CN116244782A - Method and system for optimizing sprayed concrete based on DEM-CFD coupling - Google Patents
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Abstract
The invention provides an optimization method and system for sprayed concrete based on DEM-CFD coupling, which adopts a Discrete Element (DEM) and Computational Fluid Dynamics (CFD) coupling calculation method to simulate a wet spraying concrete spraying process, and the particle rebound rate in calculation is minimized by changing the mesoscopic parameters of a particle-plane contact model so as to represent the minimum concrete rebound rate, and meanwhile, the macroscopic characteristic parameters corresponding to the mesoscopic parameters when the particle rebound rate is minimum are determined based on the relationship between the mesoscopic parameters established by an indoor test and the characteristic parameters of the concrete material so as to deepen the study of the wet spraying concrete rebound mechanism and realize the effective optimization of the concrete material in the wet spraying technology.
Description
Technical Field
The invention belongs to the technical field of concrete, and particularly relates to an optimization method and system for sprayed concrete based on DEM-CFD coupling.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Along with the development and the needs of social economy, the number and the scale of construction of railways and highway tunnels in China are increased year by year. The lining support plays a key role in the safe construction and safe operation of the tunnel.
Under the current development background of tunnel engineering construction mechanization in China, the shotcrete technology is widely applied in the tunnel lining support process, finished concrete is conveyed to a nozzle through pumping pressure and mixed with an accelerator to form raw materials, and high-pressure air is utilized to spray the raw materials to tunnel surrounding rocks, so that the surrounding rocks are made into new structures with high strength and good stability, and the purpose of tunnel surrounding rock support is achieved.
The concrete spraying process goes through a development stage from dry spraying to wet spraying. At present, wet spraying concrete takes the dominant role in tunnel support, and the wet spraying technology greatly reduces the problems of high rebound rate, large dust amount, low construction quality and the like inherent in dry spraying operation, but the problem of high rebound rate of the wet spraying concrete still exists due to lack of deep research on wet spraying theory, and the speed and quality of tunnel construction are seriously influenced. According to statistics, the concrete rebound rate in tunnel construction is generally more than 12%, the maximum of the tunnel top can reach 30%, and the cost loss is about 100 ten thousand yuan/kilometer.
The wet spraying concrete jet flow field is analyzed and simulated by the methods of theoretical calculation, indoor test, field test and calculation fluid or discrete element numerical simulation by the scholars at home and abroad, and the internal movement mechanism of the wet spraying concrete jet flow field is difficult to deeply explore by a macroscopic test means, so that the movement state and rule of the wet spraying concrete particle flow cannot be accurately described by a single numerical calculation method, and the rebound quantity of concrete in actual construction cannot be guided by theory.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an optimization method and system for sprayed concrete based on DEM-CFD coupling, which adopts a Discrete Element (DEM) and Computational Fluid Dynamics (CFD) coupling calculation method to simulate the spraying process of wet sprayed concrete, and the rebound rate of particles in calculation is minimized by changing the microscopic parameters of a particle-plane contact model so as to represent the minimum rebound rate of the concrete, and meanwhile, the macroscopic characteristic parameters corresponding to the microscopic parameters when the rebound rate of the particles is minimum are determined based on the relationship between the microscopic parameters established by an indoor test and the characteristic parameters of the concrete material, so that the research on the rebound mechanism of the wet sprayed concrete is deepened, and the effective optimization of the concrete material in the wet spraying technology is realized.
To achieve the above object, one or more embodiments of the present invention provide the following technical solutions: an optimization method of sprayed concrete based on DEM-CFD coupling comprises the following steps:
obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
A second aspect of the present invention proposes an optimization system for sprayed concrete based on DEM-CFD coupling, characterized by comprising:
and a concrete particle calculation model construction module: obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
and (3) a concrete particle calibration module: acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
the high-pressure air flow field calculation model building module: building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
simulation and optimization module: and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
A third aspect of the invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method described above.
A fourth aspect of the invention provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the method described above.
The one or more of the above technical solutions have the following beneficial effects:
1. the invention establishes a fluid-solid coupling calculation model of high-speed high-pressure air and concrete particles aiming at the concrete spraying process, and provides a numerical simulation foundation for the mechanism research of the concrete spraying process.
2. The invention effectively simulates the movement characteristics and the mechanical characteristics of concrete particles under the action of high-speed high-pressure air in the operation of sprayed concrete, and solves the problems of large operation difficulty, poor test precision and the like of a macroscopic test means.
3. The invention simultaneously considers the combination of a plurality of groups of characteristic parameters of the concrete material, obtains the optimal effect of reducing the rebound rate of the concrete, and solves the problems of large variable control difficulty, high test material cost and the like of a macroscopic test means.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a calculation flow of a simulation implementation of a concrete spraying process in a first embodiment of the invention;
FIG. 2 is a schematic diagram of a simulation of a single concrete pellet and high pressure air system in accordance with a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a grid division of a streaming system in accordance with an embodiment of the present invention;
fig. 4 is a schematic view of a calculation model in the first embodiment of the present invention.
Description of the drawings 1, high pressure air System, 2, concrete particles, 3, nozzle, 4, jet zone
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1-4, the present embodiment discloses a method for optimizing sprayed concrete based on DEM-CFD coupling, including:
obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
In this embodiment, step 1: and obtaining characteristic parameters of the fresh concrete.
And (3) carrying out concrete tests on fresh concrete required in the field or a laboratory to obtain corresponding concrete material characteristic parameters, wherein the parameters comprise: density, particle size distribution, workability (flowability, water retention, cohesiveness), strength, and the like.
Step 2: and acquiring parameters of a particle-fluid system in the sprayed concrete.
Shooting a concrete spraying process in a construction site or a laboratory by using high-speed camera equipment, analyzing high-speed camera video, and obtaining the geometric shape and the initial speed v of concrete particles in the spraying process 0 And parameters such as flight trajectory and the like, and simultaneously recording the working wind pressure P of the wet spraying machine, the spraying distance L from the nozzle to the working surface and the jet flow area shape.
Step 3: and establishing a solid phase and fluid phase control equation.
Step 3-1: a fluid phase (high pressure air) control equation is established based on the CFD method, wherein the fluid phase control equation includes a continuity equation and a momentum equation. The continuity equation is expressed as:
wherein ρ is f For high pressure air density, u is the average velocity of the gas and ε is the void fraction of the gas.
The momentum equation is expressed as:
wherein p is the gas pressure, F f-p Is the interaction force between air and particles, τ is the gas stress tensor.
Step 3-2: and establishing a solid phase (concrete particles) motion control equation according to a DEM method, wherein the solid phase control equation comprises a translation equation and a rotation equation. The particle translation equation is expressed as:
wherein m is i For the mass of particle i, v i For the horizontal velocity of particle i, f c,ij For the contact force between the particles, f d,ij Is the viscous drag between the particles.
The particle rotation equation is expressed as:
wherein omega i For particle i angular velocity, T ij As tangential moment between particles, M ij Is the rolling friction moment among particles.
It can be found that the interaction force of air and particles exists in the established solid phase and fluid phase control equations, and the coupling calculation can be carried out by the two methods of DEM and CFD through the interaction force.
Step 4: and (5) high-precision fluid-solid coupling simulation of the concrete spraying process.
And 4-1, generating a concrete particle calculation model.
Determining the particle radius r, the particle number N and the initial speed v based on the parameters such as the density rho, the grading parameter, the initial speed v0 and the like of the concrete particles obtained in the step 1 and the step 2 0 And the method is used for establishing a particle computing model of the sprayed concrete in the DEM model. The actual sprayed concrete is simplified into particles one by one, each particle needs to be assigned with a radius r, and meanwhile, a limited number of particles need to be generated, namely the particle number N. The concrete spraying process is simulated, so that the particles cannot be stationary in place after being generated, and the initial speed v is required to be specified 0 So that the concrete particles move.
According to the characteristic test of the concrete material required in the site or laboratory, parameters such as fluidity, water retention, cohesiveness, strength and the like of the concrete are obtained, and the contact model of the concrete particle model and the microscopic parameters such as friction coefficient mu, elastic modulus E, rigidity k, bonding strength sigma and the like of the contact model are calibrated.
Calibrating a contact model of the concrete particle model: numerical simulation of an indoor concrete characteristic test is carried out through a DEM, a contact model of proper concrete particles is selected, and mesoscopic parameters in the contact model are changed, so that properties such as fluidity and cohesiveness of the simulated concrete particles are kept consistent with properties obtained by the actual indoor concrete characteristic test, and the mesoscopic parameters in the contact model are mesoscopic parameters of a calibrated concrete particle model.
Step 4-2: a high-speed high-pressure air flow field calculation model is generated.
Based on the working wind pressure P, the spraying distance L and the jet flow area morphological parameters in the step 2, a flow field calculation model is selected, a proper fluid calculation grid is divided, the air flow field inlet wind pressure P0 is determined, and the flow field outlet pressure is specified to be 100kPa (standard atmosphere).
Step 4-3: a working surface model is generated.
Based on the form and the size of the jet flow field, a working surface model is established, and particle-plane contact mesoscopic parameters are calibrated, wherein the parameters comprise: coefficient of friction mu ball-facet Modulus of elasticity E ball-facet Stiffness k ball-facet Adhesive tapeJunction strength sigma ball-facet Etc. so that the mass spatial distribution pattern of particles attached to the working surface is practically identical. Calibrating a mesoscopic parameter of particle-particle contact, wherein the parameter comprises: friction coefficient μ, elastic modulus E, stiffness k, bond strength σ, and the like.
The mesoscopic parameter calibration process of the contact of the particles and the plane comprises the following steps: the numerical simulation of concrete particle wall springback is carried out through the DEM, firstly, a proper contact model of the concrete particles and the working surface is selected, and the mesoscopic parameters in the contact model are changed, so that the particle model can generate bonding and springback effects, the springback rate is controlled within 20%, the mass space distribution form on the working surface accords with the reality, and at the moment, the mesoscopic parameters in the contact model are the mesoscopic parameters for calibrating the concrete particles and the working surface.
Working face model: the sprayed concrete particles are allowed to bind on or rebound after impact to a flat surface which simulates the walls of a reproduction tunnel.
Mesoscopic parameter calibration of particle-particle contact: numerical simulation of an indoor concrete characteristic test is carried out through a DEM, a contact model of proper concrete particles is selected, and mesoscopic parameters in the contact model are changed, so that properties such as fluidity and cohesiveness of the simulated concrete particles are kept consistent with properties obtained by the actual indoor concrete characteristic test, and the mesoscopic parameters in the contact model are mesoscopic parameters of a calibrated concrete particle model.
And 5, reducing the size of the particle-fluid system and shortening the calculation time.
And (3) adopting a coarse graining method, correcting the sprayed concrete particle-fluid system established in the step (4), further enlarging the particle size of the model, reducing the scale of the calculation model, shortening the calculation time, and ensuring that the calculation effect of the model after coarse graining is the same as that of the original model. The coarse graining method is derived based on impulse theorem energy conservation, and the particle acting force of the fluid-solid coupling model after correction is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the interaction force between air and particles after coarsening, +.>
The interaction force between air and particles in the original system (coupling model before coarse graining), and alpha is the particle size ratio of coarse graining particles to original particles.
Size of force between particles and fluid:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the interaction force between the coarsened particles, +.>
Is the interaction force among particles of the original system.
Step 6: and simulating a concrete spraying process.
And carrying out iterative simulation calculation on the concrete spraying process, stopping calculation when the set iterative steps are reached, and completing one-time concrete spraying simulation.
In particular, DEM simulation calculations may provide information on the position, velocity, angular velocity, volume, etc. of particles for CFD calculations, while CFD may provide information on forces, moments, etc. for discrete element part calculations.
The CFD firstly carries out flow field calculation of a time step, the DEM starts iterative calculation of the current time step, the DEM acquires CFD flow field information in the time step, the CFD comprises drag force and other interaction forces, the interaction forces are introduced into particle motion calculation, after the DEM completes one-step calculation, the particle information and the interaction forces are transmitted back to the CFD module, and the CFD carries out flow field calculation of the next time step.
Step 7: and obtaining the mesoscopic parameter when the rebound rate is minimum.
And (3) modifying the inter-particle mesoscopic parameters, updating the concrete particle model, repeating the step (6) to simulate and calculate the concrete spraying process, and obtaining the mesoscopic parameters of the concrete particle model when the rebound rate is minimum.
Step 8: optimizing the fresh concrete material.
And (3) optimizing the properties of the freshly mixed concrete through means of indoor tests such as changing the mixing ratio of the concrete, additives and the like based on the particle microscopic parameters with the minimum rebound rate obtained in the step (7), and carrying out a spraying test on the freshly mixed concrete to obtain the corresponding minimum rebound rate, thereby realizing the optimal design method for the sprayed concrete process based on the particle microscopic parameters.
Example two
An optimization system for sprayed concrete based on DEM-CFD coupling, comprising:
and a concrete particle calculation model construction module: obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
and (3) a concrete particle calibration module: acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
the high-pressure air flow field calculation model building module: building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
simulation and optimization module: and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
Example III
It is an object of the present embodiment to provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the above method when executing the program.
Example IV
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium having stored thereon a computer program, the program being executed by a processor
The steps involved in the devices of the second, third and fourth embodiments correspond to those of the first embodiment of the method, and the detailed description of the embodiments can be found in the related description section of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. The method for optimizing the sprayed concrete based on DEM-CFD coupling is characterized by comprising the following steps of:
obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
2. A method of optimizing sprayed concrete based on DEM-CFD coupling as claimed in claim 1, wherein the fresh concrete required in the field or laboratory is subjected to a concrete test to obtain concrete material characteristic parameters including density, grain size, flowability, water retention, cohesiveness, strength.
3. The method for optimizing sprayed concrete based on DEM-CFD coupling according to claim 1, wherein the geometrical parameters, the motion parameters and the jet parameters of the wet spraying machine of the sprayed concrete are obtained by shooting the concrete spraying process in a construction site or a laboratory through high-speed camera equipment; the geometric parameters comprise the size and the shape of sprayed concrete particles, and the motion parameters comprise the flight track and the initial speed of sprayed concrete;
the jet flow parameters comprise working wind pressure of the wet spraying machine, spraying distance from a nozzle to a working surface and jet flow area shape.
4. A method of optimizing sprayed concrete based on DEM-CFD coupling as in claim 1, wherein said particles' mesoscopic parameters with the planar contact model include coefficient of friction, modulus of elasticity, stiffness, bond strength; the mesoscopic parameters of the particle-to-particle contact include: coefficient of friction, modulus of elasticity, stiffness, bond strength.
5. A method of optimizing sprayed concrete based on DEM-CFD coupling as in claim 1, wherein the coupling calculation model is modified using a coarse-grained approach based on impulse theorem and conservation of energy.
6. The method for optimizing sprayed concrete based on DEM-CFD coupling according to claim 4, wherein the interaction force between air and particles in the coupling calculation model corrected by adopting the coarse grain method is the product of the interaction between air and particles in the original coupling calculation model and the cube of the grain size ratio of coarse grain particles to original particles;
and the interaction force among the grains after coarsening in the corrected coupling calculation model is equal to the interaction force among the grains in the original coupling calculation model.
7. The method for optimizing sprayed concrete based on DEM-CFD coupling according to claim 1, wherein in the coupling calculation simulation of sprayed concrete, the mesoscopic parameters of the concrete particle calculation model corresponding to the time when the rebound rate is minimum are obtained, and the characteristic parameters of the concrete material are optimized through the mesoscopic parameters.
8. An optimization system for sprayed concrete based on DEM-CFD coupling, comprising:
and a concrete particle calculation model construction module: obtaining geometrical parameters, motion parameters and jet flow parameters of sprayed concrete, and establishing a concrete particle calculation model based on the DEM according to the geometrical parameters and the motion parameters;
and (3) a concrete particle calibration module: acquiring characteristic parameters of the mixed concrete material, and calibrating microscopic parameters of the particle-particle and particle-plane contact model under the concrete particle calculation model according to the characteristic parameters of the concrete;
the high-pressure air flow field calculation model building module: building a high-pressure air flow field calculation model based on CFD according to jet flow parameters of the sprayed concrete;
simulation and optimization module: and (3) coupling calculation simulation of the concrete particle calculation model and the high-pressure air flow field calculation model, changing the mesoscopic parameters of the concrete particle calculation model, and optimizing the concrete material characteristic parameters.
9. A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of optimizing sprayed concrete based on DEM-CFD coupling as claimed in any one of claims 1 to 7.
10. A processing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of a method of optimizing sprayed concrete based on DEM-CFD coupling as claimed in any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116992795A (en) * | 2023-09-28 | 2023-11-03 | 北京城建集团有限责任公司 | Tunnel ventilation-spray dust removal simulation method and system based on DEM-CFD coupling |
| CN117851749A (en) * | 2024-03-07 | 2024-04-09 | 西华大学 | Method and system for controlling rebound rate of sprayed concrete |
| CN118136130A (en) * | 2024-05-10 | 2024-06-04 | 四川农业大学 | Molecular power calculation method for nanocrystalline glue polymer sprayed concrete |
| CN118136129A (en) * | 2024-05-10 | 2024-06-04 | 四川农业大学 | Method for simulating injection-rebound molecular power under injection pressure of glue materials with different fineness |
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2022
- 2022-11-30 CN CN202211519651.2A patent/CN116244782A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116992795A (en) * | 2023-09-28 | 2023-11-03 | 北京城建集团有限责任公司 | Tunnel ventilation-spray dust removal simulation method and system based on DEM-CFD coupling |
| CN116992795B (en) * | 2023-09-28 | 2024-02-13 | 北京城建集团有限责任公司 | Tunnel ventilation-spray dust removal simulation method and system based on DEM-CFD coupling |
| CN117851749A (en) * | 2024-03-07 | 2024-04-09 | 西华大学 | Method and system for controlling rebound rate of sprayed concrete |
| CN117851749B (en) * | 2024-03-07 | 2024-05-14 | 西华大学 | Method and system for controlling rebound rate of sprayed concrete |
| CN118136130A (en) * | 2024-05-10 | 2024-06-04 | 四川农业大学 | Molecular power calculation method for nanocrystalline glue polymer sprayed concrete |
| CN118136129A (en) * | 2024-05-10 | 2024-06-04 | 四川农业大学 | Method for simulating injection-rebound molecular power under injection pressure of glue materials with different fineness |
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