GPU parallel two-dimensional particle discontinuous deformation analysis method, device, medium and equipment

Abstract

The present invention provides a GPU parallel two-dimensional particle discontinuous deformation analysis method, device, medium and equipment, belonging to the field of computer program virtual reality simulation technology. The method includes: establishing a two-dimensional particle discretization model to be analyzed, material properties and control parameter information; transmitting the two-dimensional particle discretization model to be analyzed, material properties and control parameter information data to the GPU; performing contact pair retrieval to obtain the two-dimensional particle contact pairs and contact states to be analyzed; assembling a non-contact force stiffness matrix according to the minimum potential energy principle; assembling a contact force stiffness matrix according to the minimum potential energy principle for the contact states of the two-dimensional particle contact pairs to be analyzed; establishing and solving the overall equation; and updating the particle position, velocity, and contact pair state information according to the calculation results. The device, storage medium and equipment can implement the method. It can realize dynamic and static simulation of rock and soil bodies, and has high operating efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of computer program virtual reality simulation, and in particular to a GPU parallel particle discontinuous deformation analysis method, device, medium and equipment. Background Art

[0002] In the field of discontinuous numerical methods, discontinuous deformation analysis (DDA) adopts an implicit format, uses displacement as an unknown, and establishes an equilibrium equation through the principle of minimum potential energy to unify the contact problem between blocks and the deformation of the blocks themselves into the equation. It has the characteristics of rigorous theoretical derivation and high calculation accuracy. The particle-based discontinuous deformation analysis method has unique advantages in simulating sand and rock failure, and has gradually gained attention. For a long time, due to the huge amount of calculation of the two-dimensional particle discontinuous deformation analysis method, its calculation particles are usually from thousands to hundreds of thousands, which greatly limits the calculation speed of the discontinuous deformation analysis method. In recent years, GPU (general graphics processing unit) has relied on its powerful dense matrix data parallel computing capability. In terms of matrix calculation, GPU can provide performance that is dozens or even hundreds of times higher than CPU, which can significantly improve the speed of numerical calculation. Therefore, it is necessary to use a specific method to achieve GPU parallel acceleration of the discontinuous deformation analysis method. Summary of the invention

[0003] In order to overcome the problems of large amount of calculation and low calculation efficiency in two-dimensional particle discontinuous deformation analysis. The purpose of the present invention is to propose a method, device, medium and equipment suitable for GPU parallel two-dimensional particle discontinuous deformation analysis. Vectorized programming is used to transfer vectorized matrixed calculation data to the GPU to achieve GPU parallel calculation. All calculations of this method can be performed on the GPU and realized through GPU matrix operations, which can effectively improve the calculation efficiency of the two-dimensional particle discontinuous deformation analysis method, realize large-scale particle simulation, and realize efficient simulation of complex geotechnical engineering problems. In addition, the operation efficiency is high, so it is more suitable for practical use.

[0004] In order to achieve the first objective above, the technical solution of the GPU parallel particle discontinuous deformation analysis method provided by the present invention is as follows:

[0005] The GPU parallel particle discontinuous deformation analysis method provided by the present invention comprises the following steps:

[0006] S1. Establish the discretization model data of the particles to be analyzed, including the particle position information matrix: the abscissa matrix X, the ordinate matrix Y, the radius information matrix R, the material property information matrix MAT and the control parameter information, and number the particles from 1-Nump, where Nump is the number of particles;

[0007] S2, transmitting the discretization model data information of the particles to be analyzed to the GPU;

[0008] S3, performing contact search to obtain the contact pairs and contact states of the particles to be analyzed;

[0009] S4, assemble the non-contact force stiffness matrix and load matrix by the minimum potential energy principle;

[0010] S5, assembling a contact force stiffness matrix and a load matrix according to the contact state of the particle contact pair to be analyzed by using the minimum potential energy principle;

[0011] S6, taking the displacement of the particle as an unknown quantity, combining it with the stiffness matrix and the load matrix, establishing an overall equation, and solving the overall equation to obtain the current displacement of the particle;

[0012] S7, updating the particle contact pair matrix contact state matrix according to the particle displacement obtained by solving the equation;

[0013] S8, judging whether the contact state has converged. If not, repeating S5-S7 for a set number of times. If it still does not converge after repeating the set number of times, shortening the calculation time step and then executing S5-S7;

[0014] S8. Update the particle position, force, speed, and contact related information according to the calculation results;

[0015] S9. Execute S3-S8 cyclically to realize the GPU parallel numerical simulation of particle discontinuous deformation analysis.

[0016] The GPU parallel particle discontinuous deformation analysis method provided by the present invention can also be further implemented by adopting the following technical measures.

[0017] Preferably, in the process of transmitting the discretization model data information of the particles to be analyzed to the GPU, the discretization model data information of the particles to be analyzed is written into the GPU using the MATLAB built-in function gpuArray.

[0018] Preferably, the contact search adopts a buffer strategy, and determines whether contact search needs to be performed again by setting a larger contact distance threshold. The displacement of the discretized particles obtained by solving the overall balance equation each time is accumulated. If the maximum accumulated displacement of each particle within a number of time steps does not exceed a given threshold, then these contact pairs will be maintained for a number of time steps. If the maximum accumulated displacement of the particle within a number of time steps exceeds a given threshold, a new contact search is performed.

[0019] Preferably, in the contact search process, the contact search adopts a GPU parallel acceleration algorithm, including the following steps:

[0020] (1) dividing the background grid according to the radius of the discretized particle, and projecting each of the discretized particles into the background grid according to the center of the discretized particle, and assigning an independent number to the grid number;

[0021] (2) Retrieving the discretized particles in eight grids surrounding the background grid where each discretized particle is located;

[0022] (3) Calculate the distance O between each discretized particle and the center of the discretized particles in the eight background grids surrounding the background grid where it is located. i O j , if the distance between the center of the circle and i O j Less than the sum of the two particle radii R i +R j Add a preset threshold d gap , that is, O i O j <R i +R j +d gap , then the two particles form a contact pair. The subscripts i and j are only used to distinguish two different particles.

[0023] Preferably, the particle contact pair is virtually connected through one, two or three of a virtual normal spring, a virtual tangential spring and a virtual rotation spring; there are four independent contact states, namely, open, sliding, locked and bonded, and the contact state of the contact pair is updated by adding or deleting virtual normal springs, virtual tangential springs and virtual rotation springs.

[0024] Preferably, in assembling the stiffness matrix and the load matrix, the assembly of the stiffness matrix and the load matrix adopts a GPU parallel algorithm, including the following steps:

[0025] (1) Find the contact pairs that need to add or delete virtual springs;

[0026] (2) For each of the above contact pairs:

[0027] Calculate its related row index matrix KrowInd and column index matrix KcolInd in the stiffness matrix K, and its related row index matrix FrowInd and column index matrix FcolInd in the load matrix F;

[0028] Calculate the stiffness value Kval and the load value Fval according to the addition and deletion of virtual spring types;

[0029] (3) Use the built-in MATLAB sparse function to add KrowInd, KcolInd, and Kval to the stiffness matrix, and use the built-in MATLAB accumarray function to add FrowInd, FcolInd, and Fval to the load matrix.

[0030] Preferably, the overall equilibrium equation is solved using a built-in iteration function of MATLAB.

[0031] In order to achieve the above second purpose, the technical solution of the GPU parallel particle discontinuous deformation analysis method provided by the present invention is as follows:

[0032] The GPU parallel particle discontinuous deformation analysis device provided by the present invention comprises:

[0033] Discretization model acquisition module, used to obtain the discretization model of the particles to be analyzed, including the position information of the discrete particles: the abscissa matrix X, the ordinate matrix Y, the radius information matrix R, the material attribute information matrix MAT and the control parameter information;

[0034] A data transmission module is used to transmit the calculation data to the GPU for calculation;

[0035] A contact pair retrieval module is used to perform contact pair retrieval under the condition of the discretization model of the particle to be analyzed, and obtain the contact pair and contact state of the particle to be analyzed;

[0036] A discontinuous deformation analysis calculation module is used to perform discontinuous deformation analysis calculation according to the current contact state of the particle contact pair to be analyzed to obtain a calculation result;

[0037] The post-processing module is used to perform post-processing analysis according to the calculation results to obtain the analysis conclusion of the discontinuous deformation analysis of the particles to be analyzed.

[0038] In order to achieve the third objective above, the technical solution of the computer-readable storage medium provided by the present invention is as follows:

[0039] The computer-readable storage medium provided by the present invention stores a particle discontinuous deformation analysis program, and when the particle discontinuous deformation analysis program is executed by a processor, the steps of the two-dimensional particle discontinuous deformation analysis method provided by the present invention are implemented.

[0040] In order to achieve the fourth objective, the technical solution of the electronic device provided by the present invention is as follows:

[0041] The electronic device provided by the present invention comprises a memory and a processor, wherein a GPU parallel particle discontinuous deformation analysis program is stored in the memory, and when the GPU parallel particle discontinuous deformation analysis program is executed by the processor, the steps of the GPU parallel particle discontinuous deformation analysis method provided by the present invention are implemented.

[0042] The GPU parallel two-dimensional particle discontinuous deformation analysis method, device, medium and equipment provided by the present invention use vectorized programming to store and calculate the discretized model data information in the form of vector or matrix data. It makes up for the low operating efficiency of the traditional two-dimensional particle discontinuous deformation analysis program. In addition, the vectorized matrix data is written into the GPU using the MATLAB built-in function gpuArray, and the GPU is mobilized to execute the program. Compared with the traditional two-dimensional particle discontinuous deformation analysis method, the two-dimensional particle discontinuous deformation analysis method executed on the GPU has significant improvements in contact retrieval, matrix assembly and overall equation solution speed, and significantly improves the calculation speed and number of calculations of two-dimensional particle discontinuous deformation analysis simulation. The invention can efficiently perform discontinuous deformation analysis calculations of hundreds of thousands of two-dimensional particles, and can realize dynamic and static simulations of large-scale rock and soil bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 A flowchart of the steps of the GPU parallel particle discontinuous deformation analysis method provided by an embodiment of the present invention;

[0045] Figure 2a A schematic diagram of a virtual spring involved in a GPU parallel particle discontinuous deformation analysis method provided in an embodiment of the present invention (wherein the virtual spring is a virtual normal spring);

[0046] Figure 2b A schematic diagram of a virtual spring involved in a GPU parallel particle discontinuous deformation analysis method provided in an embodiment of the present invention (wherein the virtual spring is a virtual tangential spring);

[0047] Figure 2c A schematic diagram of a virtual spring involved in a GPU parallel particle discontinuous deformation analysis method provided in an embodiment of the present invention (wherein the virtual spring is a virtual rotation spring);

[0048] Figure 3 A schematic diagram of contact retrieval involved in a GPU parallel particle discontinuous deformation analysis method provided in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the signal flow relationship between the functional modules in the GPU parallel particle discontinuous deformation analysis device provided by an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of a GPU parallel particle discontinuous deformation analysis device in the hardware operating environment involved in an embodiment of the invention. DETAILED DESCRIPTION

[0051] In view of this, the present invention provides a two-dimensional particle discontinuous deformation analysis method, device, storage medium and electronic device, which can realize dynamic and static simulation of rock and soil bodies, and has high operating efficiency, so it is more suitable for practical use.

[0052] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a two-dimensional particle discontinuous deformation analysis method, device, storage medium and electronic device proposed by the present invention, its specific implementation, structure, characteristics and effects as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0053] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B. Specifically, it is understood that: A and B may be included at the same time, A may exist alone, or B may exist alone, and any of the above three situations may be met.

[0054] GPU parallel particle discontinuous deformation analysis method embodiment

[0055] See attached Figure 1 , Figure 2, attached Figure 3 The GPU parallel particle discontinuous deformation analysis method provided by the embodiment of the present invention comprises the following steps:

[0056] 1. Establish the discretization model data of the particles to be analyzed, including the particle position information matrix: abscissa matrix X, ordinate matrix Y, radius information matrix R, material attribute information matrix MAT and control parameter information, and number the particles from 1-Nump, where Nump is the number of particles;

[0057] 2. The discretized model data information of the particles to be analyzed is transmitted to the GPU; using the built-in function gpuArray in MATLAB, X=gpuArray(X), the particle horizontal coordinate matrix X can be converted into a data type for GPU execution. Other information matrices are transmitted to the GPU through this method.

[0058] 3. Perform contact search to obtain the contact pairs and contact states of the particles to be analyzed. Specifically, the contact search uses a GPU parallel acceleration algorithm. The contact search steps are as follows:

[0059] (1) Divide the background grid according to the radius of the discretized particle, the size dL of the background grid is determined by the minimum particle radius, and project each discretized particle into the background grid according to the center of the discretized particle, and assign an independent grid number.

[0060] (2) Retrieve the discretized particles in the background grid where each discretized particle is located and the eight grids surrounding it. For particle 4, it is located in grid 12, so it is necessary to retrieve the particles located in grids 6, 7, 8, 11, 12, 13, 16, 17, and 18; and obtain particles 1, 2, 3, and 8 to be analyzed.

[0061] (3) Calculate the distance O between each discretized particle and the center of the background grid where it is located and the discretized particles in the eight surrounding background grids. i O j , if the distance between the center of the circle and i O j Less than the sum of the two particle radii R i +R j Add a preset threshold d gap , that is, O i O j <R i +R j +d gap , then the two particles form a contact pair. The subscripts i and j are only used to distinguish two different particles.

[0062] 4. Specifically, the contact search adopts a buffer strategy, and determines whether contact search needs to be performed again by setting a larger contact distance threshold. The displacement of the discretized particles obtained by solving the overall balance equation each time is accumulated. If the maximum accumulated displacement of each particle within a number of time steps does not exceed a given threshold, then these contact pairs will be maintained for a number of time steps. If the maximum accumulated displacement of the particle within a number of time steps exceeds a given threshold, a new contact search is performed.

[0063] 5. Assemble the non-contact force stiffness matrix and load matrix based on the minimum potential energy principle; non-contact force includes inertia force, velocity constraint, and damping force. Its calculation method is based on the basic calculation method of discontinuous deformation.

[0064] 6. According to the contact state of the particle contact pair to be analyzed, the contact force stiffness matrix and the load matrix are assembled by the minimum potential energy principle; the contact force includes a virtual normal spring contact force, a virtual tangential spring contact force, and a virtual rotational spring contact force, and the calculation method thereof is based on the basic calculation method of discontinuous deformation.

[0065] 7. Specifically, in assembling the stiffness matrix and the load matrix, the assembly of the stiffness matrix and the load matrix adopts a GPU parallel algorithm, including the following steps:

[0066] (1) Find the contact pairs that need to add or delete virtual springs;

[0067] (2) For each of the above contact pairs:

[0068] Calculate the relevant row index value KrowInd and column index value KcolInd in the overall stiffness matrix K, and the relevant row index value FrowInd and column index value FcolInd in the overall load matrix F; wherein, KrowInd, KcolInd, FrowInd, and FcolInd are calculated by the position of the contact pair matrix where the contact pair to which the virtual spring needs to be added or deleted is located.

[0069] Calculate the stiffness value Kval and the load value Fval according to the type of virtual springs added or deleted. Adding a virtual spring takes a positive value, while deleting a virtual spring takes a negative value.

[0070] (3) Use the built-in MATLAB sparse function to add KrowInd, KcolInd, and Kval to the stiffness matrix D. The specific command is as follows: D = D + sparse (KrowInd, KcolInd, Kval). Use the built-in MATLAB accumarray function to add FrowInd, FcolInd, and Fval to the load matrix F. The specific command is as follows: F = F + accumarray (FrowInd, Fval) + accumarray (FcolInd, Fval).

[0071] 8. The displacement of the particle: lateral displacement x, longitudinal displacement y, rotation angle θ, as the unknown quantity matrix D = (x, y, θ), is combined with the stiffness matrix K and the load matrix F to establish the overall equation KD = F, and the overall equation is solved to obtain the current displacement of the particle;

[0072] 9. Update the contact state of the particle contact pair according to the particle displacement obtained by solving the equation;

[0073] 10. Specifically, the particle contact pair is virtually connected through one, two or three of a virtual normal spring, a virtual tangential spring and a virtual rotation spring; there are four independent contact states, namely, open, sliding, locked and bonded. The contact state of the contact pair is updated by adding or deleting a virtual normal spring, a virtual tangential spring and a virtual rotation spring;

[0074] Furthermore, the change of contact state due to particle contact is judged using traditional discontinuous deformation analysis method.

[0075] 11. Determine whether the contact state converges. If not, repeat 6-9 for a set number of times. If it still does not converge after repeating the set number of times, shorten the calculation time step and then execute 6-9;

[0076] 12. Update the particle position, force, speed, and contact-related information according to the calculation results;

[0077] 13. Execute steps 3-12 in a loop to realize GPU parallel numerical simulation of discontinuous deformation analysis of particles.

[0078] Embodiment of two-dimensional particle discontinuous deformation analysis device

[0079] See attached Figure 4 The two-dimensional particle discontinuous deformation analysis device provided by the embodiment of the present invention comprises:

[0080] The discretization model acquisition module is used to obtain the discretization model of the particles to be analyzed, including the position information of the discrete particles: the horizontal coordinate matrix X, the vertical coordinate matrix Y, the radius information matrix R, the material attribute information matrix MAT and the control parameter information.

[0081] A data transmission module is used to transmit the calculation data to the GPU for calculation;

[0082] A contact pair retrieval module is used to perform contact pair retrieval under the condition of the discretization model of the particle to be analyzed, and obtain the contact pair and contact state of the particle to be analyzed;

[0083] A discontinuous deformation analysis calculation module is used to perform discontinuous deformation analysis calculation according to the current contact state of the particle contact pair to be analyzed to obtain a calculation result;

[0084] The post-processing module is used to perform post-processing analysis according to the calculation results to obtain the analysis conclusion of the discontinuous deformation analysis of the particles to be analyzed.

[0085] Computer Readable Storage Medium Embodiments

[0086] The computer-readable storage medium provided in the embodiment of the present invention stores a GPU parallel particle discontinuous deformation analysis program. When the GPU parallel particle discontinuous deformation analysis program is executed by a processor, the steps of the GPU parallel particle discontinuous deformation analysis method provided in the present invention are implemented.

[0087] Electronic device embodiment

[0088] The electronic device provided by an embodiment of the present invention includes a memory and a processor. The memory stores a GPU parallel particle discontinuous deformation analysis program. When the two-dimensional particle discontinuous deformation analysis program is executed by the processor, the steps of the GPU parallel particle discontinuous deformation analysis method provided by the present invention are implemented.

[0089] Reference Figure 5 , Figure 5 It is a schematic diagram of the structure of a GPU parallel particle discontinuous deformation analysis device in the hardware operating environment involved in an embodiment of the present invention.

[0090] like Figure 5 As shown, the GPU parallel particle discontinuous deformation analysis device may include: a processor 1001, such as a central processing unit (CPU), a graphics processing unit (GPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0091] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the GPU parallel particle discontinuous deformation analysis device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0092] like Figure 5As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a GPU parallel particle discontinuous deformation analysis program.

[0093] exist Figure 5 In the two-dimensional particle discontinuous deformation analysis device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the two-dimensional particle discontinuous deformation analysis device of the present invention can be set in the two-dimensional particle discontinuous deformation analysis device, and the GPU parallel particle discontinuous deformation analysis device calls the two-dimensional particle discontinuous deformation analysis program stored in the memory 1005 through the processor 1001, and executes the GPU parallel particle discontinuous deformation analysis method provided by the embodiment of the present invention.

[0094] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0095] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A GPU parallel particle discontinuous deformation analysis method, characterized in that: The following steps are involved: S1. Establish the discretization model data of the particles to be analyzed, including the particle position information matrix: the abscissa matrix X, the ordinate matrix Y, the radius information matrix R, the material property information matrix MAT and the control parameter information, and number the particles from 1-Nump, where Nump is the number of particles; S2, transmitting the discretization model data information of the particles to be analyzed to the GPU, wherein the discretization model data information of the particles to be analyzed is written into the GPU using the MATLAB built-in function gpuArray; S3, performing contact search to obtain the contact pairs of the particles to be analyzed and the contact states of the contact pairs of the particles to be analyzed; S4, assemble the non-contact force stiffness matrix and load matrix by the minimum potential energy principle; S5, assembling a contact force stiffness matrix and a load matrix according to the contact state of the particle contact pair to be analyzed by using the minimum potential energy principle; S6, taking the displacement of the particle as an unknown quantity, combining it with the stiffness matrix and the load matrix, establishing an overall equation, and solving the overall equation to obtain the current displacement of the particle; S7, updating the contact state of the particle contact pair according to the particle displacement obtained by solving the equation; S8, judging whether the contact state converges, if not convergent, repeating S5-S7 for a set number of times, if it still does not converge after repeating the set number of times, shortening the calculation time step and then executing S5-S7; S8. Update the particle position, force, speed, and contact related information according to the calculation results; S9. Execute S3-S8 cyclically to realize the GPU parallel numerical simulation of particle discontinuous deformation analysis.

2. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The particle discretization model data information to be analyzed is written into the GPU using the MATLAB built-in function gpuArray.

3. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The contact search adopts a buffer strategy, which determines whether contact search is needed again by setting a larger contact distance threshold. The displacement of the discretized particles obtained by solving the overall equation each time is accumulated. If the maximum accumulated displacement of each particle within several time steps does not exceed a given threshold, then these contact pairs will be maintained for several time steps. If the maximum accumulated displacement of the particle within several time steps exceeds a given threshold, a new contact search is performed.

4. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The contact pair retrieval adopts a GPU parallel acceleration algorithm, including the following steps: (1) Dividing the background grid according to the radius of the discretized particle, and projecting each of the discretized particles into the background grid according to the center of the discretized particle, and assigning an independent number to the grid number; (2) Retrieving the discretized particles in the background grid where each discretized particle is located and the eight grids surrounding it; (3) Calculate the distance O between each discretized particle and the center of the background grid where it is located and the discretized particles in the eight surrounding background grids. i O j , if the distance between the center of the circle and i O j Less than the sum of the two particle radii R i +R j Add a preset threshold d gap , that is, O i O j <R i +R j +d gap , then the two particles form a contact pair, where the subscripts i and j are only used to distinguish two different particles.

5. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The particle contact pair is virtually connected through one, two or three of a virtual normal spring, a virtual tangential spring and a virtual rotation spring; there are four independent contact states, namely, open, sliding, locked and bonded, and the contact state of the contact pair is updated by adding or deleting virtual normal springs, virtual tangential springs and virtual rotation springs.

6. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The assembly stiffness matrix uses a GPU parallel algorithm, which includes the following steps: (1) Find the contact pairs that need to add or delete virtual springs; (2) For each of the above contact pairs: Calculate its related row index matrix KrowInd and column index matrix KcolInd in the stiffness matrix K, and its related row index matrix FrowInd and column index matrix FcolInd in the load matrix F; Calculate the stiffness value Kval and the load value Fval according to the addition and deletion of virtual spring types; (3) Use the built-in MATLAB sparse function to add KrowInd, KcolInd, and Kval to the stiffness matrix, and use the built-in MATLAB accumarray function to add FrowInd, FcolInd, and Fval to the load matrix.

7. The GPU parallel particle discontinuous deformation analysis method according to claim 1, characterized in that: The overall equation is solved using MATLAB built-in iteration function.

8. A GPU parallel particle discontinuous deformation analysis device according to any one of the methods of claims 1 to 7, characterized in that: include: Discretization model acquisition module, used to obtain the discretization model of the particles to be analyzed, including the position information of the discrete particles: the abscissa matrix X, the ordinate matrix Y, the radius information matrix R, the material attribute information matrix MAT and the control parameter information; A data transmission module is used to transmit the calculation data to the GPU for calculation; A contact pair retrieval module is used to perform contact pair retrieval under the condition of the discretization model of the particle to be analyzed, and obtain the contact pair and contact state of the particle to be analyzed; A discontinuous deformation analysis calculation module, used to perform calculations according to the current contact state of the particle contact pair to be analyzed to obtain calculation results; The post-processing module is used to perform post-processing analysis according to the calculation results to obtain the analysis conclusion of the discontinuous deformation analysis of the particles to be analyzed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a particle discontinuous deformation analysis program, and when the particle discontinuous deformation analysis program is executed by the processor, the steps of the GPU parallel particle discontinuous deformation analysis method described in any one of claims 1-7 are implemented.

10. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein a GPU parallel particle discontinuous deformation analysis program is stored in the memory, and when the GPU parallel particle discontinuous deformation analysis program is executed by the processor, the steps of the GPU parallel particle discontinuous deformation analysis method described in any one of claims 1 to 7 are implemented.

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