Information processing device, information processing method, and information processing program

Abstract

An information processing apparatus according to the present disclosure includes: an acquisition unit that acquires spatial information indicating a mesh model of a first particle and a continuum, the first particle being a particle arranged in a space and having a physical quantity; and a simulation unit performing a simulation including: a projection process of projecting a physical quantity of a spatial grid to a second particle, the spatial grid being a grid arranged in the space, the physical quantity of the first particle being projected to the grid, and the physical quantity of the second particle being projected to the grid; the second particles are particles arranged at positions corresponding to nodes of the mesh model in the space; an update process for updating the position of the second particle on the basis of the physical quantity projected onto the second particle; and a generation process of generating the mesh model based on the updated position of the second particle.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and an information processing program. BACKGROUND

[0002] A technique of generating a mesh model of an object in, for example, computer graphics is provided. For example, a simulation device is provided which arranges particles at nodes of a mesh of a mesh model and develops a state of the particles over time, changes a time interval according to a distance between the particles (for example, Patent Literature 1).

[0003] LIST OF CITATIONS

[0004] PATENT LITERATURE

[0005] Patent Literature 1: JP 2020-095400 A SUMMARY

[0006] PROBLEMS

[0007] According to the conventional technique, mesh division is performed on an object to be simulated based on shape definition data and mesh division conditions.

[0008] However, in the conventional technique, although the mesh can be divided according to the conditions and the like, there is room for improvement in analysis processing such as calculation of positions of particles arranged at nodes of the mesh. For example, in the conventional technique, it is necessary to impart a mass to each particle corresponding to a node of the mesh and determine an interaction potential between the particles. For example, when the mass and the interaction potential of each particle are not correctly reflected on a state of the object, it can be difficult to generate an appropriate mesh model. Therefore, it is desirable to appropriately generate a mesh model by, for example, using information on a spatial grid and the like.

[0009] Therefore, the present disclosure proposes an information processing apparatus, an information processing method, and an information processing program capable of appropriately generating a mesh model.

[0010] SOLUTION TO THE PROBLEM

[0011] According to the present disclosure, an information processing apparatus includes an acquisition unit that acquires spatial information indicating a mesh model of a continuum and first particles, the first particles being particles arranged in a space and having a physical quantity, and a simulation unit that performs simulation including projection processing of projecting a physical quantity of a spatial grid to second particles, the spatial grid being a grid arranged in the space to which the physical quantity of the first particles is projected, the second particles being particles arranged at positions corresponding to nodes of the mesh model in the space, update processing of updating positions of the second particles based on the physical quantity projected to the second particles, and generation processing of generating the mesh model based on the updated positions of the second particles. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 An example of information processing according to an embodiment of the present disclosure is shown.

[0013] Figure 2 An example of processing using particles and lattices is shown.

[0014] Figure 3 A configuration example of an information processing device according to an embodiment of the present disclosure is shown.

[0015] Figure 4 is a flowchart showing a process of information processing according to an embodiment of the present disclosure.

[0016] Figure 5 is a flowchart showing an example of a detailed process of information processing.

[0017] Figure 6 An example of information in a first process according to an embodiment is shown.

[0018] Figure 7 An example of information in a second process according to the embodiment is shown.

[0019] Figure 8 An example of information in a third process according to the embodiment is shown.

[0020] Figure 9 An example of information in a fourth process according to the embodiment is shown.

[0021] Figure 10 An example of information in a fifth process according to an embodiment is shown.

[0022] Figure 11 An example of processing related to interaction with a rigid body is shown.

[0023] Figure 12 An example of processing related to interaction with a rigid body is shown.

[0024] Figure 13 An overview of a sixth process according to the embodiment is given.

[0025] Figure 14 is a flowchart showing a process of the sixth process according to the embodiment.

[0026] Figure 15 A configuration example of an information processing system according to a modification of the present disclosure is shown.

[0027] Figure 16 A configuration example of an information processing device according to a modification of the present disclosure is shown.

[0028] Figure 17 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing apparatus.

[0029] Figure 18 is a flowchart showing an example of a processing procedure of the conventional method.

[0030] Figure 19 is a flowchart showing an example of a processing procedure of the conventional method.

[0031] Figure 20 An example of processing related to the penalty force is shown.

[0032] Figure 21 An example of processing related to the penalty force is shown.

[0033] Figure 22 is a flowchart showing a processing procedure using the penalty force.

[0034] Figure 23 is a flowchart showing a processing procedure using the penalty force.

[0035] Figure 24 Simulation results with and without the penalty force are shown. DETAILED DESCRIPTION

[0036] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by the embodiments. Furthermore, in the following embodiments, the same portions are denoted by the same reference numerals, and repetitive explanations are omitted.

[0037] The present disclosure will be described in the following order of items.

[0038] 1. Embodiments

[0039] 1-1. Overview of information processing according to embodiments of the present disclosure

[0040] 1-1-1. Background, effects, and the like

[0041] 1-2. Configuration of information processing apparatus according to embodiments

[0042] 1-3. Information processing procedure according to embodiments

[0043] 1-4. Processing examples

[0044] 1-4-1. First processing (uniform grid width)

[0045] 1-4-2. Second processing (non-uniform grid width)

[0046] 1-4-3. Third processing (layers of spatial grid)

[0047] 1-4-4. Fourth Processing Step (Mesh Generation)

[0048] 1-4-5. Fifth Processing Step (Mesh Coupling)

[0049] 1-4-6. Sixth Treatment (Interaction with Rigid Bodies)

[0050] 1-4-6-1. Example of using penalty force

[0051] 2. Other embodiments

[0052] 2-1. Variation

[0053] 2-2. Other configuration examples

[0054] 2-3. Others

[0055] 3. Based on the effects of this disclosure

[0056] 4. Hardware Configuration

[0057] [1. Example]

[0058] [1-1. Summary of Information Processing According to Embodiments of the Present Disclosure]

[0059] Figure 1 An example of information processing according to embodiments of the present disclosure is shown. Specifically, Figure 1 This paper illustrates a process (also known as a “generative process”) for generating a mesh model using the Material Point Method (MPM). MPM is an example of a hybrid method that uses mass points and a grid (computational grid) arranged in space (computational space). MPM is also referred to as the mass point method. The movement of a continuum, such as an object, is simulated in MPM. The following will primarily describe the parts relevant to the process of this application, and detailed descriptions will be omitted. Note that the MPM used in the generative process is merely an example. Not only MPM, but any method can be used as long as it is a hybrid method using mass points and a computational grid. This will be described later.

[0060] Figure 3 The information processing device 100 performs information processing according to embodiments of the present disclosure. Figure 3 The information processing apparatus 100 is an example of an information processing apparatus that performs simulation processing (also simply referred to as "simulation") including generation processing for generating a mesh model. The information processing apparatus 100 performs information processing according to this embodiment.

[0061] Figure 1Only a portion of the information arranged in computational space (also known as “space SP”) is shown, illustrating the concepts of processing for generating mesh models (also known as “object mesh models”) of objects (examples of continuums). For example, as Figure 2 As shown in the mesh model MM1, the object can be a rabbit. For example, only meshes MS1 to MS4, which are part of the object's mesh model, are shown, and their movement is demonstrated. When describing meshes MS1 to MS4 without distinguishing between them, they are collectively referred to as meshes MS. Note that although the object's mesh model includes a large number of meshes MS in addition to meshes MS1 to MS4, Figure 1 Only grids MS1 to MS4 are shown for description.

[0062] also, Figure 1 Only particles PT1 to PT8 are shown, and their movement is demonstrated. Particles PT1 to PT8 are examples of particles (also called "first particles") arranged in space SP and possessing physical quantities. When particles PT1 to PT8 are described indiscriminately, they are collectively referred to as particles PT. For example, particles PT correspond to point masses in mixing methods such as MPM. Note that although the first particles arranged in space SP include a large number of particles PT in addition to particles PT1 to PT8, Figure 1 For illustrative purposes, only particles PT1 to PT8 are shown.

[0063] also, Figure 1 Only the spatial grid SL is shown (see Figure 2 A portion of a spatial raster SL, for example, includes region LT1 containing grid points LP1 to LP4. Grid points LP1 to LP4 are formed by the spatial raster SL. The spatial raster SL is a raster arranged in a spatial SP. Furthermore, when describing grid points LP1 to LP4 without distinction, they are collectively referred to as grid points LP. For example, the spatial raster SL corresponds to a computational raster fixed in space in a hybrid method such as MPM. Note that although the spatial raster SL arranged in the spatial SP includes a large number of grid points LP besides grid points LP1 to LP4, Figure 1 Only grid points LP1 to LP4 are shown for description.

[0064] Note that, as Figure 2 As shown, the spatial grid SL includes multiple regions, such as regions LT2, LT3 and LTX in addition to region LT1. Figure 2 An example of processing using points and a raster is shown. Note that the points are used to conceptually illustrate the relationship with hybrid methods such as MPM. Figure 2Only a portion of particle PT is shown, and the grid MS is omitted. The spatial grid SL is fixed in space SP. Particle PT is located in space SP. Particle PT is a point mass with physical quantities and is mobile.

[0065] For example, repeating the process in hybrid methods such as MPM Figure 2 The processes (1) to (4) are shown. For example, in process (1), the mass from the particle to the grid point is projected, and the velocity at the grid point at time n is calculated. Furthermore, in process (2), the force and acceleration at the grid point are calculated, and the grid point velocity at time n is updated. Furthermore, in process (3), the position of the particle is updated according to the grid point velocity, and the time is updated from n to n+1. Furthermore, in process (4), the stress and volume of the particle at time n+1 are updated. Then, the process (1) is repeated by using the stress and volume of the particle at time n+1 and setting time n+1 to time n. Note that this will be described later. Figure 2 Details of the points within the grid. Furthermore, the grid MS lies in space SP, has a changing shape, and moves. These points will be described later.

[0066] also, Figure 1 The spatial information ST1 to ST5 visually illustrates the changes in information during the simulation process. Note that, without distinguishing between spatial information ST1 to ST5, they are collectively referred to as spatial information ST. Spatial information ST corresponds to the computational space used when generating the object mesh model.

[0067] The following will be based on the above premises. Figure 1 First, the information processing device 100 acquires information as shown in the spatial information ST1. For example, the information processing device 100 acquires information including an object mesh model, which includes particles PT1 to PT8, grid points LP1 to LP4 of the spatial grid SL, and grids MS1 to MS4. Particles PT1 to PT8 are first particles arranged in the space SP and possessing physical quantities. Grid points LP1 to LP4 are arranged in the space SP. The physical quantities of the first particles are projected onto grid points LP1 to LP4.

[0068] Furthermore, the information processing device 100 acquires information indicating dummy particles GP1 to GP6, etc. Dummy particles GP1 to GP6 are particles (also referred to as "second particles") arranged at positions corresponding to nodes of an object mesh model including meshes MS1 to MS4, etc. Note that... Figure 1Only dummy particles GP1 to GP6 corresponding to nodes of meshes MS1 to MS4 are shown. Without distinguishing between dummy particles GP1 to GP6, they are collectively referred to as dummy particles GP. Here, dummy particles GP are virtual particles that do not affect the analysis results. For example, only positional information is associated with dummy particles GP. Dummy particles GP do not have physical quantities such as mass and velocity. Note that although, in addition to dummy particles GP1 to GP6, the second particles arranged in the space SP include a large number of dummy particles GP corresponding to the meshes MS included in the object mesh model, Figure 1 Only dummy particles GP1 to GP6 are shown for description.

[0069] Figure 1 This illustrates, in one example, an information processing device 100 generating (updating) an object mesh model from information such as spatial information ST1, in which the shapes of meshes MS1 to MS4 are changed. Note that spatial information ST1 may indicate the state at the start of the simulation process (also called the "initial state"), or it may indicate the state in the middle of the simulation process (also called the "intermediate processing state"). First, reference will be made below. Figure 1 The process is outlined in the overview, followed by a description of the details of the mathematical expressions used in the process.

[0070] The information processing device 100 acquires information representing physical quantities of each particle PT as shown in the spatial information ST2 (step S1). For example, the information processing device 100 acquires information indicating physical quantities Q11 of particle PT1, Q12 of particle PT2, Q13 of particle PT3, Q14 of particle PT4, Q15 of particle PT5, Q16 of particle PT6, Q17 of particle PT7, and Q18 of particle PT8. Figure 1 In this process, the information processing device 100 acquires information representing the velocity of each particle's PT as a physical quantity. From Figure 1 In the spatial information ST2, the length of the arrow extending from each particle PT corresponds to the magnitude of a physical quantity (e.g., velocity). The orientation of the arrow corresponds to the direction of the physical quantity.

[0071] Notice, Figure 1 Only physical quantities Q11 to Q18 are shown, which correspond to particles PT1 to PT8. When explaining physical quantities of particles PT without distinguishing between them, such as physical quantities Q11 to Q18, these physical quantities are collectively referred to as physical quantity Q. Here, particle PT possesses physical quantity Q. Particle PT is the particle (first particle) that affects the analysis results. Note that although physical quantity Q includes a large number of physical quantities Q corresponding to particles PT, in addition to physical quantities Q11 to Q18, Figure 1Only physical quantities Q11 to Q18 are shown for description. Figure 1 In this process, the information processing device 100 acquires information representing the physical quantity Q of particles PT arranged in the space SP. The physical quantity Q includes the physical quantities Q11 to Q18 of particles PT1 to PT8.

[0072] As shown in spatial information ST3, the information processing device 100 projects the physical quantity Q of particle PT onto the grid point LP of the spatial grid SL (step S2). The information processing device 100 projects the physical quantity Q of particle PT arranged in space SP onto the grid point LP of the spatial grid SL. The physical quantity Q includes the physical quantities Q11 to Q18 of particles PT1 to PT8. For example, the information processing device 100 determines (calculates) the physical quantity to be assigned to the grid point LP by using a shape function.

[0073] As described above, the information processing device 100 performs projection processing of physical quantities of particles and grids, as well as grids and nodes, using any shape function. For example, the information processing device 100 uses any shape function such as a b-spline function and a hat function in the projection processing. Note that it is optional to specify which particle PT's physical quantity Q should be projected onto grid point LP. For example, the physical quantity Q of particle PT located within a predetermined range from grid point LP can be projected onto grid point LP.

[0074] For example, the information processing device 100 projects the physical quantity Q of particles PT arranged in space SP onto grid points LP1 to LP4 of the space grid SL. The physical quantity Q includes the physical quantities Q11 to Q18 of particles PT1 to PT8. The information processing device 100 then calculates the physical quantity LQ1 of grid point LP1, the physical quantity LQ2 of grid point LP2, the physical quantity LQ3 of grid point LP3, and the physical quantity LQ4 of grid point LP4. Figure 1 In the spatial information ST3, the length of the arrow extending from each grid point LP corresponds to the magnitude of the physical quantity. The orientation of the arrow corresponds to the direction of the physical quantity.

[0075] Notice, Figure 1 Only physical quantities LQ1 to LQ4 are shown, which correspond to grid points LP1 to LP4. When describing the physical quantities of grid points LP, such as physical quantities LQ1 to LQ4, without distinction, the physical quantities are collectively referred to as physical quantities LQ. Here, physical quantities LQ are projected onto grid points LP of the spatial grid SL. Note that although physical quantities LQ include a large number of physical quantities LQ corresponding to grid points LP in addition to physical quantities LQ1 to LQ4, Figure 1 Only physical quantities LQ1 to LQ4 are shown for description. Figure 1In this process, the information processing device 100 projects the physical quantity Q of particle PT onto grid points LP of spatial grid SL, which includes the physical quantities LQ1 to LQ4 of grid points LP1 to LP4, etc. The information processing device 100 then calculates the physical quantity LQ of grid points LP of spatial grid SL. The physical quantity LQ includes the physical quantities LQ1 to LQ4 of grid points LP1 to LP4.

[0076] Then, as shown in spatial information ST4a, the information processing device 100 updates the physical quantity Q of the particle PT arranged in the space SP using the physical quantity LQ of the grid point LP of the spatial grid SL (step S3a). For example, the information processing device 100 updates the physical quantity Q of the particle PT using the physical quantity LQ of the grid point LP of the spatial grid SL. The grid point LP includes grid points LP1 to LP4. Note that it is possible to optionally set which grid point LP's physical quantity LQ is used to update the particle PT's physical quantity Q. For example, the physical quantity LQ of the grid point LP located within a predetermined range from the particle PT can be used to update the particle PT's physical quantity Q.

[0077] For example, the information processing device 100 updates the physical quantities Q11 to Q18 of particles PT1 to PT8 using the physical quantity LQ of the grid point LP of the spatial grid SL. The physical quantity LQ includes the physical quantities LQ1 to LQ4 of the grid points LP1 to LP4. The information processing device 100 thus updates the physical quantity Q11 of particle PT1 to the physical quantity QL21. Similarly, the information processing device 100 updates the physical quantities Q12 to Q18 of particles PT2 to PT8 to the physical quantities Q22 to Q28. Figure 1 In the spatial information ST4a, the length of the arrow extending from each particle PT corresponds to the magnitude of the updated physical quantity. The orientation of the arrow corresponds to the direction of the updated physical quantity.

[0078] Furthermore, as shown in spatial information ST4b, the information processing device 100 projects the physical quantity LQ of the grid point LP of the spatial grid SL onto the dummy particles GP arranged at positions corresponding to the nodes of the target grid model, including grids MS1 to MS4, etc. (step S3b). The information processing device 100 projects the physical quantity LQ of the grid point LP of the spatial grid SL onto the dummy particles GP arranged at positions corresponding to the nodes of the object grid model. The physical quantity LQ includes the physical quantities LQ1 to LQ4 of the grid points LP1 to LP4. Note that the physical quantity LQ of the grid point LP to be projected onto the dummy particle GP can be arbitrarily set. For example, the physical quantity LQ of the grid point LP located within a predetermined range from the dummy particle GP can be projected onto the dummy particle GP.

[0079] For example, the information processing device 100 projects the physical quantity LQ of the grid points LP of the spatial grid SL onto dummy particles GP1 to GP6 corresponding to the nodes of the grids MS1 to MS4. The physical quantity LQ includes the physical quantities LQ1 to LQ4 of the grid points LP1 to LP4. Thus, the information processing device 100 calculates the dummy physical quantity GQ1 of dummy particle GP1, the dummy physical quantity GQ2 of dummy particle GP2, the dummy physical quantity GQ3 of dummy particle GP3, the dummy physical quantity GQ4 of dummy particle GP4, the dummy physical quantity GQ5 of dummy particle GP5, and the dummy physical quantity GQ6 of dummy particle GP6. Figure 1 In the spatial information ST4b, the length of the arrows extending from each dummy particle GP corresponds to the magnitude of the physical quantity. The orientation of the arrows corresponds to the direction of the physical quantity.

[0080] Notice, Figure 1 Only dummy physical quantities GQ1 to GQ6 are illustrated, which correspond to dummy particles GP1 to GP6. When describing the physical quantities of grid points LP such as dummy physical quantities GQ1 to GQ6 without distinction, the physical quantities are collectively referred to as dummy physical quantities GQ. Here, dummy physical quantities GQ are projected onto dummy particles GP arranged at positions corresponding to nodes in the object mesh model, and are virtual physical quantities used to generate (update) virtual particles in the mesh model.

[0081] Note that although the spurious physics GQ includes a large number of spurious physics GQs other than GQ1 to GQ6, which correspond to spurious particles GP at the nodes of the object mesh model, Figure 1 Only spurious physical quantities GQ1 to GQ6 are shown for descriptive purposes. Figure 2 In this process, the information processing device 100 projects the physical quantity LQ of the grid point LP onto the dummy particles GP of the object mesh model, including the dummy physical quantities GQ1 to GQ6 of dummy particles GP1 to GP6. Thereby, the information processing device 100 calculates the dummy physical quantities GQ of the dummy particles GP of the object mesh model, including the dummy physical quantities GQ1 to GQ6 of dummy particles GP1 to GP6. Note that either step S3a or step S3b can be performed first, or these processes can be performed in parallel.

[0082] Then, as shown in spatial information ST5, the information processing device 100 updates the position of the particles PT arranged in the space SP by using the updated physical quantity Q of the particles PT arranged in the space SP (step S4a). Figure 2 In the process, the information processing device 100 updates the position of particle PT1 based on the physical quantity Q21 of particle PT1 shown in the spatial information ST4a.

[0083] For example, the information processing device 100 calculates the position of particle PT1 shown in spatial information ST5 based on the position of particle PT1 and physical quantity Q21 shown in spatial information ST4a. Similarly, the information processing device 100 updates the positions of particles PT2 to PT8 based on physical quantities Q22 to Q28 shown in spatial information ST4a. Note that the information processing device 100 similarly updates the positions of particles PT other than particles PT1 to PT8 arranged in space SP.

[0084] Furthermore, as shown in spatial information ST5, the information processing device 100 updates the position of the dummy particle GP by using the dummy physical quantity GQ corresponding to the dummy particle GP at the node of the object mesh model (step S4b). Figure 2 In the process, the information processing device 100 updates the position of the spurious particle GP1 based on the spurious physical quantity GQ1 of the spurious particle GP1 shown in the spatial information ST4b.

[0085] For example, the information processing device 100 calculates the position of the dummy particle GP1 shown in the spatial information ST5 based on the position of the dummy particle GP1 shown in the spatial information ST4b and the dummy physical quantity GQ1. Similarly, the information processing device 100 updates the positions of the dummy particles GP2 to GP6 based on the dummy physical quantities GQ2 to GQ6 illustrated in the spatial information ST4b. Note that the information processing device 100 similarly updates the positions of the dummy particles GP at the nodes of the object mesh model other than the dummy particles GP1 to GP6.

[0086] The information processing device 100 generates (updates) the object mesh model based on the above processing. For example, the information processing device 100 generates (updates) the object mesh model by setting the positions of the nodes of the object mesh model to the updated positions of the corresponding dummy particles GP. Figure 1 In this process, the information processing device 100 changes the positions of the nodes of meshes MS1 to MS4 based on the updates of the positions of the dummy particles GP1 to GP6, thereby changing the shape of the meshes MS1 to MS4. The information processing device 100 thus generates (updates) an object mesh model containing meshes MS1 to MS4 with the updated shapes. Note that either step S4a or step S4b can be performed first, or these processes can be performed in parallel.

[0087] The details of the processing performed by the information processing device 100 in the above-described process flow will be described below. First, refer to... Figure 1 This paper provides an overview of the process of projecting the physical quantities of particle PT onto the grid points LP of the spatial grid SL. For example, Figure 3This conceptually illustrates a pattern for projecting the physical quantities of a particle PT (defined as "particle PTX") located in a region LTX onto grid points LP (defined as "grid points LPX") that form a portion of the region LTX. Arrows extending from particle PTX to each grid point LPX correspond to the projection of the physical quantities of particle PTX onto each grid point LPX. The size of the arrows corresponds to the magnitude of the physical quantities projected from particle PTX to each grid point LPX.

[0088] Furthermore, the size of the circle of each grid point LPX corresponds to the magnitude of the physical quantity of each grid point LPX. Figure 3 A conceptual diagram also illustrates the pattern of projecting the physical quantities of each grid point LPX onto the particle PTX. Arrows extending from each grid point LPX to the particle PTX correspond to the projection of the physical quantities of each grid point LPX onto the particle PTX. The size of the arrow corresponds to the size of the physical quantity projected from the particle PTX onto each grid point LPX. Note that detailed descriptions are omitted because the projection of physical quantities between particles and grids is similar to that in hybridization methods such as MPM. Based on the shape function S... ip Determine the physical quantities (e.g., the mass of a particle) assigned to the grid.

[0089] For reference Figure 3 As illustrated in the example, the information processing device 100 projects the physical quantities of the particles onto the grid points of the computation grid. Then, after updating the velocities at the grid points, the information processing device 100 performs projection onto the nodes between the particles and the mesh model. The information processing device 100 then updates the positions of the nodes in the mesh model and the particles. The preconditions for the process described above will be briefly described.

[0090] For example, the information processing device 100 obtains the particle position x at time step t for updating the particle coordinates (e.g., the position of particle PT) through analysis. p t The velocity v at that point p n+1 As shown in expression (1).

[0091]

[0092] Note that the information processing device 100 calculates the particle position x for updating the particle coordinates based on expression (2) instead of expression (1). p t The velocity v at that point p n+1 For example, when the grid speed v can be obtained. i t+1 At that time, the information processing device 100 calculates the velocity v at the particle position xpt according to expression (2). p t+1Note that S in expression (2) ip It is the shape function at the particle's position.

[0093]

[0094] For example, the information processing device 100 performs processing based on the MPM method. For example, the information processing device 100 projects the physical quantities of the particles onto the grid points of the computation grid through the following processing. The information processing device 100 calculates the grid mass m using expression (3). i .

[0095]

[0096] Then, the information processing device 100 calculates the grid momentum Pit by using expression (4).

[0097]

[0098] Then, the information processing device 100 calculates the grid speed vit using expression (5).

[0099]

[0100] Then, the information processing device 100 updates the grid speed v. i t For example, the information processing device 100 applies a grid internal force f. i int,t and external force f i ext,t And perform grid speed v i t+1 The update. For example, the processing using expressions (3) to (5) corresponds to Figure 3 Steps S1 and S2 in the process.

[0101] Note that the information processing device 100 may calculate the grid speed not only using MPM. In this case, the information processing device 100 performs the following processing: The information processing device 100 calculates the grid quality m using expression (6). i .

[0102]

[0103] Then, the information processing device 100 calculates the grid momentum P using expression (7). i t+1 .

[0104]

[0105] Then, the information processing device 100 calculates the grid speed v using expression (8). i t+1 .

[0106]

[0107] Note that the above is an example. The information processing device 100 can calculate the grid speed by any method.

[0108] For example, after updating the velocity at the grid point through the following process, the information processing device 100 performs projection onto the nodes between the particle and the mesh model, and updates the positions of the nodes of the particle and the mesh model. The information processing device 100 calculates the velocity v at the node of the mesh (e.g., the dummy particle GP) using expression (9). n t+1 As described above, the information processing device 100 calculates the spurious physical quantity GQ of the spurious particle GP using expression (9). Note that S in expression (2) in It is the shape function at the node location of the mesh.

[0109]

[0110] Then, the information processing device 100 updates the coordinates x of the nodes of the grid using expression (10). n t+1 The information processing device 100 thereby updates the positions of the nodes in the object mesh model.

[0111]

[0112] Furthermore, the information processing device 100 calculates the particle velocity v using expression (11). p t+1 The information processing device 100 thereby updates the physical quantities of the particles PT arranged in space SP.

[0113]

[0114] Then, the information processing device 100 updates the particle's coordinate x using expression (12). p t+1 The information processing device 100 thereby updates the positions of the particles PT arranged in space SP.

[0115]

[0116] Here, for example, while the Intra-cell Particle (PIC) method can be used as a scheme for transferring physical quantities from particles to the grid (also known as "G2P") or from the grid to particles or mesh nodes (also known as "P2G"), PIC is not a limitation. Any method can be used, such as Fluid Implicit Particle (FLIP) and Affine Particle Intra-cell (APIC).

[0117] [1-1-1. Background, Effects, etc.]

[0118] The use of traveling cubes to generate meshes through particle coordinates, particle volumes, etc., is known. For example, in MPM mesh modeling, MPM particles are easily converted into voxels due to the use of computational grids. Shapes can be reproduced solely through dense particles. However, conventional methods, as described above, have the problem of requiring different processing than MPM algorithms, having a different number of meshes than in the original model, and the small number of particles leading to coarsening. Furthermore, for example, analysis is performed on particles at the mesh node locations as particles are arranged at the nodes and the mesh shape evolves over time through interactions between particles. This makes handling between objects at contact difficult.

[0119] In contrast, as described above, the information processing device 100 updates the positions of the nodes of the mesh model by reflecting the physical quantities of the grid points. The information processing device 100 projects the physical quantities of particles onto the grid points of the computational grid, thereby updating the node coordinates of the mesh model of the continuum in a hybrid simulation method that uses particles and the computational grid to represent the behavior of the continuum. Therefore, in the processing performed by the information processing device 100, the smoothness of the generated mesh model does not depend on the number of particles used in the simulation. Therefore, the information processing device 100 can appropriately generate the mesh model.

[0120] As described above, the information processing device 100 can efficiently generate mesh models based on MPM in physical simulations. For example, the information processing device 100 projects the physical quantities of particles onto grid points of a computation grid and updates the velocities at the grid points. Then, the information processing device 100 performs projections onto the particles and mesh model nodes and updates the positions of the particles and mesh model nodes. The information processing device 100 can generate mesh models even in three dimensions using a similar method. Furthermore, the information processing device 100 can generate mesh models regardless of the shape of the mesh model elements.

[0121] [1-2. Configuration of the information processing apparatus according to the embodiment]

[0122] Next, the configuration of the information processing apparatus 100 will be described. The information processing apparatus 100 is an example of an information processing apparatus that performs information processing according to this embodiment.Figure 3 An example configuration of an information processing apparatus according to an embodiment of the present disclosure is shown. For example, Figure 3 The information processing device 100 in the text is an example of an information processing device.

[0123] like Figure 3 As shown, the information processing device 100 includes a communication unit 11, an input unit 12, a display unit 13, a storage unit 14, and a control unit 15. Figure 4 In the example, the information processing device 100 includes an input unit 12 (e.g., a keyboard and mouse) and a display unit 13 (e.g., a liquid crystal display). The input unit 12 receives various operations from an administrator or other user of the information processing device 100. The display unit 13 displays various information.

[0124] The communication unit 11 is implemented, for example, by a network interface card (NIC) and communication circuitry. The communication unit 11 is connected to a communication network N (such as the Internet) via a wired or wireless means, and sends and receives information to and from another device via the communication network N.

[0125] The user inputs various operations to the input unit 12. The input unit 12 receives input from the user. The input unit 12 receives input from the user containing information for generating the mesh model. The input unit 12 can receive various operations from the user via a keyboard, mouse, and touch panel provided in the information processing device 100.

[0126] Display unit 13 displays various information. Display unit 13 is a display device (display unit) such as a monitor, and displays various information. Display unit 13 displays information generated by simulation unit 152. Display unit 13 displays information about the analysis results from simulation unit 152.

[0127] Furthermore, the information processing device 100 may include not only the display unit 13, but also a function configuration for outputting information. Note that the information processing device 100 may have the function of outputting information as voice. For example, the information processing device 100 may include a voice output unit, such as a speaker, for outputting voice.

[0128] Storage unit 14 is implemented using, for example, semiconductor memory elements (such as random access memory (RAM) and flash memory) or storage devices (such as hard disks and optical disks). Storage unit 14 includes analog information storage unit 141 and grid model information storage unit 142.

[0129] The simulation information storage unit 141 of this embodiment stores various information related to simulation. For example, the simulation information storage unit 141 stores simulation information.

[0130] For example, the analog information storage unit 141 stores various types of information, such as state functions used for simulation processing. Furthermore, the analog information storage unit 141 can store not only the aforementioned information, but also information for various other purposes.

[0131] According to this embodiment, the mesh model information storage unit 142 stores information about the mesh model. For example, the mesh model information storage unit 142 stores information (model data) about the mesh generated through simulation processing. The mesh model information storage unit 142 includes items such as "model ID" and "model data". The model ID represents identification information used to identify the model. The model data represents various information that constitutes the model, such as information and functions related to the networks included in the model.

[0132] For example, the model data of the mesh model (mesh model MM1) identified by model ID "MM1" is model data MDT1. The mesh model information storage unit 142 can store multiple models. For example, the mesh model information storage unit 142 can store mesh model MM2 and mesh model MM3 other than mesh model MM1.

[0133] Note that the grid model information storage unit 142 can store not only the above-mentioned information, but also various information for any purpose.

[0134] Return to Figure 4 The description will continue. The control unit 15 is implemented, for example, by a central processing unit (CPU) and a microprocessor unit (MPU), which executes programs (e.g., information processing programs according to this disclosure) stored within the information processing device 100 using random access memory (RAM) or similar memory as its working area. Furthermore, the control unit 15 is a controller and can be implemented by integrated circuits such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs).

[0135] like Figure 4 As shown, the control unit 15 includes an acquisition unit 151, an analog unit 152, and a transmission unit 153. The control unit 15 implements or performs the information processing functions and operations described below. Note that the internal configuration of the control unit 15 is not limited to... Figure 18 The configuration in the document can be other than those described later, as long as these configurations perform the information processing described later.

[0136] Acquisition unit 151 acquires various types of information. Acquisition unit 151 acquires various types of information from an external information processing device. Acquisition unit 151 retrieves various types of information from storage unit 14. Acquisition unit 151 acquires information received from input unit 12.

[0137] Acquisition unit 151 obtains various information from storage unit 14. Acquisition unit 151 obtains various information from simulation information storage unit 141 and mesh model information storage unit 142. Acquisition unit 151 obtains information indicating a first particle, which is a particle arranged in space and having physical quantities. Acquisition unit 151 obtains information representing a mesh model of a continuum. Acquisition unit 151 obtains information representing the spatial grid onto which the physical quantities of the first particle are projected.

[0138] Acquisition unit 151 acquires spatial information representing a first particle arranged in space and possessing physical quantities, as well as a mesh model of the continuum. Acquisition unit 151 acquires spatial information from simulation information storage unit 141. Acquisition unit 151 acquires various information generated by simulation unit 152.

[0139] Simulation unit 152 performs various simulation processes related to the generation of the mesh model. Simulation unit 152 performs simulations including projection processing, update processing, and generation processing. In the projection processing, physical quantities of a spatial grid are projected onto a second particle. The spatial grid is a grid arranged in space. The physical quantities of a first particle are projected onto the grid. The second particle is a particle arranged at a position corresponding to a node of the mesh model in space. In the update processing, the position of the second particle is updated based on the physical quantities projected onto it. In the generation processing, the mesh model is generated based on the updated position of the second particle.

[0140] Simulation unit 152 performs a simulation of continuum deformation behavior using a hybrid method employing a first particle and a spatial grid used as a computational grid. Simulation unit 152 also performs the simulation using a second particle, a virtual particle that does not affect the analysis results. Simulation unit 152 performs the simulation using a spatial grid with uniform or non-uniform grid widths. Simulation unit 152 adjusts the grid width of the spatial grid according to the continuum deformation indicated by the mesh model.

[0141] Simulation unit 152 performs simulations using spatial grids comprising single or multiple layers. To represent interactions with a rigid body, simulation unit 152 sets up two overlapping spatial grids near the rigid body, projecting the particle's physical quantities onto each of the two overlapping spatial grids using a shading distance field (CDF) method, and projecting physical quantities from each of the two overlapping spatial grids onto the particle. For example, by dividing the signed distance into unsigned distances d... i and symbol label T ir And add the label A for the valid grid. ir To obtain CDF.

[0142] Simulation element 152 increases or decreases the density of the mesh model during the generation process. When the mesh model has a size larger than a predetermined datum, simulation element 152 divides the mesh. When multiple meshes obtained by dividing the mesh satisfy the predetermined datum, simulation element 152 couples the multiple meshes. When multiple meshes of the mesh model have a size that satisfies the predetermined datum, simulation element 152 couples the multiple meshes.

[0143] Simulation unit 152 performs analysis processing based on simulation processing. Simulation unit 152 analyzes each piece of information. Simulation unit 152 analyzes various types of information based on information obtained from an external information processing device. Simulation unit 152 analyzes various types of information according to the information stored in storage unit 14. Simulation unit 152 analyzes various types of information according to simulation processing. Simulation unit 152 stores information about the analysis results in storage unit 14. Simulation unit 152 performs various analysis processes based on the generated information.

[0144] Simulation unit 152 performs generation processing based on simulation processing to generate various types of information. Simulation unit 152 generates various types of information based on information obtained by acquisition unit 151. Simulation unit 152 generates various types of information according to information stored in storage unit 14. Simulation unit 152 generates various types of information according to simulation processing.

[0145] The simulation unit 152 can generate various types of information to be displayed on the display unit 13. The simulation unit 152 can generate various types of information to be displayed on the display unit 13, such as image information containing a mesh model. In this case, the simulation unit 152 generates the information (image) on the screen by appropriately using various existing technologies related to images. The simulation unit 152 generates the image by appropriately using various conventional technologies related to the GUI. For example, the simulation unit 152 can generate the image using CSS, JavaScript (registered trademark), HTML, or any language that can be used for information processing such as the aforementioned information display and operation reception.

[0146] Sending unit 153 sends various types of information. Sending unit 153 provides various types of information. Sending unit 153 provides various types of information to an external information processing device. Sending unit 153 sends various types of information to an external information processing device. Sending unit 153 sends information stored in storage unit 14. Sending unit 153 sends information stored in simulation information storage unit 141 and grid model information storage unit 142.

[0147] Sending unit 153 sends the analysis results from simulation unit 152. Sending unit 153 sends the information generated by simulation unit 152. Sending unit 153 sends the mesh model generated by simulation unit 152 to an external device.

[0148] [1-3. Information processing procedure according to the embodiments]

[0149] The information processing procedure according to this embodiment will now be described. First, refer to... Figure 18 The information processing procedure according to this embodiment is described. Figure 18 This is a flowchart illustrating an information processing procedure according to an embodiment of the present disclosure.

[0150] like Figure 5 As shown, the information processing device 100 acquires spatial information representing a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and possessing physical quantities (step S101). The information processing device 100 performs a projection process that projects the physical quantities of the spatial grid onto the second particle (step S102). The spatial grid is a grid arranged in space. The physical quantities of the first particle are projected onto the grid. The second particle is a particle arranged in space at a position corresponding to a node of the mesh model.

[0151] The information processing device 100 performs an update process to update the position of the second particle based on the physical quantity projected onto the second particle (step S103). The information processing device 100 performs a generation process to generate a mesh model based on the updated position of the second particle (step S104).

[0152] Next, the detailed flow of the processing performed by the information processing device 100 will be described. First, before describing the detailed flow of the processing performed by the information processing device 100, reference will be made to… Figure 5 Describe the processing flow of traditional methods. Figure 1 This is a flowchart illustrating an example of the processing procedure using a traditional method. For example, Figure 6 An example of the processing procedure for the above-described mixing method is shown.

[0153] In the conventional method, MPM particles constituting a continuum in the space are arranged (step S1201). For example, in the conventional method, MPM particles constituting a continuum in the space (corresponding to the aforementioned space SP) (corresponding to the aforementioned particles PT) are arranged.

[0154] In the conventional method, the particle physical quantities of the MPM particles are projected onto the calculation grid points to calculate the grid point physical quantities (step S1202). For example, in the conventional method, as part of the P2G process, the particle physical quantities of the MPM particles (corresponding to the aforementioned physical quantity Q) are projected onto the calculation grid points (corresponding to the aforementioned grid points LP) to calculate the grid point physical quantities (corresponding to the aforementioned physical quantity LQ).

[0155] In the conventional method, the grid point physical quantities are updated (step S1203). In the conventional method, the boundary conditions of the grid points are applied (step S1204). In the conventional method, the grid point physical quantities are projected onto the MPM particles to update the particle physical quantities (step S1205). For example, in the conventional method, as a G2P process, the grid point physical quantities are projected onto the MPM particles to update the particle physical quantities.

[0156] In conventional methods, the positions of MPM particles are updated based on particle physics quantities (step S1206). For example, in existing methods, the positions of MPM particles are updated based on particle physics quantities. In conventional methods, the computation grid is reset (step S1207).

[0157] In the conventional method, when mesh data is output (step S1208: Yes), the mesh data is constructed, for example, by the traveling cube from the MPM particles to be output (step S1209). In contrast, in the conventional method, when no mesh data is output (step S1208: No), the processing of step S1209 is not performed.

[0158] Then, in the conventional method, if the analysis has not ended (step S1210: No), the process returns to step S1201 to repeat. In contrast, in the conventional method, the process ends when the analysis ends (step S1210: Yes).

[0159] Reference from here Figure 6 An example describing the detailed process of the processing performed by the information processing apparatus 100 according to this embodiment. Figure 6 This is a flowchart illustrating an example of a detailed information processing procedure.

[0160] The information processing device 100 arranges MPM particles constituting a continuum in space and arranges dummy particles at the node positions of the grid data of the continuum (step S201). For example, the information processing device 100 arranges particles PT constituting a continuum (e.g., a target object) in space SP and arranges dummy particles GP at the node positions of the grid data of the continuum.

[0161] The information processing device 100 projects the particle physical quantities of the MPM particles onto the calculation grid points to calculate the grid point physical quantities (step S202). For example, as part of the P2G processing, the information processing device 100 projects the physical quantity Q of the particle PT onto the grid point LP to calculate the physical quantity LQ of the grid point LP.

[0162] The information processing device 100 updates the physical quantities of the grid points (step S203). For example, the information processing device 100 updates the physical quantity LQ of the grid point LP. The information processing device 100 applies the boundary conditions of the grid points (step S204). For example, the information processing device 100 similarly applies the boundary conditions of the grid points in the MPM method.

[0163] The information processing device 100 projects grid point physical quantities onto MPM particles and dummy particles to update particle physical quantities (step S205). For example, as a G2P process, the information processing device 100 projects the physical quantity LQ of grid point LP onto particle PT and dummy particle GP to update the physical quantity Q of particle PT and the dummy physical quantity GQ of dummy particle GP.

[0164] The information processing device 100 updates the positions of the MPM particles and dummy particles based on particle physical quantities (step S206). For example, the information processing device 100 updates the position of particle PT based on the physical quantity Q of particle PT, and updates the position of dummy particle GP based on the dummy physical quantity GQ of dummy particle GP.

[0165] The information processing device 100 resets the computational grid (step S207). For example, the information processing device 100 resets the physical quantity LQ of the grid point LP.

[0166] When outputting mesh data (step S208: Yes), the information processing device 100 constructs mesh data from dummy particles and outputs the mesh data (step S209). For example, the information processing device 100 generates (builds) a mesh model by updating the shape of each mesh MS based on the updated position of the dummy particle GP, and outputs data about the generated mesh model. In contrast, when not outputting mesh data (step S208: No), the information processing device 100 does not perform the processing in step S209.

[0167] Then, if the analysis has not ended (step S210: No), the information processing device 100 returns the processing to step S201 to repeat the processing. In contrast, if the analysis ends (step S210: Yes), the information processing device 100 ends the processing.

[0168] [1-4. Processing Examples]

[0169] Based on the above premises, various processing examples related to the embodiments will be described here. The information processing device 100 performs the following first to sixth processes. For example, the information processing device 100 performs at least one of the first to sixth processes. Note that references will be appropriately omitted. Figure 6 The description includes points similar to those in the content being described. Furthermore, the information processing device 100 can combine the first to sixth processing execution controls.

[0170] [1-4-1. First Processing (Uniform Grid Width)]

[0171] First, refer to Figure 6 Describe the first process. Figure 1 An example of information in the first process according to this embodiment is shown. Specifically, Figure 7 An example of information with a fixed grid width is shown.

[0172] exist Figure 7 In the example shown in spatial information ST11, the information processing device 100 performs simulation processing by using information about a spatial grid with uniform grid width. Note that, due to Figure 7 First processing and reference in Figure 7 The processing described is similar, so its detailed description will be omitted.

[0173] [1-4-2. Second Processing (Non-uniform Grid Width)]

[0174] Next, we will refer to Figure 7 Describe the second process. Figure 1 An example of information in the second process according to this embodiment is shown. Specifically, Figure 8 An example of information in the case of a variable grid width is shown. Note that descriptions of points similar to those in the above processing will be appropriately omitted.

[0175] exist Figure 8 As shown in spatial information ST21, the information processing device 100 performs simulation processing by using information about a spatial grid with non-uniform grid widths. Therefore, the information processing device 100 can improve the accuracy of the simulation processing while reducing the amount of memory required. Note that, in addition to the non-uniform grid widths of the spatial grid, Figure 8 Second processing and reference Figure 8 The processing described is similar, so its detailed description will be omitted.

[0176] The information processing device 100 can perform simulation processing by combining the first processing and the second processing. For example, the information processing device 100 can perform the first processing at the start of the simulation processing and switch to the second processing during the simulation processing. The information processing device 100 can start the simulation processing when the spatial grid has a uniform grid width and change the grid width of the spatial grid according to changes in the grid during the simulation processing, etc.

[0177] For example, the information processing device 100 can change the grid width of the spatial grid based on the deformation of the continuum indicated by the mesh model. For example, compared to other grid widths, the information processing device 100 can narrow the grid width of the spatial grid in the range where the mesh density of the mesh model is high. For example, compared to the grid of the spatial grid in the range where the mesh of the mesh model is not located, the information processing device 100 can narrow the grid width of the spatial grid in the range where the mesh of the mesh model is located.

[0178] [1-4-3. Third Processing (Spatial Raster Layer)]

[0179] Next, we will refer to Figure 8 Describe the third processing step. Figure 1 An example of the information processed in the third step of this embodiment is shown. Specifically, Figure 9 An example of information in the presence of multiple layers is shown. Note that descriptions of points similar to those in the above processing will be appropriately omitted.

[0180] exist Figure 9 As shown in spatial information ST31, the information processing device 100 performs simulation processing on a mesh model of multiple continuums (objects). In this case, the information processing device 100 performs the simulation by using a spatial grid comprising two layers.

[0181] Specifically, the information processing device 100 performs simulations using a spatial grid comprising a first layer and a second layer. The first layer corresponds to the mesh model of the upper continuum as shown in spatial information ST31a. The second layer corresponds to the mesh model of the lower continuum as shown in spatial information ST31b. The information processing device 100 can thus perform processing on multiple continuums using a computational grid comprising multiple layers. Furthermore, the information processing device 100 can prevent mesh coupling during object collisions. Note that, in addition to the spatial grid comprising multiple layers, Figure 9 Third processing and reference in Figure 9 The processing described is similar, so its detailed description will be omitted.

[0182] [1-4-4. Ninth Processing Step (Mesh Generation)]

[0183] Next, we will refer to Figure 9 Describe the ninth process. Figure 9 An example of information in the fourth process according to this embodiment is shown. Specifically, Figure 9 An example of information is shown when mesh generation is performed. Note that descriptions of points similar to those in the above processing will be appropriately omitted.

[0184] In the fourth process, the information processing device 100 increases or decreases the density of the mesh model during the generation process. Figure 1 In this process, the information processing device 100 increases the number of meshes by mesh generation. The information processing device 100 generates the mesh when the mesh meets the generation conditions related to mesh generation. For example, the information processing device 100 adds nodes to the mesh model. The information processing device 100 generates the mesh by dividing the mesh into meshes with nodes as vertices.

[0185] For example, the information processing device 100 divides the mesh when the mesh of the mesh model has a size larger than a predetermined reference. The information processing device 100 divides the mesh when the size of the mesh of the mesh model is equal to or greater than a predetermined threshold.

[0186] Furthermore, the information processing device 100 divides the mesh when the mesh of the mesh model has a shape deformed from a predetermined reference. The information processing device 100 also divides the mesh when the mesh of the mesh model has a shape deformed by a predetermined amount or more.

[0187] exist Figure 10 In the examples shown in spatial information ST41 to ST43, the information processing device 100 performs simulation processing on mesh models of multiple continuums (objects). For example, in... Figure 10 In this context, assuming that the information processing device 100 has already processed the simulation, it determines the partitioning conditions that all four grids MS must satisfy based on the change from spatial information ST41 to spatial information ST42.

[0188] Therefore, as shown in spatial information ST43, the information processing device 100 divides each of the four grids MS into four grids. That is, the information processing device 100 generates 16 grids MS by dividing each of the four grids MS into four grids. Then, the information processing device 100 continues the simulation processing using information from these 16 grids MS. Even when the grids have many shapes and undergo changes, the information processing device 100 is able to maintain the accuracy of the shapes. Note that, in addition to the grid division, Figure 10 The fourth processing in the reference is similar. Figure 10 The description is processed accordingly, therefore its detailed description will be omitted.

[0189] [1-4-5. Fifth Processing (Mesh Coupling)]

[0190] Next, we will refer to Figure 10 Describe the fifth process. Figure 10 An example of information in the fifth process according to this embodiment is shown. Specifically, Figure 10 An example of information is shown when mesh coupling is performed. Note that descriptions of points similar to those in the above processing will be appropriately omitted.

[0191] In the fifth process, the information processing device 100 increases or decreases the density of the mesh model during the generation process. Figure 1 In this process, the information processing device 100 increases the number of grids through grid coupling. When multiple grids meet the coupling conditions related to grid coupling, the information processing device 100 couples multiple grids.

[0192] For example, when multiple adjacent grids of a mesh model have dimensions that meet a predetermined reference, the information processing device 100 couples to multiple grids. When the dimensions of multiple adjacent grids of a mesh model are less than a predetermined threshold, the information processing device 100 couples to multiple adjacent grids.

[0193] exist Figure 9 In the examples shown in spatial information ST51 to ST53, the information processing device 100 performs simulation processing on mesh models of multiple continuums (objects). For example, in... Figure 9 In this context, it is assumed that the information processing device 100 has already performed simulation processing and determined the partitioning conditions that the four grids MS must satisfy based on the change from spatial information ST51 to spatial information ST52.

[0194] Therefore, as shown in Spatial Information ST53, the information processing device 100 couples four grids MS into two grids. That is, the information processing device 100 generates two grid MSs by reconfiguring the four grid MSs into two grids. Then, the information processing device 100 continues the simulation processing using information including the two grid MSs. When the grids have small shapes, the information processing device 100 can thereby save on the amount of memory required for the grids. Note that, in addition to the grids being coupled, Figure 11 The fifth processing and reference in Figure 12 The processing described is similar, so its detailed description will be omitted.

[0195] Furthermore, when multiple grids generated through grid division meet a predetermined benchmark, the information processing device 100 couples the multiple grids. When the multiple grids obtained through grid division have a size smaller than a predetermined threshold, the information processing device 100 can recouple the multiple grids.

[0196] For example, when Figure 11 The shape of the mesh model in the spatial information ST43 is returned to Figure 11 When determining the shape of the mesh model in the spatial information ST41, the information processing device 100 can recouple 16 meshes MS into four meshes MS. In this case, the information processing device 100 can recouple 16 meshes MS into four meshes MS by returning the meshes MS obtained through partitioning in the partitioning process to the original meshes MS.

[0197] [1-4-6. Sixth Treatment (Interaction with Rigid Bodies)]

[0198] From here on, the description will include treatments involving interactions with rigid bodies. First, refer to... Figure 11 and Figure 12 This paper provides an overview of the rigid body coupling analysis method in MPM. Figure 13 and 12 An example of the treatment related to the interaction between the particle and the rigid body is shown. In MPM, the Intra-Element Compatible Particle (CPIC) method is used as an MPM particle-rigid body coupling analysis method.

[0199] For example, in conventional methods, the interaction between rigid bodies and MPM particles can be analyzed using the methods described above, and cuts in ultrathin rigid bodies can be represented. In conventional methods, the normal vector and distance to the rigid body boundary are calculated for each particle using the moving least squares method.

[0200] In the moving least squares method, the following is performed: Figure 13 The processing shown. In Figure 13 In the figures GR11, GR12, and GR13, the original data, the application of weights, and the fitting are conceptually illustrated, respectively. Moving least squares is a least squares method that uses a weight function and is used to obtain the fitting result from the nearest neighbor points. The primary moving least squares method is shown in expression (13).

[0201]

[0202] [I](x) is the diagonal matrix of the weighting function. Furthermore, x is represented by the following expression (14) at the location of the known data. i When the expression (15) and (16) are true, the following expressions hold true.

[0203]

[0204]

[0205]

[0206] Traditional methods, while determining values ​​with some precision when two or more grid points with known distances exist, impose a significant computational burden. In existing methods, such as... Figure 13 As shown, the distance to the rigid body surface is defined as u. i Define the shape function as S i Under these circumstances, equations (17) and (18) hold true.

[0207]

[0208]

[0209] In the existing method, the distance from the particle's position to the rigid body surface and the normal vector can be derived in the following equation (19).

[0210]

[0211] In traditional methods, the unit normal vector from the particle's position to the rigid body surface is Vu. p / |Vu p As mentioned above, in traditional methods, the normal vector and distance to the rigid body boundary need to be calculated for each particle using the moving least squares method. Unfortunately, this increases computational cost.

[0212] Therefore, the information processing device 100 performs a sixth process, in which the CPIC algorithm, which does not require moving least squares, is extended. In the sixth process, in order to represent the interaction with the rigid body, the information processing device 100 sets two overlapping spatial grids near the rigid body, projects the physical quantities of the particle onto each of the two overlapping spatial grids using the CDF method, and projects the physical quantities from each of the two overlapping spatial grids onto the particle.

[0213] First, refer to Figure 13 Overview of the sixth process. Figure 13 The sixth process according to this embodiment is summarized. Specifically, Figure 19 An example of a process involving interaction with a rigid body is shown. Note that descriptions of points similar to those in the processes described above will be appropriately omitted.

[0214] The information processing device 100 generates multiple meshes near the rigid body. Figure 19 As shown in spatial information ST61, the information processing device 100 generates multiple meshes near the rigid body RB. Figure 19 The "T" is identifiable by the addition of different shading patterns. ir = +1” grid point LP and “T” ir The grid point LP is set to "= -1". Furthermore, in... Figure 14 In this context, particle PTX is "T pr The particle PT is "+1", and the particle PTY is "T". pr Particle PT with = -1”.

[0215] In the sixth process, the information processing device 100 performs the rigid body-related processing on the grid near the rigid body, as shown in process PS1. The information processing device 100 projects the physical quantity onto "T". pr T ir= -1” grid. The information processing device 100 updates the grid speed, including collision handling. Then, the information processing device 100 from “T pr T ir = -1” is the physical quantity of the raster projection.

[0216] Furthermore, in the sixth process, the information processing device 100 performs processing related to the normal grid on the normal grid, as shown in process PS2. The information processing device 100 projects physical quantities onto “T”. pr T ir = 1” or “T” pr T ir = 0” grid. The information processing device 100 updates the normal grid speed. Then, the information processing device 100 from “T” pr T ir = 1” or “T” pr T ir =0” for the raster projection physical quantity.

[0217] Next, the detailed flow of the sixth process performed by the information processing device 100 will be described based on the aforementioned overview of the process. First, before describing the detailed flow of the sixth process performed by the information processing device 100, reference will be made to… Figure 14 Describe the processing flow of traditional methods. Figure 20 This is a flowchart illustrating an example of the processing procedure using a traditional method. For example, Figure 21 An example of the processing procedure for the above-described blending method is shown, in which a rigid body is added. Note that descriptions of points similar to those mentioned above will be appropriately omitted.

[0218] In the conventional method, contact processing between rigid bodies is performed, and the joint angles and velocities of the rigid bodies are updated (step S1301). In the conventional method, the CDF of the computational grid points is calculated from the computational points on the surface of the rigid bodies (step S1302).

[0219] In the conventional method, the sign tag and contact determination tag of the MPM particle are calculated from the CDF of the calculated grid points (step S1303). In the existing method, the unsigned distance and normal vector of the MPM particle to the rigid body surface are calculated from the CDF of the calculated grid points by using the moving least squares method (step S1304).

[0220] In conventional methods, particle physical quantities are projected onto non-contact grid points of each MPM particle to calculate grid point physical quantities (step S1305). For example, in conventional methods, as part of P2G processing, particle physical quantities of MPM particles are projected onto non-contact grid points (corresponding to the aforementioned grid point LP) to calculate grid point physical quantities.

[0221] In the existing method, for each MPM particle, the particle physical quantities are projected onto a calculation point on the surface of the rigid body closest to the contact grid point to calculate the impulse of the rigid body (step S1306). In the conventional method, the grid point physical quantities are updated (step S1307). In the conventional method, the boundary conditions of the grid point are applied (step S1308).

[0222] In the conventional method, for each MPM particle, the physical quantities of non-contact grid points and the contact conditions between contact grid points and the rigid body are projected onto the particle to update the particle's physical quantities (step S1309). For example, in the conventional method, as part of G2P processing, for each MPM particle, the physical quantities of non-contact grid points (corresponding to the aforementioned physical quantity LQ) and the contact conditions between contact grid points and the rigid body are projected onto the particle to update the particle's physical quantities.

[0223] In existing methods, the positions of MPM particles are updated based on particle physical quantities (step S1310). In conventional methods, the computation grid is reset (step S1311).

[0224] Then, in the conventional method, if the analysis has not ended (step S1312: No), the process returns to step S1301 to repeat. Conversely, in the conventional method, the process ends when the analysis ends (step S1312: Yes).

[0225] Reference from here Figure 20 An example describing the detailed process of the sixth process performed by the information processing apparatus 100 according to this embodiment. Figure 20 This is a flowchart illustrating the process of the sixth process according to this embodiment.

[0226] The information processing device 100 performs contact processing between rigid bodies and updates the joint angles and velocities of the rigid bodies (step S301). The information processing device 100 calculates the CDF of the computational grid points from the computational points on the surface of the rigid bodies (step S302).

[0227] In the conventional method, the information processing device 100 calculates the symbolic tag and contact determination tag of the MPM particle based on the CDF of the calculation grid points (step S303). For each MPM particle, the information processing device 100 projects the particle physical quantity to the contact grid points of calculation grid A and the non-contact grid points of calculation grid B to calculate the grid point physical quantity (step S304). For example, as a P2G process, the information processing device 100 projects the physical quantity Q for each particle PT to the contact grid points of calculation grid A and the non-contact grid points of calculation grid B to calculate the physical quantity LQ.

[0228] The information processing device 100 projects the physical quantity of the contact grid point of the calculation grid A onto the nearest calculation point on the surface of the rigid body to calculate the impulse of the rigid body (step S305). For example, the information processing device 100 projects the physical quantity LQ of the contact grid point of the calculation grid A onto the nearest calculation point on the surface of the rigid body to calculate the impulse of the rigid body.

[0229] The information processing device 100 updates the physical quantities of the grid points (step S306). For example, the information processing device 100 updates the physical quantity LQ of the grid point LP. The information processing device 100 applies boundary conditions to the grid points, including contact conditions with the rigid body (step S307).

[0230] The information processing device 100 calculates the physical quantities of the contact grid points of grid A and the non-contact grid points of grid B for each MPM particle projection, in order to calculate the particle physical quantity (step S308). For example, as a G2P process, the information processing device 100 calculates the physical quantity LQ of the contact grid points LT in grid A and the physical quantity LQ of the non-contact grid points LT in grid B for each particle PT projection, in order to calculate the physical quantity Q.

[0231] The information processing device 100 updates the position of the MPM particle based on the particle's physical quantity (step S309). For example, the information processing device 100 updates the position of particle PT based on the physical quantity Q of particle PT. The information processing device 100 resets computation grid A and computation grid B (step S310). For example, the information processing device 100 resets the physical quantity LQ of grid point LP of computation grid A and computation grid B.

[0232] Then, if the analysis has not ended (step S311: No), the information processing device 100 returns the processing to step S301 to repeat the processing. Conversely, if the analysis ends (step S311: Yes), the information processing device 100 ends the processing. Furthermore, the information processing device 100 can output a mesh model at the end of the analysis.

[0233] As described above, the information processing device 100 can provide expressions similar to those in typical CPIC without using moving least squares. Furthermore, since the sixth processing described above is easy to implement and can reduce computational costs, it can be an efficient method for particle-rigid body coupling analysis in MPM.

[0234] [1-4-6-1. Example of using punishment]

[0235] The processing involving interactions with rigid bodies is not limited to the processes described above. The information processing device 100 can perform processing involving interactions with rigid bodies by using various types of information. For example, the information processing device 100 can perform processing involving interactions with rigid bodies by using a penalty force.

[0236] For example, the following literature discloses the punitive force in MPM.

[0237] - A Moving Least Squares Material Point Method with DisplacementDiscontinuity and Two-Way Rigid Body Coupling, Yuanming Hu, et al. https: / / dl.acm.org / doi / 10.1145 / 3197517.3201293

[0238] However, the processing disclosed in the aforementioned literature has room for improvement in the way the penalty force is applied to each particle (MPM particle). For example, it cannot be said that the computational grid is used sufficiently and effectively when applying the penalty force to each particle in the processing disclosed in the aforementioned literature. It is desirable to perform a process that applies a sufficient penalty force to each particle by using the computational grid more effectively.

[0239] Therefore, the information processing device 100 performs a process involving the interaction of rigid bodies by using a penalty force. An example of a process involving the interaction of rigid bodies using a penalty force will be described below. The information processing device 100 performs this process. Note that descriptions of points similar to those in the foregoing will be appropriately omitted.

[0240] In processes involving interactions between rigid bodies that employ penalty forces, the information processing device 100 projects information about the particle onto a grid (computation grid) at once using the penalty force (penalty impulse) to calculate (form) a penalty field. For example, the information processing device 100 calculates the penalty field by calculating the penalty force at each grid point near the particle. The information processing device 100 can thus improve local errors.

[0241] The information processing device 100 calculates the penalty field of the grid points updated according to the penalty force of the particle. For example, the information processing device 100 calculates the penalty force of each grid point near the particle updated according to the penalty force of the particle. For example, the information processing device 100 calculates the penalty field of the grid points updated according to the penalty force of the particle using expression (20).

[0242]

[0243] For example, f on the leftP,n This corresponds to the penalty force at each grid point. The subscript i corresponds to the information used to identify each grid point (e.g., grid point LT) (e.g., grid point ID). For example, f on the right... P,n The penalty force corresponds to the particle. The subscript p corresponds to the information (e.g., particle ID) used to identify each particle (e.g., particle PT). Here, in the following example, the penalty force is defined in a manner proportional to the mass of the particle, as shown in equations (21) and (22) below.

[0244]

[0245]

[0246] For example, expression (21) corresponds to T having less than c. pr d p The penalty force of the particles is a function of the penalty force used in the case of grid points (calculated expression). Furthermore, m p Corresponding to the weight of the particle, k h Corresponding to the spring constant. T pr The plus or minus sign corresponds to the positional relationship between the particle and the rigid body. Furthermore, d p This corresponds to the normal distance from the surface of the rigid body to the particle.

[0247] Additionally, c is the contact gap. This can be set to any value, such as a value corresponding to a grid width. Furthermore, n... p This corresponds to the unit normal at the particle's position (to the rigid body). As shown in expression (21), when T pr d p When T is less than c, the force (value) calculated based on, for example, the penalty force at a particle is reflected in the penalty force at the grid point. For example, when T pr d p When the value is less than c, the influence of the particle is reflected in the penalty force at the grid point in proportion to the particle's weight.

[0248] Furthermore, equation (22) corresponds to T having a value equal to or greater than c. pr d p The penalty force of the particles is reflected as a function of the penalty force used in the case of grid points. As shown in expression (22), when T pr d p When c is equal to or greater than c, the penalty force at the particle is not reflected in the penalty force at the grid point.

[0249] When the effects of each particle on the grid point are summed, the information processing device 100 can reduce the influence of particles with small mass and increase the influence of particles with large mass by using the above expressions (20) to (22). Furthermore, the information processing device 100 can use c as the contact gap from T... pr d p <c begins contact processing and can reduce particle penetration. Furthermore, the information processing device 100 updates the velocity of each grid point using the following expression (23).

[0250]

[0251] For example, the information processing device 100 calculates the velocity of each grid point to which the penalty force is applied by adding a value indicating the effect of the penalty force to the velocity of each grid point that does not have the effect of the penalty force. For example, the information processing device 100 calculates a value that is proportional to the penalty force of the grid point and inversely proportional to the mass of the grid point (grid mass) as a value indicating the effect of the penalty force to be reflected in the velocity.

[0252] From this point onward, we will describe the treatment without using moving least squares in the treatment of rigid body interactions involving penalized forces. For example, without using moving least squares, we need to consider the rigid body particles r closest to particle p. pη,closest To calculate the unsigned distance d at the particle's position p and unit normal n p For example, the information processing device 100 uses, for example, Figure 20 and Figure 21 The process shown is used to calculate the unsigned distance d at the particle's position. p and unit normal n p . Figure 21 and 21 An example of processing related to punitive force is shown.

[0253] Figure 22 and 21 This illustrates a case where the particle PTZ is used as an example to determine rigid body particles (computation points) near the particle PTZ. For example, the information processing device 100 calculates the distance between a rigid body particle (e.g., a calculation point) with an ID stored in a grid point near the MPM particle and the MPM particle, and uses the nearest distance to the rigid body particle. Figure 14 As shown, the information processing device 100 identifies the rigid particle RP2 as the closest rigid particle to particle PTZ among the rigid particles RP1, RP2, and RP3 located on the surface of the rigid body RB. For example, the information processing device 100 identifies the rigid particle RP2 as the closest rigid particle to particle PTZ by using the search process of expression (24).

[0254]

[0255] For example, equation (24) is a function used to derive the distance between the particle and the rigid body. Then, as... Figure 22 As shown, the information processing device 100 uses the unsigned distance between rigid body particle RP2 and particle PTZ (corresponding to...). Figure 22 d in p Rigid particle RP2 is the closest rigid particle to particle PTZ. Information processing device 100 calculates the unit normal n at the position of particle PTZ using the expression (25) for calculating the unit normal at the particle's position. p .

[0256]

[0257] Based on the above, we will refer to this. Figure 22 and 23 An example describing the detailed process of the processing using penalty force performed by the information processing device 100. Note that appropriate omissions and references will be made. Figure 23 The description focuses on points similar to those mentioned in the description. First, the description... Figure 23 The processing in the middle. Figure 23 This is a flowchart illustrating the process of applying punitive force. Specifically, Figure 21 This is a flowchart illustrating the processing procedure when a penalty force is applied using the moving least squares method.

[0258] The information processing device 100 performs contact processing between rigid bodies and updates the joint angles and velocities of the rigid bodies (step S401). The information processing device 100 calculates the CDF of the computational grid points from the calculation points on the surface of the rigid bodies (step S402).

[0259] The information processing device 100 calculates the symbolic tag and contact determination tag of the MPM particle based on the CDF of the calculated grid points (step S403). The information processing device 100 calculates the unsigned distance and normal vector to the rigid surface of the MPM particle based on the CDF of the calculated grid points using the moving least squares method (step S404). For example, the information processing device 100 calculates the unsigned distance and normal vector to the rigid surface for each particle PT using the moving least squares method.

[0260] The information processing device 100 calculates the penalty force for each MPM particle, projects the penalty force onto the contact grid points of calculation grid A and the non-contact grid points of calculation grid B, and calculates the penalty force at the grid points (step S405). For example, as part of the P2G process, the information processing device 100 calculates the penalty force for each particle PT, projects the calculated penalty force onto the contact grid points LT in calculation grid A and the non-contact grid points LT in calculation grid B, and calculates the penalty force at the grid point LT.

[0261] The information processing device 100 projects the particle physical quantity onto a non-contact grid point in the calculation grid B for each MPM particle to calculate the grid point physical quantity (step S406). For example, as a P2G process, the information processing device 100 projects the physical quantity Q onto a non-contact grid point in the grid point LT of the calculation grid B for each particle PT to calculate the physical quantity LQ.

[0262] The information processing device 100 projects the particle physical quantity for each MPM particle onto a calculation point on the surface of a rigid body closest to the contact grid point to calculate the impulse of the rigid body (step S407). For example, the information processing device 100 projects the physical quantity Q for each MPM particle onto a calculation point on the surface of a rigid body closest to the contact grid point LT to calculate the impulse of the rigid body.

[0263] The information processing device 100 updates the physical quantities of the grid points (step S408). For example, the information processing device 100 updates the physical quantity LQ of the grid point LP. The information processing device 100 applies the boundary conditions of the grid points (step S409). For example, the information processing device 100 applies the boundary conditions of the grid point LP, which include contact conditions with a rigid body.

[0264] The information processing device 100 calculates the penalty force and physical quantity of the non-contact grid points of grid B and the penalty force and contact condition with the rigid body of the contact grid points of grid A for each MPM particle, in order to update the particle physical quantity (step S410). For example, as a G2P process, the information processing device 100 calculates the penalty force and physical quantity LQ of the non-contact grid points in grid point LT of grid B and the penalty force and contact condition of the contact grid points in grid point LT of grid A for each particle PT, in order to calculate the physical quantity Q.

[0265] The information processing device 100 updates the position of the MPM particle based on the particle's physical quantity (step S411). For example, the information processing device 100 updates the position of particle PT based on the physical quantity Q of particle PT. The information processing device 100 resets the computation grid (step S412). For example, the information processing device 100 resets the physical quantity LQ of grid point LP of computation grid A and computation grid B.

[0266] Then, if the analysis has not ended (step S413: No), the information processing device 100 returns the processing to step S401 to repeat the processing. Conversely, if the analysis ends (step S413: Yes), the information processing device 100 ends the processing. Furthermore, the information processing device 100 can output a mesh model at the end of the analysis.

[0267] Next, we will describe Figure 24 The processing in the middle. Figure 24 This is a flowchart illustrating the process of applying punitive force. Specifically, Figure 24 This is a flowchart illustrating the process of searching for the nearest calculated point on the surface of a rigid body from the CDF.

[0268] The information processing device 100 performs contact processing between rigid bodies and updates the joint angles and velocities of the rigid bodies (step S501). The information processing device 100 calculates the CDF of the computational grid points from the computational points on the surface of the rigid bodies (step S502).

[0269] The information processing device 100 calculates the symbolic label and contact determination label of the MPM particles based on the CDF of the calculated grid points (step S503). The information processing device 100 searches the CDF of the calculated grid points for the nearest calculated point on the rigid body surface for each MPM particle, thereby calculating the unsigned distance and normal vector of each MPM particle to the rigid body surface (step S504). For example, the information processing device 100 searches for the nearest calculated point on the rigid body surface using an expression (24) for each particle's PT, etc. The information processing device 100 calculates, for example, the nearest calculated point on the rigid body surface by using the calculated points extracted (selected) through the search. Figure 24 The unsigned distance to the rigid body surface and the normal vector are shown.

[0270] The information processing device 100 calculates the penalty force for each MPM particle, projects the penalty force onto the contact grid points of calculation grid A and the non-contact grid points of calculation grid B, and calculates the penalty force at the grid points (step S505). For example, as part of the P2G process, the information processing device 100 calculates the penalty force for each particle PT, projects the calculated penalty force onto the contact grid points LT in calculation grid A and the non-contact grid points LT in calculation grid B, and calculates the penalty force at the grid point LT.

[0271] The information processing device 100 projects the physical quantity of the contact grid point of the calculation grid A onto the nearest calculation point on the surface of the rigid body to calculate the impulse of the rigid body (step S506). For example, the information processing device 100 projects the physical quantity LQ of the contact grid point of the calculation grid A onto the nearest calculation point on the surface of the rigid body to calculate the impulse of the rigid body.

[0272] The information processing device 100 updates the physical quantities of the grid points (step S507). For example, the information processing device 100 updates the physical quantity LQ of the grid point LP. The information processing device 100 applies boundary conditions to the grid points, including contact conditions with the rigid body (step S508).

[0273] The information processing device 100 calculates the physical quantities of the contact grid points of grid A and the non-contact grid points of grid B for each MPM particle projection, in order to calculate the particle physical quantity (step S509). For example, as a G2P process, the information processing device 100 calculates the physical quantity LQ of the contact grid points LT in grid A and the physical quantity LQ of the non-contact grid points LT in grid B for each particle PT projection, in order to calculate the physical quantity Q.

[0274] The information processing device 100 updates the position of the MPM particle based on the particle's physical quantity (step S510). For example, the information processing device 100 updates the position of particle PT based on the physical quantity Q of particle PT. The information processing device 100 resets computation grid A and computation grid B (step S511). For example, the information processing device 100 resets the physical quantity LQ of grid point LP of computation grid A and computation grid B.

[0275] Then, if the analysis has not ended (step S512: No), the information processing device 100 returns the processing to step S501 to repeat the processing. Conversely, if the analysis ends (step S512: Yes), the information processing device 100 ends the processing. Furthermore, the information processing device 100 can output a mesh model at the end of the analysis.

[0276] Here, we will refer to Figure 24 Examples of simulation results are described, showing the results with and without the aforementioned penalty force. Figure 15 Simulation results are shown with and without a penalty force applied. For example, Figure 15 This demonstrates how to perform clothing simulation in MPM.

[0277] Figure 16 The simulation results RS1 without applying a penalty force are shown. Furthermore, Figure 15 The simulation result RS2 is shown with a penalty force applied. When no penalty force is applied, penetration can be easily observed in a thin continuum, such as clothing, as shown in simulation result RS1. Conversely, when a penalty force is applied, penetration is prevented, and a stable analysis can be performed as shown in simulation result RS2. As described above, the information processing device 100 can perform a more appropriate analysis that conforms to the laws of real space by using a penalty force.

[0278] [2. Other embodiments]

[0279] The processing according to the above embodiments can be performed in various forms (variations) different from those described above. For example, the system configuration is not limited to the system configuration in the example above. Various modes of system configuration can be adopted. This will be described below. Note that descriptions of points similar to those in the information processing apparatus 100 according to this embodiment will be appropriately omitted below.

[0280] [2-1. Example of variation]

[0281] For example, although in the above example, the information processing device 100, which is a terminal device used by the user, performs the generation process, the information processing device 100 performing the generation process and the terminal device used by the user can be separated from each other. This will be referred to... Figure 15 and 16 Describe it. Figure 15 An example configuration of an information processing system according to a variant of this disclosure is shown. Figure 16 An example configuration of an information processing apparatus according to a variation of this disclosure is shown.

[0282] like Figure 16 As shown, the information processing system 1 includes a terminal device 10 and an information processing apparatus 100A. The terminal device 10 and the information processing apparatus 100A are communicatively connected via a communication network N, either wired or wirelessly. Note that... Figure 16 The information processing system 1 may include multiple terminal devices 10 and multiple information processing units 100A. The information processing units 100A communicate with the terminal devices 10 via a communication network N.

[0283] Information processing device 100A can generate a mesh model based on information received from terminal device 10 and send the generated mesh model to terminal device 10. For example, information processing device 100A generates a mesh model based on information already received from terminal device 10 requesting the generation of a mesh model and information specifying the objects to be generated. Furthermore, information processing device 100A can generate a mesh model based on information such as parameters specified by a user via terminal device 10. Note that information processing system 1 may include information processing device 100 instead of information processing device 100A.

[0284] Terminal device 10 is an information processing device used by a user. Terminal device 10 is implemented as, for example, a notebook personal computer (PC), desktop PC, smartphone, tablet terminal, mobile phone, and personal digital assistant (PDA). Note that any terminal device 10 can be used, as long as it can display information provided by information processing device 100A. Terminal device 10 is a client terminal.

[0285] In addition, terminal device 10 receives user operations.Figure 17 In the example, terminal device 10 displays information provided by information processing device 100A on the screen. Furthermore, terminal device 10 sends information such as instructions for user operations to information processing device 100A. For example, terminal device 10 sends information requesting the generation of a mesh model to information processing device 100A. For instance, terminal device 10 sends information to information processing device 100A specifying the object for which a mesh model should be generated.

[0286] Terminal device 10 displays information received from information processing device 100A. Terminal device 10 displays the results of simulation processing received from information processing device 100A. Terminal device 10 displays the mesh model received from information processing device 100A. Terminal device 10 displays the analysis results received from information processing device 100A.

[0287] Information processing device 100A performs information processing similar to that performed by information processing device 100, except that information processing device 100A provides information to terminal device 10 and retrieves information from terminal device 10. Information processing device 100A acts as a server providing services to terminal device 10, which functions as a client terminal. For example, information processing device 100A performs simulation processing to generate a mesh model based on information retrieved from terminal device 10 and sends the execution result to terminal device 10.

[0288] like Figure 17 As shown, the information processing device 100A includes a communication unit 11, a storage unit 14, and a control unit 15A. The communication unit 11 is connected to a communication network N (e.g., the Internet) via a wired or wireless means, and sends and receives information from the terminal device 10 via the communication network N. In this case, the information processing device 100A does not need to have the function of displaying information present in the information processing device 100. Note that the information processing device 100A may include an input unit (e.g., a keyboard and mouse) and a display unit (e.g., a liquid crystal display) used by an administrator or other user of the information processing device 100A.

[0289] The control unit 15A is implemented by executing programs (e.g., information processing programs according to this disclosure) stored in the information processing device 100A using RAM or similar memory as a working area, such as a CPU or MPU. Furthermore, the control unit 15A can be implemented using integrated circuits such as ASICs and FPGAs.

[0290] like ​As shown, the control unit 15A includes an acquisition unit 151A, an analog unit 152, and a transmission unit 153A. The control unit 15A implements or performs the information processing functions and operations described below. Note that the internal configuration of the control unit 15A is not limited to... ​ The configuration in [the document / reference] is acceptable. Alternatively, a different configuration can be used, as long as that configuration performs the information processing described later.

[0291] Acquisition unit 151A acquires various information similarly to acquisition unit 151. Acquisition unit 151A obtains various information from storage unit 14. Acquisition unit 151A obtains various information from terminal device 10. Acquisition unit 151A obtains user input information from terminal device 10.

[0292] Transmitting unit 153A provides various information similar to that of transmitting unit 153. Transmitting unit 153A provides various information to terminal device 10. Transmitting unit 153A sends various information to terminal device 10. Transmitting unit 153A provides terminal device 10 with information generated by simulation unit 152. Transmitting unit 153A provides terminal device 10 with analysis results from simulation unit 152. Transmitting unit 153A sends information displayed on terminal device 10. Transmitting unit 153A sends terminal device 10 with a mesh model generated by simulation unit 152.

[0293] [2-2. Other configuration examples]

[0294] Furthermore, the processing according to the above embodiments or variations can be performed in various different forms (variations) besides the embodiments and variations described above. For example, the device performing the simulation processing (simulation device) and the device communicating with external devices and providing information (information providing device) can be separated from each other. In this case, the information processing system may include the simulation device and the information providing device. Note that the above is an example. The information processing system can be implemented by various configurations.

[0295] [2-3. Others]

[0296] Furthermore, in the processes described in the above embodiments, all or part of the processes described as automatically executed can be executed manually, or all or part of the processes described as manually executed can be executed automatically by known methods. Additionally, unless otherwise stated, the processing procedures, specific names, and information including various data and parameters in the above documents and figures can be arbitrarily changed. For example, the information in the figures is not limited to the information illustrated.

[0297] Furthermore, each component of each illustrated device is functional and conceptual, and does not necessarily require physical configuration as shown. That is, the specific form of distribution / integration of the devices is not limited to the illustrated form, and all or part of the devices can be configured in any unit in a functionally or physically distributed / integrated manner according to various loads and usage conditions.

[0298] Furthermore, the above embodiments and variations can be appropriately combined as long as the content being processed does not contradict each other.

[0299] Furthermore, the effects described in this specification are merely illustrative and not limiting. Other effects may be achieved.

[0300] [3. Effects of this disclosure]

[0301] As described above, the information processing apparatus according to this disclosure (information processing apparatuses 100 and 100A in the embodiments) includes an acquisition unit (acquisition units 151 and 151A in the embodiments) and a simulation unit (simulation unit 152 in the embodiments). The acquisition unit acquires spatial information indicating a first particle as a particle arranged in space and having physical quantities, and a mesh model of a continuum. The simulation unit performs a simulation including projection processing, update processing, and generation processing. In the projection processing, the physical quantities of a spatial grid are projected onto a second particle. The spatial grid is a grid arranged in space. The physical quantities of the first particle are projected onto the grid. The second particle is a particle arranged in the space at a position corresponding to a node of the mesh model. In the update processing, the position of the second particle is updated based on the physical quantities projected onto the second particle. In the generation processing, the mesh model is generated based on the updated position of the second particle.

[0302] According to the information processing apparatus of this disclosure, a mesh model can be appropriately generated by updating the positions corresponding to the nodes of the mesh model and by generating the mesh model using information about particles arranged in space and having physical quantities, a spatial grid, and particles arranged at positions corresponding to the nodes of the mesh model.

[0303] Furthermore, the simulation unit performs a simulation of continuum deformation behavior using a hybrid method that employs a first particle and a spatial grid used as a computational grid. Thus, the information processing device is able to appropriately generate a mesh model by applying methods for generating mesh models, such as MPM.

[0304] Furthermore, the simulation unit performs the simulation using a second particle, which is a virtual particle that does not affect the analysis results. Thus, the information processing device can appropriately generate a mesh model using virtual particles.

[0305] Furthermore, the simulation unit performs the simulation using a spatial grid with uniform or non-uniform grid widths. Thus, the information processing device is able to appropriately generate a mesh model using spatial grids with uniform or non-uniform grid widths.

[0306] Furthermore, the simulation unit changes the grid width of the spatial grid according to the deformation of the continuum indicated by the grid model. Thus, the information processing device can appropriately generate the grid model by changing the grid width of the spatial grid according to the deformation of the continuum indicated by the grid model.

[0307] Furthermore, the simulation unit performs the simulation using a spatial grid comprising one or more layers. Thus, the information processing device is able to appropriately generate a mesh model using a spatial grid comprising one or more layers.

[0308] Furthermore, to represent the interaction with the rigid body, the simulation unit sets up two overlapping spatial grids near the rigid body. The particle's physical quantities are projected onto each of the two overlapping spatial grids using a CDF (Content Formatting Rendering) method, and the physical quantities from each of the two overlapping spatial grids are projected onto the particle. Thus, the information processing device can appropriately generate a mesh model by applying a CDF method used to generate the mesh model.

[0309] Furthermore, the simulation unit increases or decreases the density of the mesh model during the generation process. Thus, the information processing device can appropriately generate a mesh model by outputting a model and increasing or decreasing the mesh model density during the generation process.

[0310] Furthermore, when the mesh of the mesh model has a size larger than a predetermined reference, the simulation elements are meshed. Thus, the information processing device can appropriately generate a mesh model by meshing.

[0311] Furthermore, when multiple meshes obtained through meshing meet predetermined criteria, the simulation element couples the multiple meshes. Thus, the information processing device can appropriately generate a mesh model by recoupled the multiple meshes obtained through meshing.

[0312] Furthermore, when multiple meshes in a mesh model have dimensions that satisfy a predetermined criterion, the simulation element couples multiple meshes. Thus, the information processing device can appropriately generate a mesh model by coupling multiple meshes that satisfy the predetermined criterion.

[0313] [4. Hardware Configuration]

[0314] For example, information devices such as the information processing apparatus 100 and 100A according to the above embodiments are provided with ​ The computer 1000 with the configuration shown is implemented. ​This is a hardware configuration diagram illustrating an example of a computer 1000 that implements the functions of information processing devices such as information processing apparatuses 100 and 100A. An example of the information processing apparatus 100 according to this embodiment will now be described. The computer 1000 includes a CPU 1100, RAM 1200, read-only memory (ROM) 1300, hard disk drive (HDD) 1400, communication interface 1500, and input / output interface 1600. The various units of the computer 1000 are connected via a bus 1050.

[0315] The CPU 1100 operates based on programs stored in ROM 1300 or HDD 1400 and controls these units. For example, the CPU 1100 develops programs stored in ROM 1300 or HDD 1400 on RAM 1200 and performs processing according to various programs.

[0316] ROM 1300 stores boot programs such as the Basic Input / Output System (BIOS) and programs that depend on the hardware of computer 1000, which are executed by CPU 1100 when computer 1000 starts up.

[0317] HDD 1400 is a computer-readable recording medium that non-temporarily records a program executed by CPU 1100, data used by the program, etc. Specifically, HDD 1400 is a recording medium that records an information processing program according to this disclosure. The information processing program is an example of program data 1450.

[0318] Communication interface 1500 connects computer 1000 to external network 1550 (e.g., the Internet). For example, CPU 1100 receives data from another device and sends data generated by CPU 1100 to another device via communication interface 1500.

[0319] Input / output interface 1600 connects input / output device 1650 to computer 1000. For example, CPU 1100 receives data from input devices such as keyboard and mouse via input / output interface 1600. Furthermore, CPU 1100 sends data to output devices such as monitor, speakers, and printer via input / output interface 1600. Additionally, input / output interface 1600 can be used as a media interface for reading programs recorded on a predetermined recording medium. Examples of media include optical recording media such as Digital Universal Disc (DVD) and Phase Change Rewritable Disc (PD), magneto-optical recording media such as magneto-optical disc (MO), magnetic tape, magnetic recording media, and semiconductor memory.

[0320] For example, when the computer 1000 is used as the information processing device 100 according to this embodiment, the CPU 1100 of the computer 1000 implements the functions of the control unit 15, etc., by executing the information processing program loaded on the RAM 1200. Furthermore, the HDD 1400 stores the information processing program and data according to this disclosure in the storage unit 14. Note that the CPU 1100 reads program data 1450 from the HDD 1400 and executes the program data 1450. In another example, the CPU 1100 may obtain these programs from another device via an external network 1550.

[0321] Note that this technology can also have the following configuration.

[0322] (1) An information processing device, comprising:

[0323] The acquisition unit acquires spatial information, which indicates a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and possessing physical quantities; and

[0324] The simulation unit performs a simulation including the following processes: a projection process, which projects the physical quantities of a spatial grid onto a second particle, the spatial grid being a grid arranged in the space, the physical quantities of a first particle being projected onto the grid, and the second particle being a particle arranged in the space at a position corresponding to a node of the mesh model; an update process, which updates the position of the second particle based on the physical quantities projected onto it; and a generation process, which generates the mesh model based on the updated position of the second particle.

[0325] (2) The information processing device according to (1),

[0326] The simulation unit

[0327] The simulation is performed by using a hybrid method of a first particle and a spatial grid used as a computational grid to simulate the deformation behavior of a continuum.

[0328] (3) The information processing device according to (1) or (2),

[0329] The simulation unit

[0330] The simulation is performed using a second particle, which is a virtual particle that does not affect the analysis results.

[0331] (4) The information processing apparatus according to any one of (1) to (3),

[0332] The simulation unit

[0333] The simulation is performed using a spatial grid with uniform or non-uniform grid widths.

[0334] (5) The information processing device according to (4),

[0335] The simulation unit

[0336] The grid width of the spatial grid is changed according to the deformation of the continuum indicated by the grid model.

[0337] (6) The information processing apparatus according to any one of (1) to (5),

[0338] The simulation unit

[0339] The simulation is performed using a spatial grid that includes a single layer or multiple layers.

[0340] (7) The information processing apparatus according to (6),

[0341] In order to represent the interaction with the rigid body, the simulation element

[0342] Two overlapping spatial grids are set near the rigid body. The physical quantities of the particle are projected onto each of the two overlapping spatial grids using the Colored Distance Field (CDF) method, and the physical quantities are projected onto the particle based on the physical quantities of each of the two overlapping spatial grids.

[0343] (8) The information processing apparatus according to (7),

[0344] In order to reduce penetration of rigid bodies, simulation elements

[0345] Two overlapping spatial grids are set near the rigid body. The penalty force of each first particle is calculated using the CDF method and the moving least squares method. The penalty force of each first particle is projected onto each of the two overlapping spatial grids. The physical quantities are projected onto the particles based on the physical quantities of each of the two overlapping spatial grids.

[0346] (9) The information processing device according to (7),

[0347] In order to reduce penetration with rigid bodies, the simulation unit

[0348] Two overlapping spatial grids are set near the rigid body. The penalty force is calculated using the first particle, the CDF method, and the calculation point on the surface of the rigid body located closest to each first particle. The penalty force of each first particle is projected onto each of the two overlapping spatial grids, and physical quantities are projected onto the particle based on the physical quantities of each of the two overlapping spatial grids.

[0349] (10) The information processing apparatus according to any one of (1) to (9),

[0350] The simulation unit

[0351] Increase or decrease the density of the mesh model during the generation process.

[0352] (11) The information processing apparatus according to (10),

[0353] Wherein, when the mesh of the mesh model has a size larger than a predetermined reference, the simulation unit

[0354] Divide the grid.

[0355] (12) The information processing apparatus according to (11),

[0356] Among them, when multiple grids obtained by dividing the grid meet a predetermined reference, the simulation element

[0357] The multiple meshes are coupled.

[0358] (13) The information processing apparatus according to any one of (10) to (12),

[0359] Wherein, when multiple meshes in the mesh model have dimensions that satisfy a predetermined reference, the simulation element is coupled to the multiple meshes.

[0360] (14) An information processing method, comprising:

[0361] Acquire spatial information, which indicates a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and possessing physical quantities; and

[0362] The simulation includes the following processes: a projection process that projects physical quantities of a spatial grid onto a second particle, the spatial grid being a grid arranged in the space, the physical quantities of a first particle being projected onto the grid, and the second particle being a particle arranged in the space at a position corresponding to a node of the mesh model; an update process that updates the position of the second particle based on the physical quantities projected onto it; and a generation process that generates the mesh model based on the updated position of the second particle.

[0363] (15) An information processing program,

[0364] This enables the acquisition of spatial information, which indicates a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and possessing physical quantities; and

[0365] The simulation is performed by: a projection process that projects physical quantities of a spatial grid onto a second particle, the spatial grid being a grid arranged in the space, the physical quantities of a first particle being projected onto the grid, and the second particle being a particle arranged in the space at a position corresponding to a node of the mesh model; an update process that updates the position of the second particle based on the physical quantities projected onto it; and a generation process that generates the mesh model based on the updated position of the second particle.

[0366] List of reference numerals

[0367] 100, 100a Information Processing Device

[0368] 11 Communication Units

[0369] 12 input units

[0370] 13 Display Units (Monitors)

[0371] 14 storage units

[0372] 141 Analog Information Storage Unit

[0373] 142 mesh model information storage units

[0374] 15, 15A control unit

[0375] 151, 151A Acquisition Unit

[0376] 152 simulation units

[0377] 153, 153A Transmitting Unit

Claims

1. An information processing apparatus, comprising: The acquisition unit acquires spatial information, which indicates a first particle and a grid model of a continuum. The first particle is a particle arranged in space and has physical quantities. as well as The simulation unit performs a simulation including the following processes: projection processing, which projects the physical quantities of a spatial grid onto a second particle. The spatial grid is a grid arranged in the space. The physical quantities of the first particle are projected onto the grid. The second particle is a particle arranged in the space at a position corresponding to a node of the mesh model. The update process updates the position of the second particle based on the physical quantities projected onto it; and the generation process generates the mesh model based on the updated position of the second particle.

2. The information processing device according to claim 1, The simulation unit The simulation is performed by using a hybrid method of a first particle and a spatial grid used as a computational grid to simulate the deformation behavior of a continuum.

3. The information processing device according to claim 1, The simulation unit The simulation is performed using a second particle, which is a virtual particle that does not affect the analysis results.

4. The information processing device according to claim 1, The simulation unit The simulation is performed using a spatial grid with uniform or non-uniform grid widths.

5. The information processing apparatus according to claim 4, The simulation unit The grid width of the spatial grid is changed according to the deformation of the continuum indicated by the grid model.

6. The information processing apparatus according to claim 1, The simulation unit The simulation is performed using a spatial grid that includes a single layer or multiple layers.

7. The information processing apparatus according to claim 6, in, To represent the interaction with the rigid body, the simulation element... Two overlapping spatial grids are set near the rigid body. The physical quantities of the particle are projected onto each of the two overlapping spatial grids using the Colored Distance Field (CDF) method, and the physical quantities are projected onto the particle based on the physical quantities of each of the two overlapping spatial grids.

8. The information processing apparatus according to claim 7, in, To reduce penetration into rigid bodies, the simulation element... Two overlapping spatial grids are set near the rigid body. The penalty force of each first particle is calculated using the CDF method and the moving least squares method. The penalty force of each first particle is projected onto each of the two overlapping spatial grids. The physical quantities are projected onto the particles based on the physical quantities of each of the two overlapping spatial grids.

9. The information processing apparatus according to claim 7, in, To reduce penetration with rigid bodies, the simulation element Two overlapping spatial grids are set near the rigid body. The penalty force is calculated using the first particle, the CDF method, and the calculation point on the surface of the rigid body located closest to each first particle. The penalty force of each first particle is projected onto each of the two overlapping spatial grids, and physical quantities are projected onto the particle based on the physical quantities of each of the two overlapping spatial grids.

10. The information processing apparatus according to claim 1, The simulation unit Increase or decrease the density of the mesh model during the generation process.

11. The information processing apparatus according to claim 10, in, When the mesh of the mesh model has a size larger than a predetermined reference, the simulation unit Divide the grid.

12. The information processing apparatus according to claim 11, in, When multiple meshes obtained by mesh division meet a predetermined reference, the simulation element The multiple meshes are coupled.

13. The information processing apparatus according to claim 10, in, When multiple grids in the mesh model have dimensions that satisfy a predetermined reference, the simulation unit... The multiple meshes are coupled.

14. An information processing method, comprising: Acquire spatial information, which indicates a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and has physical quantities; as well as The simulation includes the following processes: projection processing, which projects the physical quantities of a spatial grid onto a second particle, the spatial grid being a grid arranged in the space, the physical quantities of a first particle being projected onto the grid, and the second particle being a particle arranged in the space at a position corresponding to a node of the mesh model. The update process updates the position of the second particle based on the physical quantities projected onto it; and the generation process generates the mesh model based on the updated position of the second particle.

15. An information processing program, This enables the acquisition of spatial information, which indicates a first particle and a mesh model of a continuum, wherein the first particle is a particle arranged in space and has physical quantities. as well as This enables the simulation to perform the following processes: projection processing, which projects the physical quantities of a spatial grid onto a second particle, the spatial grid being a grid arranged in the space, the physical quantities of a first particle being projected onto the grid, and the second particle being a particle arranged in the space at a position corresponding to a node of the mesh model. The update process updates the position of the second particle based on the physical quantities projected onto it; and the generation process generates the mesh model based on the updated position of the second particle.

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