A multi-scale particle flow calculation method

Abstract

The present invention relates to the field of multiphase flow numerical simulation, and discloses a multi-scale particle flow calculation method, including: obtaining the position coordinates, velocity and volume of particles at the current time step; and combining with the grid distribution in Fluent software to locate the grid where the particles are located, obtaining the volume value of the grid to calculate the grid particle size ratio at the current moment; according to whether the grid particle size ratio is greater than 3, different methods are used to calculate the grid porosity and background flow field information; substituting the porosity, background flow field information, particle velocity and particle volume into the drag force model for calculation to obtain the drag force received by the particles; inputting the drag force into EDEM software to iteratively calculate the motion of the particles to obtain the motion state of the particles at the next time step; adding the negative value of the drag force to the momentum equation of Fluent software in the form of a source term to iteratively calculate the motion of the fluid to obtain the velocity and pressure information of all grids at the next time step. The accurate numerical simulation of multi-scale particles is realized.

Description

Technical Field

[0001] This application relates to the field of multiphase flow numerical simulation, and particularly to a multiscale particle flow calculation method. Background Art

[0002] The particle-fluid two-phase flow describes a complex physical system formed by the interaction between particles and fluids, which includes a particle system, a fluid system, and the exchange of momentum and energy between particles and fluids. It is an important branch of the multiphase flow system and widely exists in natural phenomena and industrial production. With the rapid development of modern computer technology, numerical simulation has become an important method for studying particle-fluid two-phase flow. The research on numerical simulation methods helps to understand the motion laws of particulate matter itself and the mechanism of fluid-solid coupling between particles and fluids.

[0003] The unresolved CFD-DEM model is the most widely used computational model for describing particle-fluid two-phase flow. This model does not require an accurate description of particle boundaries and the resolution of the flow field around particles. The forces exerted by the fluid on the particles are calculated through various empirical formulas, and the reaction forces of the particles on the fluid act on the fluid cells through a local averaging method. In order to better capture information such as the velocity and porosity of the background fluid around particles, the unresolved CFD-DEM model requires a particle grid size ratio greater than 3. When the grid size ratio is less than 3, the calculation accuracy of the unresolved CFD-DEM model is greatly reduced. Therefore, this model is mostly used for the simulation of large-scale small-scale particles. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a multiscale particle flow calculation method to solve the limitation problem that the traditional unresolved CFD-DEM method can only be used to calculate small-scale particles with a grid size ratio above 3.

[0005] According to the embodiments of this application, a multiscale particle flow calculation method is provided, including:

[0006] Obtain the position coordinates, velocity, and volume of particles at the current time step from the EDEM software;

[0007] Obtain the position distribution information of all grids in the Fluent software, and based on the position coordinates of the particles, locate the grid where the particle center is located, obtain the volume value of this grid, and calculate the grid size ratio at the current moment by comparing it with the volume of the particles;

[0008] Calculate the porosity of the grid using two different methods for grid size ratios greater than 3 and less than or equal to 3 respectively;

[0009] Calculate the background flow field information using two different methods for grid size ratios greater than 3 and less than or equal to 3 respectively;

[0010] Substitute the porosity, background flow field information, velocity of the particles, and volume of the particles into the drag force model for calculation to obtain the drag force on the particles;

[0011] Input the drag force into the EDEM software to iteratively calculate the motion of the particles, and obtain the position and velocity information of the particles at the next time step;

[0012] Add the negative value of the drag force to the momentum equation of the Fluent software in the form of a source term to iteratively calculate the motion of the fluid, and obtain the velocity and pressure information of all grids at the next time step.

[0013] Optionally, the grid particle size ratio is , where is the volume of the grid where the particle center is located, is the volume of the particle.

[0014] Optionally, when the grid particle size ratio is greater than 3 and less than or equal to 3, two different methods are used to calculate the porosity of the grid, including:

[0015] According to the grid particle size ratio, calculate the solids holdup of the grid within the range of particle action. When the grid particle size ratio is greater than 3, the particle volume is dispersed in multiple uniformly distributed characteristic points, and each characteristic point equally contains a part of the volume of the particle. When the characteristic point is located in a certain grid, it is considered that the part of the particle volume contained in the characteristic point belongs to the grid; when the grid particle size ratio is less than or equal to 3, the particle volume is weighted and distributed within the surrounding grids through a statistical kernel function;

[0016] After traversing all the particles, the porosity within the grid is calculated by the following formula:

[0017]

[0018] In the formula is the solids holdup of the i-th particle acting on the x-th grid within the fluid domain.

[0019] Optionally, when the grid particle size ratio is greater than 3 and less than or equal to 3, two different methods are used to calculate the background flow field information, including:

[0020] When the grid particle size ratio is greater than 3, the flow field information of the grid where the particle center is located is used as the background flow field information of the particle;

[0021] When the grid particle size ratio is less than or equal to 3, extract the flow field information of the grids where six uniformly distributed characteristic points on the particle surface are located and perform averaging processing, which is used as the background flow field information where the particle is located.

[0022] The technical solutions provided in the embodiments of the present application may include the following beneficial effects:

[0023] As can be seen from the above embodiments, for different grid particle size ratios, the present application adopts different methods to calculate the porosity and background flow field information required in the unresolved CFD-DEM particle flow field calculation model. At the same time, considering the time-varying nature of the grid particle size ratio, the grid particle size ratio is updated at each time step, improving the calculation accuracy, solving the limitation problem that the traditional unresolved CFD-DEM method is only applicable to calculating small-scale particles, and realizing the accurate calculation of multi-scale particles by the unresolved CFD-DEM method.

[0024] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flowchart of a multi-scale particle porosity calculation method shown according to an exemplary embodiment.

[0026] Figure 2 is a schematic diagram of a solid volume fraction calculation method shown according to an exemplary embodiment, where a is the DPVM method and b is the SKM method.

[0027] Figure 3 is a schematic diagram of a background flow field information calculation method shown according to an exemplary embodiment.

[0028] Figure 4 is a schematic diagram of a large-scale particle calculation verification model shown according to an exemplary embodiment.

[0029] Figure 5 is a comparison of large-scale particle calculation verification results shown according to an exemplary embodiment.

[0030] Figure 6 is a schematic diagram of a multi-scale particle calculation verification model shown according to an exemplary embodiment.

[0031] Figure 7 is a comparison of multi-scale particle calculation verification results shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description relates 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 present application.

[0033] The technical solutions of the present invention will be described in detail below in conjunction with the drawings and embodiments.

[0034] Figure 1It is a flowchart of a multi-scale particle flow calculation method shown according to an exemplary embodiment. As Figure 1 shown, the method may include the following steps:

[0035] S1: Obtain the position coordinates, velocity, and volume of particles at the current time step from the EDEM software.

[0036] Specifically, use the API development environment provided by the EDEM software to write and run a script program for obtaining the position coordinates, velocity, and volume of particles. Through the custom script, key information can be flexibly obtained, the dynamic information of particles can be systematically obtained, and accurate data of particles can be provided.

[0037] S2: Obtain the position distribution information of all grids in the Fluent software, and based on the position coordinates of the particles, locate the grid where the particle center is located, obtain the volume value of this grid, and calculate the grid particle size ratio at the current moment by comparing it with the volume of the particle;

[0038] Specifically, use the user-defined function (UDF) function provided by the Fluent software to extract grid information, including grid position (node coordinates and grid center coordinates) and volume, and then find the nearest grid center according to the particle position to achieve the matching of the grid and the particle position.

[0039] Calculate the grid particle size ratio based on the mutually matching grid and particle volume .

[0040] In the formula is the volume of the grid where the particle center is located, is the volume of the particle.

[0041] S3: Calculate the porosity of the grid using two different methods for the grid particle size ratio greater than 3 and less than or equal to 3 respectively;

[0042] Specifically, Figure 2 is a schematic diagram of the solid volume fraction calculation method shown according to an exemplary embodiment. When calculating small-scale particles with a grid particle size ratio greater than 3, the DPVM method is used, that is, the particle volume is dispersed in multiple uniformly distributed characteristic points, and each characteristic point equally contains a part of the volume of the particle. When the characteristic point is located in a certain grid, it is considered that the part of the volume of the particle contained in this characteristic point belongs to this fluid grid; when calculating large-scale particles with a grid particle size ratio less than 3, the SKM method is used, that is, the particle volume is weighted and distributed within the surrounding grids through a statistical kernel function.

[0043] After traversing all particles, the porosity in the grid is calculated by the following formula:

[0044]

[0045] In the formula is the solid volume fraction exerted by the i-th particle on the x-th grid in the fluid domain.

[0046] By analyzing the grid particle size ratio to judge the relative scale of the particles, and adopting different porosity calculation methods accordingly, accurate porosity values can be obtained for particles of different scales, improving the accuracy of subsequent drag force calculation.

[0047] S4: Use two different methods to calculate the background flow field information for the grid particle size ratios greater than 3 and less than or equal to 3 respectively;

[0048] Specifically, when calculating small-scale particles with a grid particle size ratio greater than 3, the flow field information of the grid where the particle center is located is used as the background flow field information of the particle; when calculating large-scale particles with a grid particle size ratio less than 3, Figure 3 is a schematic diagram of the background flow field information calculation method shown in an exemplary embodiment. The flow field information of the grids where six uniformly distributed characteristic points on the particle surface are located is extracted and averaged to be used as the background flow field information where the particle is located.

[0049] By analyzing the grid particle size ratio to judge the relative scale of the particles, different background flow field information calculation methods are adopted for particles of different scales. While ensuring the calculation accuracy, it greatly saves calculation resources and improves the calculation efficiency.

[0050] S5: Substitute the porosity, background flow field information, particle velocity, and particle volume into the drag force model for calculation to obtain the drag force on the particle;

[0051] Specifically, the Gidaspow drag force model is used. The Gidaspow drag force model calculates the resistance exerted on the particle according to the correlation coefficient of Gidaspow, considering the porosity, background flow field, and particle dynamics, enhancing the physical meaning of drag force calculation.

[0052] S6: Input the drag force into the EDEM software to iteratively calculate the motion of the particle to obtain the position and velocity information of the particle at the next time step;

[0053] Specifically, based on the particle motion equation, the EDEM software simultaneously considers the collision forces between particles-particles and particles-wall surfaces, dynamically couples the drag force and particle motion, improves the physical accuracy of the calculation, and realizes the efficient dynamic simulation of particles.

[0054] S7: Add the negative value of the drag force to the momentum equation of the Fluent software as a source term and perform iterative calculations on the movement of the fluid to obtain the velocity and pressure information of all grids at the next time step.

[0055] Specifically, the calculated drag force source term is dynamically added to the momentum equation of the fluid through the UDF function. At each time step, Fluent will guide the change of the fluid momentum through the source term and update the velocity and pressure fields of the fluid according to the source term. The source term of the momentum equation is dynamically updated, which can reflect the coupling process between particles and fluid in real time and enhance the simulation accuracy.

[0056] As can be seen from the above embodiments, the present invention is based on the unresolved CFD-DEM model to solve the two-phase flow of particulate fluid. When solving large-scale particles around this model, the original calculation methods of porosity and background flow field information cannot completely describe the actual working conditions, resulting in a significant reduction in the calculation accuracy of this model. Therefore, a method for correcting the porosity and background flow field information is proposed. Using the multi-scale particle calculation method proposed by the present invention can not only accurately simulate the movement of small-scale particles in the fluid, but also accurately simulate the movement of large-scale particles, realizing the accurate numerical simulation of multi-scale particles.

[0057] To verify the effectiveness of a multi-scale particle calculation method proposed by the present invention, first, the calculation of large-scale particles is verified by comparing with the single-particle sedimentation experiment. Figure 4 It is a schematic diagram of a single-particle sedimentation model shown according to an exemplary embodiment. The particle is a nylon ball, the medium is silicone oil, the cross-section of the container is 100mm * 100mm, the liquid level height is 160mm, and the particle freely falls from a height of 120mm. Figure 5 It is a comparison diagram of simulation and experimental results using different methods shown according to an exemplary embodiment. It can be seen from the figure that before using the calculation method proposed by the present invention, the error between the particle velocity change results obtained by using the unresolved CFD-DEM method for simulation and the experimental results is relatively large. After using the porosity and background flow field information calculation method proposed by the present invention, the numerical simulation results are very close to the results measured by the experiment, which proves the effectiveness of the present invention for the calculation of large-scale particles.

[0058] Then, the calculation of multi-scale particles is verified by comparing with the binary particle fluidized bed experiment. Figure 6 It is a schematic diagram of a fluidized bed model shown according to an exemplary embodiment. The pipe diameter is 50mm, the medium is water, and two kinds of particles with different particle sizes of 5mm and 8mm are mixed. Figure 7 It is a comparison of multi-scale particle calculation verification results shown according to an exemplary embodiment. It can be seen from the figure that at different inlet velocities, the pressure difference between the inlet and outlet measured by the experiment is very close to the numerical simulation results, which proves the effectiveness of the present invention for the calculation of multi-scale particles.

[0059] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the content disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include well-known knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

[0060] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A multi-scale particle flow calculation method, characterized in that, Including: Obtain the position coordinates, velocity, and volume of particles at the current time step from the EDEM software; Obtain the position distribution information of all grids in the Fluent software, and locate the grid where the particle center is located according to the position coordinates of the particles, obtain the volume value of this grid, and calculate the grid particle size ratio at the current moment by comparing it with the volume of the particles; Calculate the porosity of the grid using two different methods for the grid particle size ratios greater than 3 and less than or equal to 3 respectively; Calculate the background flow field information using two different methods for the grid particle size ratios greater than 3 and less than or equal to 3 respectively; Substitute the porosity, background flow field information, particle velocity, and particle volume into the drag force model for calculation to obtain the drag force on the particle; Input the drag force into the EDEM software to iteratively calculate the movement of the particles, and obtain the position and velocity information of the particles at the next time step; Add the negative value of the drag force to the momentum equation of the Fluent software in the form of a source term to iteratively calculate the movement of the fluid, and obtain the velocity and pressure information of all grids at the next time step; Calculate the porosity of the grid using two different methods for the grid particle size ratios greater than 3 and less than or equal to 3 respectively, including: Calculate the solid volume fraction of the grid within the range of particle action according to the grid particle size ratio. When the grid particle size ratio is greater than 3, disperse the particle volume among multiple uniformly distributed characteristic points, and each characteristic point equally contains a part of the particle volume. When a characteristic point is located in a certain grid, it is considered that the part of the particle volume contained in this characteristic point belongs to this grid; when the grid particle size ratio is less than or equal to 3, weight and distribute the particle volume within the surrounding grids through a statistical kernel function; After traversing all particles, the porosity within the grid is calculated by the following formula: ； where is the solid volume fraction exerted by the i-th particle on the x-th grid in the fluid domain; Calculate the background flow field information using two different methods for the grid particle size ratios greater than 3 and less than or equal to 3 respectively, including: When the grid particle size ratio is greater than 3, use the flow field information of the grid where the particle center is located as the background flow field information of the particle; When the grid particle size ratio is less than or equal to 3, extract the flow field information of the grids where six uniformly distributed characteristic points on the particle surface are located and perform averaging processing as the background flow field information where the particle is located.

2. The multi-scale particle flow calculation method according to claim 1, characterized in that The grid particle size ratio is , where is the volume of the grid where the particle center is located, is the particle volume.

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