A numerical simulation method and device for particle collision retrieval in dense gas-solid flow based on GPU parallel computing

Abstract

The application discloses a numerical simulation method and device for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel calculation, realizes large-scale parallel solution of a neighborhood particle collision retrieval algorithm in dense-phase gas-solid flow numerical simulation research on a computer graphics processing unit (GPU), realizes a parallel prefix sum algorithm in combination with CUDA programming and OpenACC instructions, writes particle collision retrieval results into an array, establishes a particle collision list, obtains dense-phase particle collision retrieval results, and is used for calculating collision forces between particles. The application overcomes the deficiency that a great amount of calculation is consumed in particle neighborhood collision retrieval by a traditional discrete element method, and fills the blank of the discrete element method in efficient processing of large-scale particle collision information.

Description

Technical Field

[0001] This invention relates to the field of computational fluid dynamics, and more particularly to a numerical simulation method and apparatus for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing. Background Technology

[0002] Fluidization refers to the phenomenon where solid particles exhibit fluid-like flow properties under the influence of a fluid. It offers advantages such as wide fuel adaptability, high gas-solid contact efficiency, and good operational stability. Therefore, the clean and efficient utilization of solid fuels through fluidization is widely applied in industries such as energy, chemical engineering, metallurgy, and environmental protection. The essential characteristic of the fluidization process is the multi-scale coupling of particle size, particle cluster size, and reactor size, as well as the multi-physical coupling within and between different scales. This directly determines the safe, efficient, and stable operation of the system and presents significant challenges to fluidization research.

[0003] On the one hand, with the development of modern measurement technology, key parameters such as pressure drop and composition within a fluidized bed can be obtained through experimental measurements to guide reactor optimization design. However, experimental measurements under high temperature and high pressure conditions are extremely difficult, and limited data is confined to typical operating conditions. On the other hand, the Euler-Euler two-fluid method (TFM) based on the pseudo-fluid hypothesis, using Reynolds-averaged and empirical / semi-empirical simplified models, struggles to accurately analyze intraphase / interphase coupling effects. The particle element volume method (MP-PIC) introduces the concept of computational particles and uses solid-state force modeling of particle collisions, failing to accurately obtain particle-scale information. Computational fluid dynamics-discrete element method (CFD-DEM) can track every particle in the system and obtain particle-scale information, but its computational demands for particle collision retrieval limit its application in the fluidization field. In recent years, computer graphics processing units (GPUs), due to their excellent multi-threaded parallel characteristics, have shown great application potential in scientific computing, and their stream processors have unique advantages for conducting large-scale particle collision retrieval calculations.

[0004] Therefore, conducting research on the integration of Discrete Element Method (DEM) and GPU technology, and developing a numerical simulation method for particle collision retrieval in dense phase gas-solid flow in fluidized beds based on GPU parallel computing, can overcome the bottleneck of traditional discrete element method in terms of computing resources and enable its application in numerical simulation research in the field of fluidization. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies. This invention provides a numerical simulation method and apparatus for particle collision retrieval in dense phase gas-solid flow in a fluidized bed based on GPU parallel computing. It solves the problem that traditional discrete element methods require a huge amount of computation for particle collision retrieval. By utilizing the multi-threaded parallel characteristics of GPUs, the DEM method is combined with GPU technology.

[0006] The objective of this invention is achieved through the following technical solution: This invention proposes a numerical simulation method for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing. The steps of this method are as follows:

[0007] Step 1: Construct a three-dimensional model of the simulated object based on its geometric parameters, mesh it, generate a structured mesh, and initialize the solid particle information according to the initial conditions of the numerical simulation.

[0008] Step 2: Set the memory for the two variables of solid particle velocity and position on the CPU as page-locked memory, and copy the initial velocity and position information of solid particle from the CPU to the GPU through the data exchange pipeline using direct memory access technology.

[0009] Step 3: Establish solid particle control equations based on the Lagrange framework on the GPU, specifically including the translational and rotational control equations of the particles;

[0010] Step 4: Map the solid particles to a structured mesh based on their current center position information, and further determine the specific position of the particles in the mesh;

[0011] Step 5: On the GPU, traverse the grid containing the solid particles and all solid particles in adjacent grids in parallel, perform solid particle collision detection, implement the parallel prefix sum algorithm by combining CUDA programming and OpenACC instructions, write the solid particle collision retrieval results into an array, and build a solid particle collision list.

[0012] Step 6: Based on the solid particle collision list obtained in Step 5, calculate the collision force on the solid particles on the GPU according to the soft ball collision model, and obtain the velocity, rotation speed and position information of the solid particles at the current moment based on the solid particle control equation according to the gravity and collision force on the solid particles.

[0013] Step 7: Repeat steps 4 through 6 within each calculation time step until the calculation ends at the termination time.

[0014] Furthermore, in step one, the size of the structured mesh is 3-5 times the diameter of the solid particles.

[0015] Furthermore, in step four, each structured grid is assumed to consist of eight identical sub-grids, which are used to further determine the specific location information of the particles in the grid.

[0016] Furthermore, in step five, when the center distance between two particles is less than or equal to 1.2 times the sum of the radii of the two particles, it is recorded as a particle collision pair and added to the solid-phase particle collision list.

[0017] Furthermore, a two-dimensional GPU thread block (x, y) is used to traverse the solid particles in the mesh. Each GPU thread in the x direction corresponds to each solid particle, and each GPU thread in the y direction corresponds to all solid particles in the mesh adjacent to the solid particle.

[0018] Furthermore, in step six, the calculation of the force, velocity, and position update of each solid particle are completed by a separate GPU thread.

[0019] Secondly, the present invention also provides a numerical simulation device for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, including a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing.

[0020] Thirdly, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing.

[0021] The beneficial effects of this invention are as follows: This invention addresses the issue of the enormous computational demands required for neighboring particle collision retrieval in traditional discrete element method (DIEM) numerical simulations of dense-phase granular flows. By establishing a GPU-based parallel computing-driven neighboring particle collision retrieval algorithm, leveraging the multi-threaded parallelism and high computational throughput of GPUs, a high-performance dense-phase particle neighboring collision retrieval algorithm based on GPU parallel computing is achieved, significantly improving computational efficiency and saving computational resources. This invention overcomes the shortcomings of traditional DIEM methods in consuming enormous computational resources for particle neighboring collision retrieval, filling the gap in the efficient processing of large-scale particle collision information using the DIEM method. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1This is a flowchart illustrating a numerical simulation method for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing.

[0024] Figure 2 This is a schematic diagram of the mapping method between particles and structured meshes.

[0025] Figure 3 This is a schematic diagram of the upward traversal method in the parallel prefix sum algorithm.

[0026] Figure 4 This is a schematic diagram of the downward traversal method in the parallel prefix sum algorithm.

[0027] Figure 5 This is a schematic diagram illustrating the acceleration effect of a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing.

[0028] Figure 6 This is a schematic diagram of a numerical simulation device for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing. Detailed Implementation

[0029] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] This invention proposes a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing. The method implements large-scale parallel solution of the neighborhood particle collision retrieval algorithm on a computer graphics processor (GPU), and implements a parallel prefix sum algorithm by combining CUDA programming and OpenACC instructions. The particle collision retrieval results are written into an array to establish a particle collision list and obtain dense phase particle collision retrieval results, which are used to calculate the collision force between particles.

[0031] The following numerical simulation method for particle collision retrieval in dense-phase gas-solid flow, based on GPU parallel computing, is applied to simulate the particle collision retrieval process in a bubbling fluidized bed. A flowchart of the calculation is shown below. Figure 1 As shown, the specific steps are as follows:

[0032] (1) Construct a three-dimensional model of the simulation object based on the geometric parameters of the bubbling fluidized bed, and generate a structured mesh by mesh generation. The mesh size is 3-5 times the particle diameter. Initialize the solid phase particle information according to the initial conditions of the numerical simulation (particle diameter and total number of particles, etc.);

[0033] (2) Set the memory for the particle's velocity and position variables on the CPU as page-locked memory, and copy the initial velocity and position information of the particle from the CPU to the GPU through the data exchange pipeline using direct memory access technology.

[0034] (3) Establish solid-phase particle control equations based on the Lagrangian framework on the GPU, specifically including the translational and rotational control equations of the particles, namely:

[0035]

[0036]

[0037] Where m i Let v be the mass of particle i. i Let F be the velocity of particle i. c,i The impact force experienced by the particle; I i Let ω be the moment of inertia of particle i. i Let L be the rotational velocity of the particle, L be the center distance between particles i and j, N be the total number of particles colliding with the current particle, n be a unit vector, and F be the rotational velocity of the particle. ct,ij Let be the tangential component of the collision force between particle i and particle j;

[0038] (4) Map the solid particles to a structured grid (I, J, K) based on their current center position information. Each structured grid can be assumed to consist of 8 identical subgrids, such as... Figure 2 As shown; therefore, the position of the particle in the grid can be further located to the subgrid (I). b J b ,K b )middle;

[0039] (5) On the GPU, perform parallel traversal of all particles in the eight adjacent grids (I,J,K) where the particle is located (I,J,K), (I,J+1,K), (I+1,J+1,K), (I,J,K+1), (I+1,J,K+1), (I,J+1,K+1), (I,J+1,K+1), and (I+1,J+1,K+1). Particle collision detection is performed based on the center distance between two particles. A parallel prefix sum algorithm is implemented using CUDA programming and OpenACC instructions. The particle collision search results are written into an array to create a particle collision list. Furthermore, the parallel prefix sum algorithm, based on the "balanced tree" concept, can be further divided into upward traversal algorithms, such as... Figure 3 As shown, traversing from the "leaf node" to the "root node", partial sums are calculated at the internal nodes of the tree, and finally the sum of all nodes in the array can be obtained at the "root node"; the downward traversal algorithm is as follows: Figure 4 As shown, taking the result of the upward traversal algorithm as input, the "root node" is first assigned the value 0 at the beginning of the calculation, and then the "root node" is traversed to the "leaf node", and finally the prefix sum of the "leaf node" is calculated.

[0040] (6) Based on the particle collision list established in step five, calculate the collision force F on the solid particles on the GPU using the soft sphere collision model. c,i Based on the gravity and collision force of the solid particles, the velocity, rotation speed and position information of the solid particles at the current moment are obtained according to the particle control equations (1) and (2).

[0041] (7) Repeat steps 4 to 6 within each calculation time step until the calculation ends at the termination time.

[0042] This invention proposes a numerical simulation method for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing. It implements a large-scale parallel solution for the neighborhood particle collision retrieval algorithm on a computer graphics processing unit (GPU), combining CUDA programming and OpenACC instructions to implement a parallel prefix sum algorithm. The particle collision retrieval results are written into an array to establish a particle collision list, obtaining the dense-phase particle collision retrieval results for calculating the collision forces between particles. Numerical simulation studies of the dense-phase particle collision process in a bubbling fluidized bed verify that the method of this invention can accurately simulate the collision retrieval process of dense-phase particles in a bubbling fluidized bed. Tests show that the computation time of this method on NVIDIA P100 GPUs and A100 GPUs is significantly reduced compared to traditional CPU-based discrete unit methods. Figure 5 As shown. Therefore, the method proposed in this invention can significantly improve computational efficiency.

[0043] Corresponding to the aforementioned embodiment of a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, the present invention also provides an embodiment of a numerical simulation device for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing.

[0044] See Figure 6 The present invention provides a numerical simulation device for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in the above embodiment.

[0045] The numerical simulation device for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing provided by this invention can be applied to any device with data processing capabilities, such as a computer. The device embodiment can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which is part of a numerical simulation device for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing provided by the present invention. (Except for...) Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0046] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0047] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0048] This invention also provides a computer-readable storage medium storing a program that, when executed by a processor, implements a numerical simulation method for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing, as described in the above embodiments.

[0049] The computer-readable storage medium can be an internal storage unit of any data processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of any data processing device. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0050] The above embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A numerical simulation method for particle collision retrieval in dense-phase gas-solid flow based on GPU parallel computing, characterized in that, The steps of this method are as follows: Step 1: Construct a three-dimensional model of the simulated object based on its geometric parameters, mesh it, generate a structured mesh, and initialize the solid particle information according to the initial conditions of the numerical simulation. Step 2: Set the memory for the two variables of solid particle velocity and position on the CPU as page-locked memory, and copy the initial velocity and position information of solid particle from the CPU to the GPU through the data exchange pipeline using direct memory access technology. Step 3: Establish solid particle control equations based on the Lagrange framework on the GPU, specifically including the translational and rotational control equations of the particles; Step 4: Map the solid particles to a structured mesh based on their current center position information, and further determine the specific position of the particles in the mesh; Step 5: On the GPU, traverse the grid containing the solid particles and all solid particles in adjacent grids in parallel, and perform solid particle collision judgment. That is, when the sum of the radii of two particles is less than the distance between the center of the particles, the particles are considered to have collided. Combine CUDA programming and OpenACC instructions to implement the parallel prefix sum algorithm, write the solid particle collision retrieval results into an array, and establish a solid particle collision list. Step 6: Based on the solid particle collision list obtained in Step 5, calculate the collision force on the solid particles on the GPU according to the soft ball collision model, and obtain the velocity, rotation speed and position information of the solid particles at the current moment based on the solid particle control equation according to the gravity and collision force on the solid particles. Step 7: Repeat steps 4 through 6 within each calculation time step until the calculation ends at the termination time.

2. The numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in claim 1, characterized in that, In step one, the size of the structured mesh is 3-5 times the diameter of the solid particles.

3. The numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in claim 1, characterized in that, In step four, each structured grid is assumed to consist of eight identical subgrids, which are used to further determine the specific location information of the particles in the grid.

4. The numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in claim 1, characterized in that, In step five, if the center distance between two particles is less than or equal to 1.2 times the sum of the radii of the two particles, it is recorded as a particle collision pair and added to the solid-phase particle collision list.

5. The numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in claim 1, characterized in that, Two-dimensional GPU thread blocks (x, y) are used to traverse the solid particles in the mesh. Each GPU thread in the x direction corresponds to each solid particle, and each GPU thread in the y direction corresponds to all solid particles in the mesh adjacent to the solid particle.

6. The numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing as described in claim 1, characterized in that, In step six, the calculation of the force, velocity, and position update of each solid particle are completed by a separate GPU thread.

7. A numerical simulation apparatus for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, as described in any one of claims 1-6.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements a numerical simulation method for particle collision retrieval in dense phase gas-solid flow based on GPU parallel computing, as described in any one of claims 1-6.

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