound effects, the rebound rate is controlled within 20%, and the mass spatial distribution shape on the working surface is consistent with reality. At this time, the microscopic parameters in the contact model are the microscopic parameters for calibrating concrete particles and working surface.

[0046] In step 2 of this embodiment, the operating parameters of the wet spraying trolley and the wet spraying concrete robotic arm are obtained, and the jet flow field characteristics during the wet spraying operation are recorded.

[0047] Conduct on-site investigations and record the working parameters of the wet spraying trolley and wet spraying concrete robotic arm, including the working air pressure P, the spraying distance L from the nozzle to the working surface, the spraying path, etc.; use high-speed camera equipment to record the jet flow field during wet spraying operations, and analyze and obtain the initial velocity v0 of concrete particles, the jet flow field morphology, etc.

[0048] According to the actual concrete jet flow field, a high-pressure air computational fluid model is generated in the CFD module. To ensure that the entire process of concrete particle injection is affected by the fluid, the length of the fluid model should include the length of the nozzle and the distance from the nozzle to the working surface. At the same time, to ensure that the nozzle has sufficient range of motion, the width and height of the fluid model should be slightly larger than the width and height of the established working surface, thereby determining the calculation range of the fluid model. Taking into account factors such as calculation accuracy, calculation time, and modeling workload, tetrahedral grids of different sizes are used for the fluid model in the nozzle and the jet flow field model in the area from the nozzle to the working surface. The flow field in the nozzle is more complicated, and a smaller tetrahedral grid can be used to refine the grid there to improve calculation accuracy.

[0049] In this embodiment, based on the recorded working parameters of the wet spraying trolley and the wet spraying concrete robot arm, the boundary conditions for the fluid model calculation are set, that is, the inlet wind pressure of the high-pressure air flow field is set to the working wind pressure, and the outlet pressure of the flow field is set to 100 kPa (1 standard atmospheric pressure).

[0050] In this example, a coarse-graining method is used to modify the established shotcrete particle-fluid system, further enlarging the model particle size, reducing the calculation model scale, shortening the calculation time, and ensuring that the calculation results of the coarse-grained model are the same as those of the original model. The coarse-graining method is derived based on the energy conservation of the impulse theorem. The magnitude of the inter-particle force in the modified fluid-solid coupling model is:

[0051]

[0052] in, is the interaction force between air and particles after coarsening, is the interaction force between air and particles in the original system (coupling model before coarse-graining), and α is the size ratio of the coarse-grained particles to the original particles.

[0053] The magnitude of the force between particles and fluid:

[0054]

[0055] in, is the interaction force between particles after coarsening, is the interaction force between particles in the original system.

[0056] In step 3 of this embodiment, the high-pressure air computational fluid model in the CFD module and the concrete particle model generated in the DEM module are coupled for computational simulation. The calculation is stopped when the set number of iteration steps is reached, and a simulation of the wet shotcrete robot operation process is completed, thereby achieving a high-precision simulation of the wet shotcrete robot operation.

[0057] Specifically, DEM simulation calculations can provide information such as particle position, velocity, angular velocity, volume, etc. for CFD calculations, while CFD can provide information such as force and torque for discrete element calculations.

[0058] CFD first performs a flow field calculation for one time step, and DEM then starts the iterative calculation of the current time step. During this time step, DEM obtains CFD flow field information, including interphase forces such as drag force, and introduces the interphase forces into the particle motion calculation. After DEM completes one step of calculation, it passes the particle information and interphase forces back to the CFD module, and CFD performs the flow field calculation for the next time step.

[0059] In this embodiment, by changing the initial parameters of the fluid calculation model, where the initial parameters include: the movement speed and movement path of the nozzle, the distance between the nozzle and the working surface, and the angle, step 3 is repeated to obtain the rebound rate of the concrete under different initial conditions, and a change curve is drawn. The curve can reflect the operating effect of the wet spraying concrete robot under different working parameters; based on the drawn concrete rebound rate change curve, combined with a digital intelligent method, the control system of the wet spraying concrete robot is optimized to timely control and adjust the operation of the wet spraying concrete robot according to the complex tunnel construction site conditions, thereby reducing the concrete rebound rate during construction.

[0060] Figure 5 The fitting curves of concrete mass changing with time are given, including the fitting curves of concrete mass sprayed out of the spray gun and the fitting curves of concrete mass bonded to the working surface.

[0061] Example 2

[0062] The purpose of this embodiment is to provide a wet shotcrete robot arm operation control system based on DEM-CFD, including:

[0063] Concrete particle model building module: obtains the material characteristic parameters of wet shotcrete and builds a concrete particle model based on discrete elements;

[0064] High-pressure air computational fluid model building module: obtains the concrete jet flow field under the operation of the wet shotcrete robot arm, and establishes a high-pressure air computational fluid model based on computational fluid dynamics;

[0065] Model coupling module: couples the concrete particle model with the high-pressure air computational fluid model;

[0066] Robotic arm operation simulation module: changes the initial conditions of the high-pressure air computational fluid model to simulate the wet shotcrete robot arm operation process under different working parameters.

[0067] Example 3

[0068] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0069] Example 4

[0070] The purpose of this embodiment is to provide a computer-readable storage medium.

[0071] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0072] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0073] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0074] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A DEM-CFD-based concrete robot arm operation simulation optimization method, characterized in that: include: Obtain the material characteristic parameters of wet shotcrete and establish a concrete particle model based on discrete element method; Acquire the concrete jet flow field under the operation of the wet shotcrete robot arm, and establish a high-pressure air computational fluid model based on computational fluid dynamics; the high-pressure air computational fluid model includes the length of the nozzle and the distance from the nozzle to the working surface, and the width and height of the high-pressure air computational fluid model are greater than the width and height of the established working surface; Couple the concrete particle model with the high-pressure air computational fluid model; Changing the initial conditions of the high-pressure air computational fluid model to simulate the operation process of the wet shotcrete robot under different working parameters; The initial conditions include: the movement speed and movement route of the nozzle, the distance between the nozzle and the working surface, and the angle. The concrete rebound rate under different initial conditions of the high-pressure air computational fluid model is obtained, and the rebound rate change curve is drawn. The actual wet spraying concrete robot arm is controlled based on the working parameters of the wet spraying concrete robot arm when the concrete rebound rate is minimum.

2. The DEM-CFD-based concrete manipulator operation simulation optimization method according to claim 1, characterized in that: The material characteristic parameters of the wet shotcrete include fluidity, water retention, cohesion, density, strength and particle gradation.

3. The DEM-CFD-based concrete manipulator operation simulation optimization method according to claim 1, characterized in that: The working surface is established based on discrete elements, and the microscopic parameters of concrete particles and the working surface are calibrated based on the concrete particle model. The microscopic parameters include: contact model between particles and the working surface, friction coefficient, elastic modulus t, stiffness, and bond strength.

4. The DEM-CFD-based concrete manipulator operation simulation optimization method according to claim 1, characterized in that: The working parameters of the robotic arm include: the spraying distance from the nozzle to the working surface, the spraying path, the nozzle movement speed, and the movement route.

5. The DEM-CFD-based concrete manipulator operation simulation optimization method according to claim 1, characterized in that: The boundary conditions of the high-pressure air computational fluid model are set as follows: the inlet wind pressure of the high-pressure air flow field is set to the working wind pressure of the robot arm, and the outlet pressure of the high-pressure air flow field is set to standard atmospheric pressure.

6. A wet shotcrete robot arm operation control system based on DEM-CFD, characterized in that: include: Concrete particle model building module: obtains the material characteristic parameters of wet shotcrete and builds a concrete particle model based on discrete elements; High-pressure air computational fluid model establishment module: This module obtains the concrete jet flow field under the operation of the wet shotcrete robot arm and establishes a high-pressure air computational fluid model based on computational fluid dynamics. The high-pressure air computational fluid model includes the length of the nozzle and the distance from the nozzle to the working surface, and the width and height of the high-pressure air computational fluid model are greater than the width and height of the established working surface. Model coupling module: couples the concrete particle model with the high-pressure air computational fluid model; Robotic arm operation simulation module: changes the initial conditions of the high-pressure air computational fluid model to simulate the wet shotcrete robot arm operation process under different working parameters; The initial conditions include: the movement speed and movement route of the nozzle, the distance between the nozzle and the working surface, and the angle. The concrete rebound rate under different initial conditions of the high-pressure air computational fluid model is obtained, and the rebound rate change curve is drawn. The actual wet spraying concrete robot arm is controlled based on the working parameters of the wet spraying concrete robot arm when the concrete rebound rate is minimum.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the DEM-CFD-based concrete robot operation simulation optimization method according to any one of claims 1 to 5 are implemented.

8. A processing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the DEM-CFD-based concrete robot operation simulation optimization method according to any one of claims 1 to 5 are implemented.

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