Particle simulation method and device based on CFD-DEM coupling and medium

Abstract

The embodiment of the invention provides a particle simulation method and device based on CFD-DEM coupling and a medium, and belongs to the technical field of civil engineering. The method comprises the following steps: collecting particles of target rock soil, and preparing the particles into a simulation sample; scanning the simulation sample through CT (Computed Tomography) to obtain a gray scale data graph of the simulation sample; generating three-dimensional data of the particulate matter according to the grayscale data graph through preset software; and importing the three-dimensional data into a verification model, and based on the verification model, determining simulation data of the particulate matter through coupling of a CFD solver and a DEM solver. The invention aims to improve the accuracy of simulation data of particulate matter CFD-DEM coupling, so that the understanding of related soil by workers is improved, and the accuracy of soil numerical simulation in civil engineering is improved.

Description

Technical Field

[0001] The present application relates to the field of civil engineering technology, and in particular to a particle simulation method, device and medium based on CFD-DEM coupling. Background Art

[0002] In the field of civil engineering, the analysis of rock and soil particles helps to enhance the understanding of rock and soil mechanical behavior, helps predict natural disasters such as landslides or mudslides, and is of great significance for ensuring the safety of projects.

[0003] The CFD-DEM method combines multiphase flow simulation technology of fluid dynamics and discrete element method, and is suitable for studying complex systems of particle-fluid interaction. It is currently a commonly used simulation and analysis method for studying rock and soil particles. However, the particle models used in current simulation and analysis methods are generally spheres or prisms, which are different from the rock and soil particles with complex shapes in reality, resulting in the accuracy of the simulation results being affected and the simulation results being poor.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a particle simulation method, device and medium based on CFD-DEM coupling, aiming to improve the accuracy of simulation data of CFD-DEM coupling of particulate matter, thereby improving the accuracy of numerical simulation of soil in civil engineering rock.

[0006] To achieve the above purpose, one aspect of an embodiment of the present application proposes a particle simulation method based on CFD-DEM coupling, the method comprising: collecting particles of target rock and soil, and making the particles into simulated samples; Scan the simulated sample by CT to obtain a grayscale data map of the simulated sample; Generate three-dimensional data of the particle according to the grayscale data map through preset software; The three-dimensional data is imported into a verification model, and based on the verification model, simulation data of the particulate matter is determined by coupling a CFD solver and a DEM solver.

[0007] In some embodiments, the step of generating the three-dimensional data of the particle according to the grayscale data map by using preset software includes: Importing the grayscale data graph into the preset software, wherein the grayscale data graph forms orthogonal slices in the preset software; Based on the orthogonal slices, median filtering is performed by the preset software, and the images of the particles in the image are separated according to the grayscale data map to determine the characteristic information of each particle; The three-dimensional data corresponding to the particles in the simulated sample is generated according to the characteristic information.

[0008] In some embodiments, the step of generating the three-dimensional data corresponding to the particles in the simulated sample according to the characteristic information includes: The particle satisfying a preset condition is three-dimensionally reconstructed using a grid according to the characteristic information to obtain the three-dimensional data corresponding to the particle, wherein the preset condition includes that an equivalent spherical radius of the particle falls within a preset interval.

[0009] In some embodiments, the step of importing the three-dimensional data into the verification model includes: A target particle model matching a preset size is determined according to the three-dimensional data, and the target particle model is imported into a first model. The verification model includes the first model, and the first model includes a cuboid filled with fluid. The first model is used to simulate the motion state of the particles falling freely in the fluid.

[0010] In some embodiments, the step of determining the simulation data of the particulate matter based on the verification model by coupling a CFD solver and a DEM solver comprises: Importing the target particle model into the DEM solver, and initializing the fluid phase in the cuboid through the CFD solver; Controlling the target particle model to start free falling at a preset position in the fluid; During the falling process of the target particle model, bidirectional coupling is set between the DEM solver and the CFD solver to transfer target parameters to each other; When the target particle model enters a state of uniform motion, spatial information and velocity information of the target particle model under the first model are obtained from the DEM solver, and the simulation data includes the spatial information and the velocity information.

[0011] In some embodiments, the step of controlling the target particle model to start free falling at a preset position in the fluid comprises: A force balance equation is constructed according to the diameter and density of the target particle model, the drag coefficient and Reynolds number of the fluid, and the gravitational acceleration and translational velocity of the target particle model during the falling process, and the target particle model is controlled to fall freely in the fluid based on the force balance equation.

[0012] In some embodiments, the step of importing the three-dimensional data into the verification model includes: Making a sample model of the particle according to the three-dimensional data, wherein the sample model is made by setting a preset pressure in the upper and lower directions of the three-dimensional data of all the particles to compact; The sample model is imported into a second model, the verification model includes the second model, and the second model is used to simulate the state of the sample model under the impact of a constant water flow.

[0013] In some embodiments, the step of determining the simulation data of the particulate matter based on the verification model by coupling a CFD solver and a DEM solver comprises: Importing the sample model into the DEM solver, and initializing the fluid phase of the steady water flow through the CFD solver; Controlling the constant water flow to impact the sample model according to a preset path; In the process of the constant water flow impacting the sample model, setting a bidirectional coupling between the DEM solver and the CFD solver and transmitting target parameters to each other; The pore information of the sample model under the constant water flow impact is obtained from the DEM solver, and the simulation data includes the pore information.

[0014] To achieve the above object, another aspect of the embodiment of the present application provides a particle simulation device based on CFD-DEM coupling, the device comprising: A collection module, the collection module is used to collect particles of target rock and soil, and make the particles into simulated samples; A scanning module, the scanning module is used to scan the simulated sample by CT to obtain a grayscale data map of the simulated sample; A data processing module, the data processing module is used to generate three-dimensional data of the particle according to the grayscale data map through preset software; A verification module is used to import the three-dimensional data into a verification model, and based on the verification model, determine the simulation data of the particulate matter by coupling a CFD solver and a DEM solver.

[0015] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0016] The embodiments of the present application include at least the following beneficial effects: The present application provides a particle simulation method, device and medium based on CFD-DEM coupling, which directly collects particles of target rock and soil, makes the particles into simulated samples, and then obtains high-precision and multi-angle grayscale data maps of the simulated samples by CT scanning, and then generates three-dimensional data of the particles according to the grayscale data map by using preset software, imports the three-dimensional data into the verification model, and obtains the simulation data of the particles through the coupling simulation of the CFD solver and the DEM solver based on the verification model. Compared with the model that replaces particles with spheres and prisms, the three-dimensional data of particles generated based on CT scanning has higher accuracy, thereby improving the accuracy of the simulation data of the particle CFD-DEM coupling, which is conducive to improving the staff's understanding of the relevant soil and improving the accuracy of the numerical simulation of soil in civil engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a particle simulation method based on CFD-DEM coupling provided in an embodiment of the present application; Figure 2 is a schematic diagram of the image processing effect of steps S201 and S202; Figure 3 is a schematic diagram of three-dimensional data of particles in an embodiment of the present application; Figure 4 is a schematic diagram of a velocity variation curve of a target particle model during free fall in an embodiment of the present application; Figure 5 is a schematic diagram of a sample model of an embodiment of the present application; Figure 6 is a schematic diagram of a pore network model of a sample of an embodiment of the present application; Figure 7 It is a structural schematic diagram of a particle simulation device based on CFD-DEM coupling provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0020] Among the related technologies, the analysis of rock and soil particles in the field of civil engineering is conducive to enhancing the understanding of rock and soil mechanical behavior, helping to predict natural disasters such as landslides or mudslides, and is of great significance for ensuring the safety of engineering and preventing disasters. At present, in order to simulate the movement and state of rock and soil particles when they interact with fluids, the CFD-DEM method is generally used in combination with multiphase flow simulation technology of fluid dynamics and discrete element method. Among them, CFD (Computational Fluid Dynamics) is a technology used to simulate fluid flow conditions, and DEM (Discrete Element Method) is a numerical method used to simulate the movement of each particle and the contact behavior with each other. Therefore, the CFD-DEM coupled technology is suitable for studying complex systems of particle-fluid interaction, and is currently a commonly used simulation and analysis method for studying rock and soil particles. However, the particle model used in the current simulation and analysis method is generally a sphere or prism, which is different from the rock and soil particles with complex shapes in reality. When simulating rock and soil particles, it is easy to ignore the influence of their complex shapes when interacting with other particles or fluids, resulting in the accuracy of the simulation results being affected, the simulation results are poor, and the accuracy of the numerical simulation of soil in civil engineering is affected, which will also affect the staff's understanding of the shear strength, compressibility and hydraulic behavior of such soils.

[0021] In view of this, the present application provides a particle simulation method, device and medium based on CFD-DEM coupling. Figure 1 is an optional flow chart of a particle simulation method based on CFD-DEM coupling provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0022] Step S101, collecting particles of target rock and soil, and making the particles into simulated samples.

[0023] In this embodiment, the target rock and soil is selected as the granite residual soil at the slope position, which is also the rock and soil position that is more easily affected by fluid in the actual environment. In other embodiments, other target rock and soil can also be selected.

[0024] Specifically, the particles of the target rock and soil are collected. After the collection is completed, the plant roots or other impurities on the surface of the particles are cleaned by an ultrasonic cleaner, and then dried. The dried particles are placed on a vibration table, and the particles are made into a simulated sample of preset specifications. The preset specifications are set to a cylindrical shape with a diameter of 30 mm and a height of 30 mm. The simulated sample can be wrapped by a glass hollow container to facilitate its molding into the predetermined specifications. In other embodiments, if it is for the convenience of scanning or verification simulation, the preset specifications can also be set to other specifications.

[0025] Step S102, scanning the simulated sample by CT to obtain a grayscale data map of the simulated sample.

[0026] After wrapping the surface of the simulated sample with plastic wrap, the simulated sample is scanned by CT. The CT scan can be performed by selecting the device X-Radia Micro-XCT-400, which includes an X-ray emitter, a flat-panel detector, a CCD camera, and a rotating sample stage. During the scan, the X-ray energy is set to 125kV, the exposure time is 2.10 seconds, and each scan generates a total of 1785 grayscale data images. The scanning accuracy of the grayscale data image is 5 microns, which is conducive to fully scanning the complex structure of each particle surface, thereby improving the accuracy of subsequent simulation verification.

[0027] Step S103, generating three-dimensional data of the particles according to the grayscale data map through preset software.

[0028] The grayscale data map of the simulated sample obtained by CT scanning is equivalent to obtaining a high-precision two-dimensional image of each particle in the simulated sample taken from various angles. Based on this, the three-dimensional data of the particle can be generated through the preset software. In this process, the specific information of each particle in the simulated sample is also obtained. The particles that are not suitable for importing into the verification model can be excluded according to their size, and the amount of three-dimensional data of the particle can be streamlined, and the interference of abnormal particles on the subsequent verification model can be avoided.

[0029] Step S104: import the three-dimensional data into the verification model, and determine the simulation data of the particulate matter by coupling the CFD solver and the DEM solver based on the verification model.

[0030] The verification model is used to simulate the three-dimensional data of particles and simulate the movement of particles interacting with fluids in certain scenarios. In an embodiment of the present application, the verification model includes a first model and a second model. The first model is used to simulate the free fall movement of particles in the fluid, and the second model is used to simulate the state of compacted particles under the impact of a constant water flow. The three-dimensional data of the particles is imported into the verification model, the fluid mechanics in the simulation process is calculated using a CFD solver, the movement state of the particles is calculated using a DEM solver, and the CFD solver and the DEM solver are coupled to obtain simulation data of the particles in a complex system where particles interact with fluids.

[0031] Among them, the verification model can be created and generated using the Workbench software, and the verification model can be meshed using the Fluent Mesh function to improve the quality of the fluid simulation.

[0032] It should be noted that the fluid described in this embodiment is set to water. If there are other simulation requirements, other fluids can also be selected.

[0033] In the steps S101 to S104 shown in the embodiment of the present application, the particles of the target rock and soil are directly collected, the particles are made into simulated samples, and then the simulated samples are scanned by CT to obtain high-precision and multi-angle grayscale data maps of the simulated samples, and then the three-dimensional data of the particles are generated according to the grayscale data map by using preset software, and the three-dimensional data are imported into the verification model. Based on the verification model, the simulation data of the particles is obtained by coupling simulation of the CFD solver and the DEM solver. Compared with the model that replaces the particles with spheres and prisms, the three-dimensional data of the particles generated based on CT scanning has higher accuracy, thereby improving the accuracy of the simulation data of the CFD-DEM coupling of the particles, which is conducive to improving the staff's understanding of the relevant soil and improving the accuracy of the numerical simulation of soil in civil engineering.

[0034] In some embodiments, step S103 includes: Step 201, importing the grayscale data graph into the preset software, and forming orthogonal slices of the grayscale data graph in the preset software.

[0035] Step 202, based on the orthogonal slices, median filtering is performed by preset software, and the images of each particle in the image are separated according to the grayscale data map to determine the characteristic information of each particle.

[0036] Step 203: Generate three-dimensional data corresponding to the particles in the simulated sample according to the characteristic information.

[0037] Specifically, in this embodiment, the preset software is Avizo, refer to Figure 2 , Figure 2 This is a schematic diagram of processing a grayscale data graph by the software. From left to right in the figure are a schematic diagram of orthogonal slicing, a schematic diagram after median filtering processing, and a schematic diagram after separating the images of each particle. After the grayscale data graph of the simulated sample is imported into the software, orthogonal slices are formed, and further median filtering processing is performed based on the orthogonal slices to enhance the image effect. Then, the grayscale data in the image is compared with the grayscale threshold by using the software's SeparateObjects function to clarify the images of the particles and the background, and further separate the images of the connected particles in the image to avoid mutual interference between different particles in the image and affect the analysis of each particle. Finally, the software's Label Analysis function is used to determine the characteristic information of each particle in the image. The characteristic information includes one or more data such as volume, surface area, average value, number of voxels, slenderness ratio, flatness ratio, anisotropy, etc. Based on the acquired detailed characteristic information of each particle, a three-dimensional model corresponding to each particle, i.e., the three-dimensional data, can be generated.

[0038] By using preset software to process and analyze the high-precision grayscale data images obtained by CT scanning, and further generating corresponding three-dimensional data, it is possible to obtain the corresponding high-precision three-dimensional model of the particles through CT scanning, thereby supporting the simulation verification of the subsequent verification model and improving the accuracy of its simulation data.

[0039] In some embodiments, step S203 includes: According to the characteristic information, the particles meeting the preset conditions are three-dimensionally reconstructed using a grid to obtain the three-dimensional data corresponding to the particles. The preset conditions include that the equivalent spherical radius of the particles falls within a preset interval.

[0040] As described in the above embodiment, in order to avoid interference of abnormal particles with subsequent verification models, preset conditions and preset intervals are set. The preset interval is used to determine the equivalent spherical radius of the particles. For example, it is set to 0.025 mm to 5 mm. When the equivalent spherical radius of the particles falls within the preset interval, it is judged that the preset conditions are met, and the particles that do not meet the preset interval are excluded.

[0041] For particles that meet the preset conditions, three-dimensional reconstruction is performed using the grid according to their characteristic information to obtain corresponding three-dimensional data, which can be in STL format or other formats. Figure 3 , Figure 3 This is a schematic diagram comparing the captured images of some particles and the three-dimensional data after three-dimensional reconstruction. For the convenience of comparison, a group of comparison is framed by dotted lines in the figure. In the dotted frame, the left side is the captured image of the particles, and the right side is the three-dimensional data after three-dimensional reconstruction.

[0042] The particles are further screened through preset intervals to exclude particles that are not suitable for verification simulation, and the three-dimensional data of the particles is reconstructed based on the characteristic information obtained from the analysis to obtain a high-precision three-dimensional model of the particles, thereby supporting the simulation verification of subsequent verification models and improving the accuracy of their simulation data.

[0043] In step S104 of some embodiments, the step of importing the three-dimensional data into the verification model includes: A target particle model matching a preset size is determined according to the three-dimensional data, and the target particle model is imported into the first model. The verification model includes the first model, and the first model includes a cuboid filled with fluid. The first model is used to simulate the motion state of particles falling freely in the fluid.

[0044] Specifically, the first model in the verification model is used to simulate the motion state of particles falling freely in a fluid. The cuboid is established using Workbench software. In this embodiment, the size of the cuboid is 300x80x80mm. In other embodiments, it can also be set to other sizes, so as not to affect the free fall motion of the particles therein. At the same time, the cuboid is filled with fluid, that is, filled with water, thereby preparing the first model.

[0045] In addition, in the simulation verification of the first model, a particle matching the preset size is selected for the verification, and the preset size is an equivalent diameter of 1 mm. A three-dimensional model of a particle is selected from the three-dimensional data, and it is adjusted by scaling and the like to make its equivalent diameter 1 mm. The selection rule can be random, or selected according to specific experimental requirements, which is not limited here. The model is defined as the target particle model, and the corresponding simulation verification process is started by importing the target particle model into the first model. It should be noted that the verification of the first model only requires one particle, so it is sufficient to import one target particle model.

[0046] By determining a target particle model that matches a preset size and importing it into the first model, the preparation required for simulation verification of the first model is completed, thereby supporting subsequent simulation verification of the first model.

[0047] In step S104 of some embodiments, the step of determining the simulation data of the particulate matter by coupling the CFD solver and the DEM solver based on the verification model includes: The target particle model is imported into the DEM solver, and the fluid phase in the cuboid is initialized through the CFD solver.

[0048] The target particle model is controlled to start free falling at a preset position in the fluid.

[0049] During the falling process of the target particle model, a bidirectional coupling is set between the DEM solver and the CFD solver, and the target parameters are transferred to each other.

[0050] When the target particle model enters a state of uniform motion, the spatial information and velocity information of the target particle model under the first model are obtained from the DEM solver, and the simulation data includes the spatial information and the velocity information.

[0051] In the first model, the target particle model is imported into the DEM solver, and the fluid phase of the fluid filled in the cuboid is initialized by the CFD solver, wherein the viscosity coefficient of the fluid is set to The density of the particles is , Young’s modulus is 0.1 GPa, Poisson’s ratio is 0.3, and the calculation step size of DEM solver and CFD solver is .

[0052] The target particle model is controlled to start free falling at a preset position in the rectangular fluid, and the preset position is set to be 30 mm from the liquid surface. Then, the DEM solver and the CFD solver are set to be bidirectionally coupled and transfer target parameters to each other, including the CFD solver transferring the calculated fluid velocity, pressure and other physical properties to the DEM solver, and the DEM solver transferring the calculated particle phase volume fraction and other parameters to the CFD solver. The parameters involved in the mutual transfer are all the target parameters. This process is as described above. The calculation steps are cycled and simulated by the semi-analytical method until the target particle model enters a state of uniform motion in the fluid.

[0053] When the target particle model enters a uniform motion state in the fluid and no longer changes, the simulation ends. The spatial information and velocity information of the target particle model in this process are obtained from the DEM solver. The spatial information includes the instantaneous spatial distribution of the target particle model at each moment, and the velocity information includes its instantaneous velocity at each moment. The spatial information and velocity information are the simulation data to be verified by the first model. Figure 4 , Figure 4 This is the velocity change curve of the target particle model during free fall.

[0054] By importing the target particle model into the first model and performing calculations through coupling the DEM solver and the CFD solver, the free fall of particles in the fluid can be simulated and the corresponding simulation data can be obtained, which is beneficial to improve the staff's understanding of the relevant soil and improve the accuracy of numerical simulation of soil in civil engineering.

[0055] In some embodiments, the step of controlling the target particle model to start free falling at a preset position in the fluid comprises: A force balance equation is constructed according to the diameter and density of the target particle model, the drag coefficient and Reynolds number of the fluid, and the gravitational acceleration and translational velocity of the target particle model during the falling process, and the target particle model is controlled to fall freely in the fluid based on the force balance equation.

[0056] In the simulation verification of the first model, the balance of forces acting on the target particle model is controlled by the following force balance equations (1) and (2), specifically: (1) (2) in, is the diameter of the particle, and are the gravitational acceleration and translational velocity of the particles, is the density of the particles, is the density of the fluid, that is, the density of water, is the drag coefficient, is the Reynolds number.

[0057] By constructing a force balance equation to control the simulation verification process of the first model, it is ensured that the target particle model can achieve free fall without being disturbed by other factors, thereby improving the accuracy of its simulation data.

[0058] In step S104 of some embodiments, the step of importing the three-dimensional data into the verification model includes: A sample model of the particles is made according to the three-dimensional data. The sample model is made by compacting the three-dimensional data of all the particles with a preset pressure in the up and down directions.

[0059] The sample model is imported into the second model. The verification model includes the second model. The second model is used to simulate the state of the sample model under the impact of a constant water flow.

[0060] Specifically, the second model in the verification model is used to simulate the state of the sample model made by compacting particles under the impact of a constant water flow. In the second model, a cylinder is established using the Workbench software. In this embodiment, the size of the cylinder is 61.8 mm in diameter and 40 mm in height. In other embodiments, it can also be set to other sizes, with the standard of being able to wrap the sample model and having space for fluid to pass through. At the same time, the three-dimensional data of all particles are imported, and all particles are compacted by setting a preset pressure in the up and down directions, and so that they do not exceed the range of the cylinder in the horizontal direction, to form the sample model, and the envelope of the sample model is a cuboid, refer to Figure 5 , Figure 5 Schematic diagram of the cylinder and specimen model in the second model.

[0061] Different from the first model, the second model uses the three-dimensional data of all particles generated above.

[0062] By making the three-dimensional data of the particulate matter into a sample model and importing it into the second model, the preparation required for the simulation verification of the second model is completed, thereby supporting the subsequent simulation verification of the second model.

[0063] In step S104 of some embodiments, the step of determining the simulation data of the particulate matter by coupling the CFD solver and the DEM solver based on the verification model includes: The specimen model was imported into the DEM solver, and the fluid phase of the steady water flow was initialized through the CFD solver.

[0064] Control the constant water flow to impact the specimen model according to the preset path.

[0065] In the process of constant water flow impacting the specimen model, a bidirectional coupling is set between the DEM solver and the CFD solver, and the target parameters are transferred to each other.

[0066] The pore information of the sample model under the impact of constant water flow is obtained from the DEM solver, and the simulation data includes the pore information.

[0067] In the second model, the sample model is imported into the DEM solver, and the fluid phase of the constant water flow is initialized through the CFD solver. The inlet and outlet of the constant water flow are set in the cylinder, for example, above and below the sample model in the cylinder, respectively. In this way, under the restrictions of setting the inlet and outlet, the preset path of the constant water flow is determined. The settings of the remaining parameters can refer to the above-mentioned first model and will not be repeated here.

[0068] Control the constant water flow to impact the sample model according to the preset path. The water flow should pass through the pores of each particle in the sample model to reach the other end of the sample model and flow out from the outlet. In this process, set the DEM solver and the CFD solver to bidirectionally couple and transfer target parameters to each other. The target parameters can also refer to the first model mentioned above and will not be described in detail. Unlike the first model, the second model does not represent the stage or state of the end of the simulation verification. Instead, it sets the simulation time, for example, 1s, within 1s. The coupled calculation is performed with a step size of . When the simulation time is over, it means that the simulation verification of the second model is over. The pore information of the sample model under the impact of constant water flow is obtained from the DEM solver, and the PNM (PoreNetwork Model) pore network model is drawn. The pore network model is the simulation data to be verified by the second model. Figure 6 , Figure 6Schematic diagram of the pore network model, in which the particles are converted into equivalent spheres to avoid the influence of particle shape on the observation of pores.

[0069] By importing the sample model into the second model and calculating through coupling the DEM solver and the CFD solver, the simulation of the sample model made of compacted particles under the impact of constant water flow is realized, and the corresponding simulation data is obtained, which is beneficial to improve the staff's understanding of the relevant soil and improve the accuracy of numerical simulation of soil in civil engineering.

[0070] On the other hand, in the simulation verification of the first model and the second model, in the DEM solver, the movement of the target particle model includes translation and rotation, and its movement is controlled by equations (3) and (4), specifically: (3) (4) in, is the mass of the particles, is the translational velocity component of the particle, is gravity, is the interaction force generated by the fluid acting on the solid phase, is the force between particles, or between particles and walls such as cylinders or cuboids. is the moment of inertia tensor of the particle, is the angular velocity component of the particle, is the torque generated by the movement between particles, or between particles and walls such as cylinders or cuboids. The above equations (3) and (4) are applicable to the first model and the second model. In the case of the first model, the parameters can be set to the case of only one particle.

[0071] Furthermore, in the CFD solver, the fluid motion is solved based on the average mass and momentum conservation equations, which are solved by equations (5) and (6). Specifically: (5) (6) in, is the fluid volume fraction, u is the fluid phase velocity component, is the fluid density, p is the common pressure, is the fluid response force tensor, is the local pressure gradient, is the momentum exchange generated by the interaction between the fluid phase and the particles. The above equations (5) and (6) are applicable to the first model and the second model.

[0072] Furthermore, in the CFD solver and the DEM solver, the interaction between particles and fluid is solved by equations (7), (8) and (9), specifically: (7) (8) (9) in, is the combined force of the fluid, For resistance, is the pressure gradient force, is the volume of the particle, is the local pressure gradient, is the drag coefficient, is the projected area of ​​the particles in the flow direction, " is the relative velocity between particles and fluid. The above equations (7), (8) and (9) are applicable to the first model and the second model.

[0073] See also Figure 7 The embodiment of the present application further provides a particle simulation device based on CFD-DEM coupling, which can implement the above-mentioned particle simulation method based on CFD-DEM coupling, and the device includes: The collection module is used to collect particles from the target rock and soil and make the particles into simulated samples.

[0074] The scanning module is used to scan the simulated sample through CT to obtain the grayscale data map of the simulated sample.

[0075] The data processing module is used to generate three-dimensional data of particles according to the grayscale data map through preset software.

[0076] The verification module is used to import the three-dimensional data into the verification model, and based on the verification model, the simulation data of the particulate matter is determined by coupling the CFD solver and the DEM solver.

[0077] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0078] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned particle simulation method based on CFD-DEM coupling is implemented.

[0079] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0082] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0083] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0085] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0086] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0087] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0088] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0091] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A particle simulation method based on CFD-DEM coupling, characterized in that: The method comprises: collecting particles of target rock and soil, and making the particles into simulated samples; Scan the simulated sample by CT to obtain a grayscale data map of the simulated sample; Generate three-dimensional data of the particle according to the grayscale data map through preset software; The three-dimensional data is imported into a verification model, and based on the verification model, simulation data of the particulate matter is determined by coupling a CFD solver and a DEM solver.

2. The method according to claim 1, characterized in that The step of generating the three-dimensional data of the particle according to the grayscale data map by using preset software includes: Importing the grayscale data graph into the preset software, wherein the grayscale data graph forms orthogonal slices in the preset software; Based on the orthogonal slices, median filtering is performed by the preset software, and the images of the particles in the image are separated according to the grayscale data map to determine the characteristic information of each particle; The three-dimensional data corresponding to the particles in the simulated sample is generated according to the characteristic information.

3. The method according to claim 2, characterized in that The step of generating the three-dimensional data corresponding to the particles in the simulated sample according to the characteristic information comprises: The particle satisfying a preset condition is three-dimensionally reconstructed using a grid according to the characteristic information to obtain the three-dimensional data corresponding to the particle, wherein the preset condition includes that an equivalent spherical radius of the particle falls within a preset interval.

4. The method according to claim 1, characterized in that The step of importing the three-dimensional data into the verification model comprises: A target particle model matching a preset size is determined according to the three-dimensional data, and the target particle model is imported into a first model. The verification model includes the first model, and the first model includes a cuboid filled with fluid. The first model is used to simulate the motion state of the particles falling freely in the fluid.

5. The method according to claim 4, characterized in that The step of determining the simulation data of the particulate matter based on the verification model by coupling a CFD solver and a DEM solver comprises: Importing the target particle model into the DEM solver, and initializing the fluid phase in the cuboid through the CFD solver; Controlling the target particle model to start free falling at a preset position in the fluid; During the falling process of the target particle model, bidirectional coupling is set between the DEM solver and the CFD solver to transfer target parameters to each other; When the target particle model enters a state of uniform motion, spatial information and velocity information of the target particle model under the first model are obtained from the DEM solver, and the simulation data includes the spatial information and the velocity information.

6. The method according to claim 5, characterized in that The step of controlling the target particle model to start free falling at a preset position in the fluid comprises: A force balance equation is constructed according to the diameter and density of the target particle model, the drag coefficient and Reynolds number of the fluid, and the gravitational acceleration and translational velocity of the target particle model during the falling process, and the target particle model is controlled to fall freely in the fluid based on the force balance equation.

7. The method according to claim 1, characterized in that The step of importing the three-dimensional data into the verification model comprises: Making a sample model of the particle according to the three-dimensional data, wherein the sample model is made by setting a preset pressure in the upper and lower directions of the three-dimensional data of all the particles to compact; The sample model is imported into a second model, the verification model includes the second model, and the second model is used to simulate the state of the sample model under the impact of a constant water flow.

8. The method according to claim 7, characterized in that The step of determining the simulation data of the particulate matter based on the verification model by coupling a CFD solver and a DEM solver comprises: Importing the sample model into the DEM solver, and initializing the fluid phase of the steady water flow through the CFD solver; Controlling the constant water flow to impact the sample model according to a preset path; In the process of the constant water flow impacting the sample model, setting a bidirectional coupling between the DEM solver and the CFD solver and transmitting target parameters to each other; The pore information of the sample model under the constant water flow impact is obtained from the DEM solver, and the simulation data includes the pore information.

9. A particle simulation device based on CFD-DEM coupling, characterized in that: The device comprises: A collection module, the collection module is used to collect particles of target rock and soil, and make the particles into simulated samples; A scanning module, the scanning module is used to scan the simulated sample by CT to obtain a grayscale data map of the simulated sample; A data processing module, the data processing module is used to generate three-dimensional data of the particle according to the grayscale data map through preset software; A verification module is used to import the three-dimensional data into a verification model, and based on the verification model, determine the simulation data of the particulate matter by coupling a CFD solver and a DEM solver.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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