Microscopic modeling method and system for obstacle avoidance of chopped fiber reinforced cementitious composite

Abstract

The application provides a short-cut fiber reinforced cement-based composite material microscopic modeling method and system for obstacle avoidance, which comprises the following steps: constructing a microscopic geometric model of the short-cut fiber reinforced cement-based composite material, and setting parameter information and obstacle information of the microscopic geometric model; performing grid division on the microscopic geometric model based on the parameter information to form grid units; constructing a double-space grid index based on the grid units and the obstacle information; performing space division on the microscopic geometric model to generate a subspace region; cyclically executing fiber generation, collision detection based on the double-space grid index and fiber registration in the subspace region; and outputting basic information of the registered fiber into a geometric model file in a preset file format. The microscopic geometric model generated by the microscopic modeling method provides an accurate geometric basis for further defining material performance and interface parameters in finite element software and completing numerical simulation of mechanical performance of the short-cut fiber reinforced cement-based composite material.

Description

Technical Field

[0001] This application relates to the fields of computer-aided engineering and digital design technology of composite materials, specifically to a method and system for microscopic modeling of short-cut fiber reinforced cementitious composite materials for obstacle avoidance. Background Technology

[0002] Short-cut fiber reinforced cementitious composites are a class of high-performance building materials formed by uniformly incorporating randomly distributed short-cut fibers into a cement matrix. Engineering cement-based composites (ECC) and ultra-high performance concrete (UHPC) are typical examples. These materials significantly improve the bridging, crack-resistant, and toughening effects of fibers, significantly reducing the brittleness of traditional cement-based materials. They endow these materials with high ductility, high damage resistance, and excellent impact and fatigue resistance, and are widely used in civil engineering fields such as structural reinforcement and repair, seismic joints, precast components, bridge deck pavement, and military protective engineering.

[0003] Accurate prediction and optimization of the mechanical properties of such materials rely heavily on numerical models that can realistically reflect their internal microstructure. A complete microscopic numerical simulation includes two key steps: first, generating a high-fidelity microscopic geometric model to accurately characterize the three-dimensional spatial distribution and orientation of chopped fibers in the matrix, as well as the geometric interactions between fibers and other reinforcing phases in the matrix (such as coarse aggregates, wire mesh, and fiber woven mesh); and second, based on this geometric model, assigning corresponding material constitutive models and performance parameters to each component (fibers, matrix, and interface) in finite element analysis software to perform mechanical response analysis.

[0004] Regarding the first key technical issue—the efficient and accurate generation of mesoscopic geometric models—existing random sequence fiber generation methods for such models suffer from the following technical shortcomings: First, insufficient obstacle avoidance capability. In composite materials containing multiple reinforcing phases, numerous obstacle regions exist. Existing methods struggle to effectively avoid these regions during fiber generation, leading to non-physical geometric penetration between fibers and obstacles, causing model distortion and severely impacting the accuracy of subsequent mechanical analysis. Second, low generation efficiency. In complex scenarios with high fiber density and dense obstacles, traditional generation methods... Detection The "discard" method requires a massive number of attempts to successfully generate an effective fiber that does not intersect with existing fibers or obstacles, resulting in huge computational costs and making it difficult to meet the needs of engineering applications for large-scale fiber generation models. Furthermore, there is an imbalance between collision detection accuracy and efficiency, and the lack of an efficient spatial indexing structure to handle large-scale collision detection problems between fibers and between fibers and obstacles makes it difficult to meet the requirements of practical applications for generation speed while ensuring geometric accuracy.

[0005] A literature search of existing technologies revealed that Chinese patent CN115329642A proposes a parametric microstructure modeling method, equipment, and storage medium for fiber-reinforced concrete. The parametric modeling is achieved using a command flow approach. By modifying the parameters, different types of fiber-reinforced concrete models, such as test blocks of different sizes, fiber parameters, and fiber content, as well as hybrid fiber models, can be established. However, the above microstructure modeling method cannot effectively avoid obstacles.

[0006] Therefore, there is an urgent need for a method to generate micro-geometric models that can efficiently and accurately construct micro-geometric models with obstacle avoidance capabilities. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this application is to provide a microscopic modeling method and system for chopped fiber reinforced cementitious composite materials for obstacle avoidance, thereby solving the technical problems of low fiber generation efficiency and inaccurate obstacle avoidance in complex obstacle environments.

[0008] According to the first aspect of this application, a method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance is provided, comprising: A microscopic geometric model of short-cut fiber reinforced cementitious composite material is constructed, and the parameter information and obstacle information of the microscopic geometric model are set. The micro-geometric model is meshed based on the parameter information to form mesh cells; a dual spatial mesh index is constructed based on the mesh cells and obstacle information, the dual spatial mesh index includes a first spatial mesh index for managing fiber spatial distribution and a second spatial mesh index for managing obstacle spatial distribution; The microscopic geometric model is spatially divided to generate at least one subspace region; fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration are performed iteratively within each subspace region. Output the basic information of the registered fibers as a geometric model file in a preset file format.

[0009] Optionally, the parameter information of the micro-geometric model includes three-dimensional size parameters, chopped fiber length, mesh scaling factor, and fiber angle group distribution parameters. The fiber angle group distribution parameters include at least one set of preset fiber angle groups and their corresponding fiber proportions and angle ranges. The obstacle information includes the spatial coordinate information of each obstacle and its obstacle identifier. The obstacles include at least one of reinforcing particles, fiber woven mesh, and wire mesh.

[0010] Optionally, the step of meshing the micro-geometric model based on the parameter information to form mesh cells; and constructing a dual-space mesh index based on the mesh cells and obstacle information, including: The product of the chopped fiber length and the mesh scaling factor is used as the mesh size to divide the micro-geometric model into mesh cells. Construct a first spatial grid index, which is based on the divided grid cells and is used to associate the fiber identifier to be registered with the corresponding fiber spatial coordinates; A second spatial grid index is constructed, which is based on the divided grid cells and is used to associate obstacle identifiers and corresponding obstacle spatial coordinate information.

[0011] Optionally, constructing the second spatial grid index includes: For each obstacle, obtain all the mesh cells covered by its three-dimensional geometry; Register the obstacle's obstacle identifier to every grid cell covered by its three-dimensional geometry.

[0012] Optionally, the step of spatially partitioning the mesoscopic geometric model to generate at least one subspace region; and cyclically performing fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration within each subspace region, includes: According to the preset volume division rules, the microscopic geometric model is divided into several non-overlapping subspace regions; Based on the preset total number of fibers and the volume ratio of each subspace region, the target number of a certain fiber angle group in each subspace region is obtained. The target number of a certain fiber angle group in the subspace region is the product of the total number of fibers, the volume ratio of the subspace region and the fiber ratio of the certain fiber angle group. Based on the fiber angle group, a corresponding fiber is generated, and the spatial node coordinates of the fiber are determined. The spatial node coordinates include the coordinates of the center point and the coordinates of both ends of the fiber. Collision detection is performed based on the spatial node coordinates of fibers and dual spatial grid indexes to obtain non-collision fibers; After dividing the non-collision fibers, register them into the grid cells corresponding to the dual spatial grid index; The fiber generation, collision detection, and registration steps described above are repeated until the number of registered fibers in each fiber angle group in each of the subspace regions reaches the target number of fibers.

[0013] Optionally, generating corresponding fibers based on the fiber angle set and determining the spatial node coordinates of the fiber includes: Set the length and center point coordinates of the fiber to be generated; The generation direction (ang_a, ang_b) of the fiber to be generated is determined. The generation direction (ang_a, ang_b) is determined based on the angle range [min_deg, max_deg] of the fiber angle group, where ang_a is the angle between the fiber to be generated and the Z-axis, and ang_a is randomly selected within the angle range [min_deg, max_deg]. ang_b is the rotation angle of the fiber to be generated around the Z-axis, and ang_b is randomly selected within the range [0, 360°). A single fiber is generated based on the set length, center point coordinates, and generation direction, and the spatial node coordinates of the generated fiber are determined.

[0014] Optionally, the collision detection based on the spatial node coordinates of the fiber and the dual spatial grid index to obtain non-collision fibers includes: First collision detection: Calculate the axial bounding box of the newly generated fiber based on the spatial node coordinates, and take the grid cell covered by the axial bounding box of the newly generated fiber as the designated grid cell; obtain all obstacle identifiers registered in the designated grid cell from the second spatial grid index to form a candidate obstacle set; The Separation Axis Theorem algorithm is used to project the newly generated fiber and the bounding boxes of obstacles in the candidate obstacle set onto the three coordinate axes of the mesoscopic geometric model coordinate system, respectively, to obtain the projection intervals of the newly generated fiber and the bounding boxes of obstacles along the three coordinate axes. If the projection interval of the newly generated fiber is separated from the projection interval of the bounding box of the obstacle along any coordinate axis, it is determined that the newly generated fiber has not collided with the obstacle; otherwise, it is determined that there is a potential collision risk between the newly generated fiber and the obstacle. All obstacles in the candidate obstacle set are traversed. If any obstacle in the candidate obstacle set has a potential collision risk with the newly generated fiber, the newly generated fiber is discarded; otherwise, the second collision detection is performed. Second collision detection: Obtain all fiber identifiers registered within the specified grid cell from the first spatial grid index to form a candidate fiber set; sequentially perform axial bounding box intersection detection, coplanarity check, and geometric intersection detection on the newly generated fiber and a registered fiber in the candidate fiber set; if the newly generated fiber and the registered fiber satisfy the corresponding collision determination conditions in the above axial bounding box intersection detection, coplanarity check, and geometric intersection detection, it is determined that the newly generated fiber and the registered fiber have a fiber-to-fiber collision; otherwise, it is determined that the newly generated fiber and the registered fiber have not a fiber-to-fiber collision; traverse all registered fibers in the candidate fiber set; if any registered fiber in the candidate fiber set has a fiber-to-fiber collision with the newly generated fiber, discard the newly generated fiber; otherwise, treat the newly generated fiber as a non-collision fiber.

[0015] Optionally, the step of dividing and registering non-collision fibers into the grid cells corresponding to the dual spatial grid index includes: Assign a unique fiber identifier to each non-collision fiber; The non-collision fibers are divided into several fiber sub-units, and interpolation point coordinates are generated. The interpolation points are the endpoints of each fiber sub-unit. The endpoints and interpolation points of the fiber before division are used as nodes. A mapping relationship between each node and the fiber subunit is created. The fiber identifier, all nodes contained in the fiber and the information of the corresponding fiber subunit are used as the basic information of the fiber. Based on the coordinates of all nodes of the fiber, the grid cell to which each node belongs is determined, and the basic information of the fiber is registered and written into the first spatial grid index of the corresponding grid cell to complete the update of the first spatial grid index.

[0016] According to a second aspect of this application, a microstructure modeling system for chopped fiber reinforced cementitious composite materials with obstacle avoidance is provided, comprising: A configuration module is used to construct a micro-geometric model of chopped fiber reinforced cementitious composite material and to set the parameter information and obstacle information of the micro-geometric model. An initialization module is used to divide the micro-geometric model into meshes based on the parameter information to form mesh cells; and to construct a dual spatial mesh index based on the divided mesh cells and obstacle information, wherein the dual spatial mesh index includes a first spatial mesh index for managing fiber spatial distribution and a second spatial mesh index for managing obstacle spatial distribution. The fiber generation engine module is used to spatially divide the microscopic geometric model to generate at least one subspace region; and to perform fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration cyclically within each of the subspace regions. The output module is used to output the basic information of the registered fibers as a geometric model file in a preset file format.

[0017] According to a third aspect of this application, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance.

[0018] This application provides a microscopic modeling method for chopped fiber reinforced cementitious composite materials with obstacle avoidance. It constructs a dual spatial mesh index using parameter and obstacle information, and performs collision detection during fiber generation and registration. This enables rapid screening and precise location of fiber-obstacle collisions, effectively solving the problem of fiber intrusion into obstacle areas. It maintains a high generation speed even in high fiber density and complex obstacle environments. This application, through dual spatial mesh indexing and collision detection, solves the technical problems of low generation efficiency and inaccurate obstacle avoidance of chopped fibers in complex obstacle environments, achieving efficient and accurate automatic generation of fiber models. It is widely applicable to the microscopic numerical simulation of chopped fiber reinforced cementitious high-performance structural materials such as fiber-reinforced high-performance cement mortar, engineering cementitious composite materials, and ultra-high performance concrete.

[0019] Other technical effects resulting from the additional features will be further illustrated in the corresponding embodiments. Attached Figure Description

[0020] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a microscopic modeling method in one embodiment of this application; Figure 2 This is an overall flowchart of the microstructure modeling method for short-cut fiber reinforced cementitious composite materials in one embodiment of this application; Figure 3 This is a schematic diagram illustrating the construction principle of a spatial grid index according to an embodiment of this application; Figure 4 In one embodiment of this application, fiber is based on spatial grid indexing. A schematic diagram of obstacle detection; Figure 5 In one embodiment of this application, the split-axis theorem is used for fiber processing. A schematic diagram illustrating the principle of precise obstacle collision detection; Figure 6 This is a schematic diagram of the geometric model for fiber orientation control in one embodiment of this application; Figure 7 This is a schematic diagram of the geometric model in one embodiment of this application; Figure 8 This is a rendering of the generated geometric model in one embodiment of this application; Figure 9 This is a schematic diagram of a microscopic modeling system in one embodiment of this application. Detailed Implementation

[0021] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all fall within the protection scope of the present application. Parts not described in detail in the following embodiments can be implemented using existing technology.

[0022] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0023] Short-cut fiber reinforced cementitious composites are a class of high-performance building materials formed by uniformly incorporating randomly distributed short-cut fibers into a cement matrix. Accurate prediction and optimization of the mechanical properties of these materials heavily rely on numerical models that can realistically reflect their internal microstructure. Existing numerical models suffer from insufficient obstacle avoidance capabilities, leading to model distortion and severely impacting the accuracy of subsequent mechanical analyses, while also exhibiting low generation efficiency. To address these issues, this application provides a method for microstructure modeling of short-cut fiber reinforced cementitious composites with obstacle avoidance capabilities, thereby resolving the aforementioned problems.

[0024] Reference Figure 1 and Figure 2 As shown in the figure, this application provides a method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance, including: S1. Construct a microscopic geometric model of the short-cut fiber reinforced cementitious composite material, and set the parameter information and obstacle information of the microscopic geometric model; S2. Based on the parameter information, the micro-geometric model is meshed to form mesh cells; based on the mesh cells and obstacle information, a dual spatial mesh index is constructed, wherein the dual spatial mesh index includes a first spatial mesh index for managing the spatial distribution of fibers and a second spatial mesh index for managing the spatial distribution of obstacles; S3. Divide the micro-geometric model into spatial regions to generate at least one subspace region; perform fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration in each subspace region in a loop. S4. Output the basic information of the registered fibers as a geometric model file in a preset file format.

[0025] For example, basic information about registered fibers can be output in a geometric model file format recognizable by finite element analysis software. The length of chopped fibers ranges from 3mm to 50mm. Meshing can construct micro-mesh elements for spatial indexing; spatial partitioning can divide the meso-geometric model into macro-computational subdomains for fiber registration generation.

[0026] The embodiments described above in this application construct a dual spatial mesh index using parameter information and obstacle information, and perform collision detection during fiber generation and registration. This enables rapid screening and precise positioning of fiber-obstacle collisions, effectively solving the problem of fiber intrusion into obstacle areas. Even in high fiber density and complex obstacle environments, it maintains a high generation speed. The mesoscopic geometric model file generated by this method provides high-quality geometric input for further material and interface property definition and mechanical simulation in finite element software, clarifying its fundamental and preliminary role in the complete numerical simulation process. This application, through dual spatial mesh indexing and collision detection, solves the technical problems of low generation efficiency and inaccurate avoidance of chopped fibers in complex obstacle environments, achieving efficient and accurate automatic generation of fiber models. It is widely applicable to the mesoscopic numerical simulation of chopped fiber reinforced cement-based high-performance structural materials such as fiber-reinforced high-performance cement mortar, engineering cement-based composites (ECC), and ultra-high performance concrete (UHPC).

[0027] In some specific embodiments of this application, the parameter information of the mesoscopic geometric model includes three-dimensional size parameters, chopped fiber length, mesh scaling factor, and fiber angle group distribution parameters. The fiber angle group distribution parameters include at least one set of preset fiber angle groups and their corresponding fiber proportions and angle ranges. The obstacle information includes the spatial coordinate information of each obstacle and its obstacle identifier. The obstacles include at least one of reinforcing particles, fiber woven mesh, and wire mesh.

[0028] In the embodiments described above, when constructing the microscopic geometric model, a three-dimensional Cartesian coordinate system is defined for the model (the X, Y, and Z axes are mutually perpendicular, providing a unified coordinate reference for fiber spatial position, angle, and collision detection). The three-dimensional dimensional parameters are the model's length L', width W', and height H'; the spatial coordinate information of the obstacle includes its minimum and maximum coordinate values ​​in the model's three-dimensional coordinate system to define the spatial occupancy range of the obstacle.

[0029] In some specific embodiments of this application, the mesoscopic geometric model is meshed based on parameter information to form mesh cells; based on the mesh cells and obstacle information, a dual spatial mesh index is constructed, which may further include: S21. Use the product of the chopped fiber length and the mesh scaling factor as the mesh size to mesh the mesoscopic geometric model and form mesh elements. S22. Construct a first spatial grid index. The first spatial grid index is based on the divided grid cells and is used to associate the fiber identifier to be registered with the corresponding fiber spatial coordinates. S23. Construct a second spatial grid index. The second spatial grid index is based on the divided grid cells and is used to associate obstacle identifiers and corresponding obstacle spatial coordinate information.

[0030] The above embodiments of this application refer to... Figure 3 As shown, parameter information and obstacle information are used to configure and initialize the model parameters, and the parameters are configured according to the chopped fiber length l. f The grid size is obtained by using the grid scaling factor 's', where Size_Grid = l f ×s divides the micro-geometric model space into three-dimensional mesh cells with the mesh division size as the side length. Then, the divided three-dimensional mesh cells are used to construct a first spatial mesh index for managing the spatial distribution of fibers and a second spatial mesh index for managing the spatial distribution of obstacles.

[0031] In some specific embodiments of this application, constructing a second spatial grid index for managing the spatial distribution of obstacles may further include: S231. For each obstacle, obtain all the mesh cells covered by its three-dimensional geometry; S232. Register the obstacle identifier of the obstacle to each grid cell covered by its three-dimensional geometry.

[0032] In some specific embodiments of this application, the mesoscopic geometric model is spatially partitioned to generate at least one subspace region; fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration are cyclically performed within each subspace region, which may further include: S31. According to the preset volume division rules, the microscopic geometric model is divided into several non-overlapping subspace regions. S32. According to the preset total number of fibers and the volume ratio of each subspace region, the target number of a certain fiber angle group in each subspace region is obtained. The target number of a certain fiber angle group in a subspace region is the product of the total number of fibers, the volume ratio of the subspace region, and the fiber ratio of the certain fiber angle group. S33. Generate the corresponding fiber based on the fiber angle group, and determine the spatial node coordinates of the fiber. The spatial node coordinates include the coordinates of the center point and the coordinates of both ends of the fiber. S34. Collision detection is performed based on the spatial node coordinates of the fiber and the dual spatial grid index to obtain the non-collision fibers; S35. After dividing the non-collision fibers, register them to the grid cells corresponding to the dual spatial grid index; S36. Repeat the above fiber generation, collision detection and registration steps until the number of registered fibers in each fiber angle group in each subspace region reaches the target number of fibers.

[0033] For example, the preset volume partitioning rule can be set as follows: the mesoscopic geometry model is divided into twelve regions, including two end regions on the left and right and ten sub-regions in the middle, but it is not limited to this. Based on the preset fiber angle group and the total number of fibers, the fiber generation task is allocated to multiple sub-space regions according to the volume proportion, and fiber generation, collision detection, and registration are performed iteratively within each sub-space region. Collision detection includes fiber generation based on the second spatial mesh index. Obstacle collision detection and fiber based on first spatial grid index Before generating fibers and performing collision detection, the fiber boundary validity must be checked to ensure that the fiber endpoints are within the model range. If the fiber endpoints are not within the model range, the fiber is discarded.

[0034] The embodiments described above employ an overall technical framework of "dual spatial mesh indexing, partition generation, and accurate collision detection." The determination of the target fiber quantity is as follows: Calculate the total fiber allocation for each subspace region: Total fiber allocation = Preset total fiber quantity × Subspace region volume percentage = Preset total fiber quantity × (Subspace region volume / Total volume of the mesoscopic geometry model); Calculate the target fiber quantity for a specific fiber angle group: Target fiber quantity = Total fiber allocation for the subspace region × Fiber percentage of that fiber angle group. Within the same subspace region, the sum of the fiber percentages of all fiber angle groups is 100%, and the sum of the subspace region's volume percentages is 100%, ensuring that the sum of the target fiber quantities for all fiber angle groups within each subspace region equals the total fiber allocation for that subspace region.

[0035] In some specific embodiments of this application, generating corresponding fibers based on fiber angle groups and determining the spatial node coordinates of the fibers may further include: S331. Set the length and center point coordinates of the fiber to be generated; S332. Determine the generation direction (ang_a, ang_b) of the fiber to be generated. The generation direction (ang_a, ang_b) is determined based on the angle range [min_deg, max_deg] of the fiber angle group, where ang_a is the angle between the fiber to be generated and the Z-axis, and ang_a is randomly selected within the angle range [min_deg, max_deg]. ang_b is the rotation angle of the fiber to be generated around the Z-axis, and ang_b is randomly selected within the range [0, 360°). It should be noted that within the angle definition range, 0° and 360° are equivalent angles of the same starting and ending boundary. To avoid repeated values ​​at the endpoints of the interval, the above angle range of this application is limited to the left-closed and right-open interval [0, 360°].

[0036] S333. Generate a single fiber based on the set length, center point coordinates and generation direction, and determine the spatial node coordinates of the generated fiber.

[0037] For example, the fiber orientation is determined by the angle pair (ang_a, ang_b), where ang_a is randomly generated within a preset angle range of [min_deg, max_deg], and the axial positive and negative directions are randomly determined by multiplying by a random power of (-1), which is used to control the angle between the fiber and the Z-axis; ang_b is randomly generated within the range of [0, 360°), which is used to control the rotation angle of the fiber around the Z-axis. When ang_a is 0° or 180°, the value of ang_b has no effect on the fiber orientation, and the fiber can be directly generated according to the Z-axis direction.

[0038] Reference Figure 6 The fiber generation process is as follows: 1) Set the length L of the fiber to be generated and the coordinates of the center point (xi, yi, zi), where the coordinates of the center point are randomly generated within the subspace region where the fiber is generated; 2) Generate random angles, where ang_a is determined by multiplying by a random power of (-1) to determine the positive and negative directions of the axis, and random() is a random number generation function; 3) Obtain the spatial node coordinates of the fiber. The formula for calculating the spatial node coordinates of the fiber is as follows: The included angle in radians is rd_ang_a = rad(90 + ang_a). Rotation angle in radians: rd_ang_b = rad(ang_b) X-axis component dx = 0.5 × L × cos(rd_ang_a) × cos(rd_ang_b) Y-axis component dy = 0.5 × L × cos(rd_ang_a) × sin(rd_ang_b) Z-axis component dz = 0.5 × L × sin(rd_ang_a) Endpoint A=(xi+dx, yi+dy, zi+dz), endpoint B=(xi-dx, yi-dy, zi-dz) 4) Output the fiber line segment ptA→ptB using the coordinates of endpoint A and endpoint B.

[0039] In some specific embodiments of this application, collision detection based on the spatial node coordinates of the fiber and a dual spatial grid index to obtain non-collision fibers may further include: S341, First collision detection: Calculate the axial bounding box of the newly generated fiber based on the spatial node coordinates, and use the mesh cells covered by the axial bounding box of the newly generated fiber as the specified mesh cells; S342. Obtain all obstacle identifiers registered within a specified grid cell from the second spatial grid index to form a candidate obstacle set; S343. Using the Separated Axis Theorem algorithm, project the bounding boxes of the newly generated fiber and the obstacles in the candidate obstacle set onto the three coordinate axes of the mesoscopic geometric model coordinate system to obtain the projection intervals of the newly generated fiber and the obstacle's axial bounding box on the three coordinate axes respectively. If the projection interval of the newly generated fiber is separated from the projection interval of the obstacle's axial bounding box in any coordinate axis direction, it is determined that the newly generated fiber has not collided with the obstacle; otherwise, it is determined that the newly generated fiber has a potential collision risk with the obstacle. Traverse all obstacles in the candidate obstacle set. If any obstacle in the candidate obstacle set has a potential collision risk with the newly generated fiber, discard the newly generated fiber; otherwise, proceed to the second collision detection. S344, Second collision detection: Obtain all fiber identifiers registered within a specified grid cell from the first spatial grid index to form a candidate fiber set; S345. Perform axial bounding box intersection detection, coplanarity check, and geometric intersection detection sequentially on the newly generated fiber and a registered fiber in the candidate fiber set. If the newly generated fiber and the registered fiber satisfy the corresponding collision determination conditions in the above axial bounding box intersection detection, coplanarity check, and geometric intersection detection, it is determined that the newly generated fiber and the registered fiber have collided. Otherwise, it is determined that the newly generated fiber and the registered fiber have not collided. Traverse all registered fibers in the candidate fiber set. If any registered fiber in the candidate fiber set has collided with the newly generated fiber, discard the newly generated fiber. Otherwise, treat the newly generated fiber as a non-collision fiber.

[0040] For example, the three coordinate axes of the micro-geometric model coordinate system are specifically the X-axis, Y-axis, and Z-axis directions in the preset three-dimensional rectangular coordinate system of the micro-geometric model.

[0041] The first collision detection is based on the second spatial grid index for fiber detection. Collision detection is performed on obstacles. In the first collision detection, if the projection area of ​​the fiber is separated from the projection area of ​​the obstacle's axial bounding box in any coordinate axis direction, it is determined that the fiber and the obstacle have not collided. If they are not separated in all coordinate axis directions, it is determined that the fiber and the obstacle have a potential collision risk. If any obstacle in the candidate obstacle set has a potential collision risk with the newly generated fiber, the newly generated fiber is discarded. If all obstacles have no collision, the second collision detection is performed. The second collision detection is based on the first spatial grid index for fiber detection. Fiber collision detection; In the second collision detection, for each registered fiber in the candidate fiber set, axial bounding box intersection detection, coplanarity check, and geometric intersection detection are performed sequentially. If any registered fiber passes all three tests, it is determined to be a fiber collision, and the newly generated fiber is discarded; if all registered fibers fail the three tests, it is determined to be a fiber non-collision, and the newly generated fiber is registered as a non-collision fiber. Specifically, the registered fibers and the newly generated fibers sequentially perform the following progressive collision detection: Axial bounding box intersection detection: Detects whether the axial bounding box of the newly generated fiber intersects with that of the currently registered fiber; if the two axial bounding boxes do not intersect, the subsequent detection of the registered fiber is terminated, and it is determined that the registered fiber and the newly generated fiber have not collided; if the two axial bounding boxes intersect, the axial bounding box intersection detection is passed, and the next detection step is performed.

[0042] Coplanarity check: Detects whether the newly generated fiber is coplanar with the currently registered fiber; if they are not coplanar, the subsequent testing of the registered fiber is terminated, and it is determined that the registered fiber and the newly generated fiber have not collided; if they are coplanar, the coplanarity check is passed and the next step of testing is carried out.

[0043] Precise geometric intersection detection: Perform geometric intersection calculations on coplanar newly generated fibers and currently registered fibers to detect whether there is an actual geometric intersection between them; if there is no geometric intersection, it is determined that the registered fiber and the newly generated fiber have not collided; if there is a geometric intersection, it is determined that the registered fiber and the newly generated fiber have passed all three detections and that a fiber-to-fiber collision has occurred.

[0044] Reference Figure 4 and Figure 5 As shown, fibers The obstacle collision detection process is as follows: 1) Obtain the maximum coordinates (x_max, y_max, z_max) and minimum coordinates (x_min, y_min, z_min) of the newly generated fiber axial bounding box; 2) Calculate the grid index range (x_start, x_end; y_start, y_end; z_start, z_end) 3) Traverse the grid cells to obtain all obstacle identifiers (IDs) and form a candidate obstacle set N; 4) The SAT algorithm based on the separation axis theorem is used to obtain the collision detection results between fibers and obstacles. The specific implementation of the separation axis theorem includes: calculating the direction vector and length of the fiber segment; calculating the center point and half-side length of the obstacle; performing projection detection in the three coordinate axis directions to determine whether separation has occurred, which may further include: 41) Input fiber segment=[ptA,ptB], determine fiber direction vector seg_dir and its magnitude seg_length, where seg_dir=ptB-ptA; 42) Determine the obstacle coordinates (min, max), the obstacle half length half_size = (max – min) / 2, the obstacle center center = (max+min) / 2; the projections of the obstacle half length half_size on the X-axis, Y-axis, and Z-axis are half_size.x, half_size.y, and half_size.z, respectively; 43) Calculate the geometric parameters of the fiber segment: the midpoint of the segment seg_mid = (ptA + ptB) / 2, the half vector of the segment seg_half = seg_mid - ptA; the projections of the half vector seg_half onto the X-axis, Y-axis, and Z-axis are seg_half.x, seg_half.y, and seg_half.z, respectively. 44) Calculate the central difference vector diff = seg_mid-center, and the projections of the central difference vector diff onto the X-axis, Y-axis, and Z-axis are diff.x, diff.y, and diff.z, respectively; 45) Perform projection detection in three coordinate axes: X-axis direction: projection_x = |diff.x|, r_x=half_size.x+|seg_half.x|; If projection_x > r_x, then they will separate and not intersect in the X-axis direction; Y-axis direction: projection_y = |diff.y|, r_y=half_size.y+|seg_half.y|; If projection_y > r_y, then they will separate and not intersect in the Y-axis direction; Z-axis direction: projection_z = |diff.z|, r_z=half_size.z+|seg_half.z|; If projection_z > r_z, then they will separate and not intersect in the Z-axis direction; If the fiber separates in any of the X, Y, or Z coordinate axes, it is determined that the fiber did not collide with the obstacle; if the fiber does not separate in any of the three coordinate axes, it is determined that the fiber collided with the obstacle.

[0045] 46) If any obstacle in the candidate obstacle set N collides with the fiber, then the fiber is determined. If obstacle collision detection fails, discard the fiber; if no obstacles are collided, pass the fiber. Obstacle collision detection.

[0046] The embodiments described above in this application possess highly efficient obstacle avoidance capabilities: by establishing a second spatial mesh index specifically for obstacles, rapid screening and precise localization of fiber-obstacle collision detection are achieved, generating a geometric model that is more physically realistic; they also exhibit significant improvements in generation efficiency: the dual spatial mesh index structure reduces the complexity of global collision detection from O(n²) to near O(n), and the partitioned generation strategy further optimizes task allocation, greatly reducing unnecessary computation, maintaining a high generation speed even in environments with high fiber density and complex obstacles; and they possess high precision and reliability: the split-axis theorem is used for fiber... Accurate obstacle collision detection, combined with AABB bounding box and precise geometry detection for fiber optics. Fiber collision detection ensures that the generated fiber geometry model is free of intersections and avoids obstacles, resulting in high reliability and meeting the accuracy requirements of engineering analysis. It also has versatility and flexibility: the method has no specific restrictions on the shape, number, and distribution of obstacles, and can be applied to the modeling needs of various composite material microstructures, such as avoiding complex scenarios like reinforcing particles, fiber woven mesh, and wire mesh.

[0047] In some specific embodiments of this application, non-collision fibers are divided and registered into the mesh cells corresponding to the dual spatial mesh index, including: S351. Assign a unique fiber identifier to each non-collision fiber. S352. Divide the non-collision fibers into several fiber sub-units, generate interpolation point coordinates, and the interpolation points are the endpoints of each fiber sub-unit. S353. Take the endpoints and interpolation points of the fiber before division as nodes, create a mapping relationship between each node and fiber subunit, and take the fiber identifier, all nodes contained in the fiber and the information of the corresponding fiber subunit as the basic information of the fiber. S354. Based on the coordinates of all nodes of the fiber, determine the grid cell to which each node belongs, and register the basic information of the fiber into the first spatial grid index of the corresponding grid cell to complete the update of the first spatial grid index.

[0048] In the above embodiments of this application, the generated fiber node coordinates and fiber sub-unit connection relationships can be output in a geometric model file format that can be recognized by finite element analysis software in subsequent steps. This geometric model file can be used as input for defining material properties and interface parameters in the finite element software to complete a complete numerical simulation of mechanical properties. Detailed generation logs are generated, including the number of each angle group generated, regional distribution statistics, and generation time.

[0049] The following examples and comparative examples will be used to further illustrate this application in order to better understand the above-mentioned technical solutions. It should be understood that the following are only some examples and are not intended to limit this application.

[0050] Application Example 1: Generation of Microscopic Geometric Model of Short-Cut Fiber Reinforced Cement-Based Composite Materials Taking the generation of a microscopic geometric model of a chopped fiber reinforced cementitious composite material containing 45 cuboid obstacles as an example, this simulation addresses the widespread application of fiber-reinforced ECC / UHPC systems in practical engineering. In this system, the fiber-reinforced mesh, as the reinforcing phase, can be considered as obstacles to be avoided in the microscopic model. This application example simulates the effective avoidance of these fiber-reinforced meshes by the chopped fibers within the matrix. The geometric model generated in this application example will serve as the geometric basis for subsequent material property definition, interface parameter setting, and mechanical property simulation in finite element software (such as Abaqus).

[0051] Step 1: Define the specimen dimensions as 190 mm × 100 mm × 24 mm (length × width × height) in the configuration file, with a chopped fiber length of 18 mm and a mesh scaling factor of 1.0. Define the fiber distribution for 7 fiber angle groups, including 0°, 15°, 30°, 45°, 60°, 75°, and 90° fiber angle groups, totaling 120,000 fibers. Simultaneously, define the minimum and maximum coordinates of 45 cuboid obstacles to simulate a bidirectional reinforced fiber woven mesh.

[0052] Step 2: After the program starts, it reads the configuration parameters and calculates the grid size: Size_Grid = 18.0 × 1.0 = 18.0. It constructs an empty first spatial grid index (for fiber management) and a second spatial grid index (for obstacle management). It iterates through all obstacles, calculates the range of grid cells covered by each obstacle, and registers its obstacle identifier to the corresponding grid cell, completing the construction of the second spatial grid index.

[0053] Step 3: Divide the model space into 12 sub-regions, including two end regions (size: 20 mm × 100 mm × 24 mm) and 10 middle sub-regions (size: 15 mm × 100 mm × 24 mm). Generate fibers sequentially according to fiber angle groups. For each fiber angle group, first calculate the number of target fibers allocated based on the volume ratio of each sub-region. Taking the 0° angle group as an example, 10,000 fibers are generated. In the first sub-region, 789 fibers are generated. The following steps are then performed iteratively within this region: Fiber generation: Randomly generate the coordinates of the fiber center point within the sub-region, generate the fiber direction based on the 0° angle range [0°, 7.5°], and calculate the coordinates of the two endpoints of the fiber.

[0054] Obstacle detection: The 3D bounding box of the fiber is calculated, and the set of candidate obstacles that may intersect is quickly located using a second spatial grid index. The separating axis theorem algorithm is used to sequentially determine whether the fiber segment intersects with each obstacle in the candidate obstacle set. If an intersection with any obstacle is detected, the current fiber is discarded and regenerated.

[0055] Inter-fiber detection: The set of existing fibers that may intersect is obtained through the first spatial grid index. First, AABB bounding box fast detection is performed to eliminate obviously non-intersecting fibers. Then, coplanarity checks are performed on the remaining candidate fibers. Finally, precise geometric intersection detection is performed on the fibers that pass the coplanarity check.

[0056] Fiber Registration: After all tests are passed, the fiber is divided into a specified number of sub-cells, and node coordinates and cell connection relationships are generated. The fiber information is stored in a data list and registered to all relevant grid cells in the first spatial grid index.

[0057] Step 4: After all fibers have been generated, write the node coordinates and element connection information into an Abaqus .inp input file. This .inp file contains complete geometric information of the model. Users can then further define the material property parameters of fibers, matrix, obstacles, and interfaces in Abaqus software based on this information, and then conduct numerical simulation analysis of mechanical properties. Simultaneously, a detailed log file is generated, recording statistical information such as the actual number of fibers generated for each angle group, the distribution of each region, and the total generation time.

[0058] This application example successfully generated a fine geometric model containing over 100,000 fibers. All fibers effectively avoided the 45 preset obstacle areas, and there was no intersecting fiber phenomenon. Figure 7 and Figure 8 As shown, the fibers successfully avoided the cuboid obstacle region. The generation process took approximately 30 minutes, which is more than 70% more efficient than traditional random sequence fiber generation methods (which typically take more than 120 minutes), and the geometric accuracy fully meets the requirements of finite element analysis.

[0059] Based on the same inventive concept, another embodiment of this application provides a microscopic modeling system for obstacle avoidance short-cut fiber reinforced cementitious composite materials, referring to... Figure 9 As shown, the microscopic modeling system 100 includes: Configuration module 110 is used to construct a micro-geometric model of chopped fiber reinforced cementitious composite material and set the parameter information and obstacle information of the micro-geometric model; The initialization module 120 is used to mesh the micro-geometric model based on parameter information to form mesh cells; based on the mesh cells and obstacle information, a dual spatial mesh index is constructed, which includes a first spatial mesh index for managing fiber spatial distribution and a second spatial mesh index for managing obstacle spatial distribution. The fiber generation engine module 130 is used to spatially divide the mesoscopic geometry model and generate at least one subspace region; fiber generation, collision detection based on dual spatial mesh indexing, and fiber registration are performed cyclically within each subspace region. Output module 140 is used to output the basic information of the registered fibers as a geometric model file in a preset file format.

[0060] In the embodiments described above, the configuration module stores and receives user-input model parameters, fiber parameters, and obstacle parameters; the initialization module, connected to the configuration module, initializes the spatial mesh according to the aforementioned parameters and constructs a first spatial mesh index and a second spatial mesh index; the fiber generation engine module, connected to the initialization module, includes: a fiber generation unit for randomly generating single fibers within a specified area; and an obstacle collision detection unit for calling the second spatial mesh index to perform fiber collision detection. Obstacle collision detection; inter-fiber collision detection unit, used to call the first spatial grid index to perform fiber collision detection. Fiber collision detection; a fiber registration unit, used to divide and register fibers that pass the collision detection into a first spatial mesh index; an output module connected to the fiber generation engine module, used to output the final generated geometric model data as a file in a specified format. The modeling system of this application embodiment has strong scalability, and the modular system design facilitates functional expansion and maintenance, and can easily adapt to the modeling needs of different types of composite materials.

[0061] It should be noted that the modules in the microstructure modeling system for obstacle-avoiding chopped fiber reinforced cementitious composite materials provided in the above embodiments correspond to the steps of the microstructure modeling method for obstacle-avoiding chopped fiber reinforced cementitious composite materials in any of the above embodiments. Those skilled in the art can refer to the step features of the microstructure modeling method for obstacle-avoiding chopped fiber reinforced cementitious composite materials to implement the corresponding modules in the microstructure modeling system for obstacle-avoiding chopped fiber reinforced cementitious composite materials, which will not be elaborated here.

[0062] In another embodiment of this application, a non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance.

[0063] In another embodiment of this application, an electronic device is also provided, including a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions stored in the memory and execute the steps of the above-described method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to the obtained program instructions.

[0064] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs and functional modules that implement the above methods), computer instructions, etc., and the aforementioned computer programs and computer instructions can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0065] The aforementioned computer programs, computer instructions, etc., can be stored in partitions within one or more memory locations. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by a processor.

[0066] A processor is used to execute a computer program stored in memory to implement the various steps of the methods involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0067] The processor and memory can be separate structures or integrated structures. When the processor and memory are separate structures, they can be coupled together via a bus.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] The preferred features in the above embodiments can be used individually in any embodiment, or in any combination thereof, provided they do not conflict with each other. Furthermore, parts not described in detail in the embodiments can be implemented using existing technologies.

[0073] The foregoing has described some specific embodiments of this application. It should be understood that this application is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the substantive content of this application. The above-described preferred features can be used in any combination without conflict.

Claims

1. A method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance, characterized in that, include: A microscopic geometric model of short-cut fiber reinforced cementitious composite material is constructed, and the parameter information and obstacle information of the microscopic geometric model are set. Based on the parameter information, the microscopic geometric model is meshed to form mesh cells; Based on the divided grid cells and obstacle information, a dual spatial grid index is constructed, which includes a first spatial grid index for managing the spatial distribution of fibers and a second spatial grid index for managing the spatial distribution of obstacles. The microscopic geometric model is spatially divided to generate at least one subspace region; Fiber generation, collision detection based on dual spatial grid indexing, and fiber registration are performed cyclically within each of the aforementioned subspace regions; Output the basic information of the registered fibers as a geometric model file in a preset file format.

2. The method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to claim 1, characterized in that, The parameter information of the micro-geometric model includes three-dimensional size parameters, chopped fiber length, mesh scaling factor, and fiber angle group distribution parameters. The fiber angle group distribution parameters include at least one set of preset fiber angle groups and their corresponding fiber proportions and angle ranges. The obstacle information includes the spatial coordinate information of each obstacle and its obstacle identifier. The obstacles include at least one of reinforcing particles, fiber woven mesh, and wire mesh.

3. The method for microscopic modeling of short-cut fiber reinforced cementitious composite materials for obstacle avoidance according to claim 2, characterized in that, The microscopic geometric model is meshed based on the parameter information to form mesh cells; Based on the divided grid cells and obstacle information, a dual spatial grid index is constructed, including: The product of the chopped fiber length and the mesh scaling factor is used as the mesh size to divide the micro-geometric model into mesh cells. Construct a first spatial grid index, which is based on the divided grid cells and is used to associate the fiber identifier to be registered with the corresponding fiber spatial coordinates; A second spatial grid index is constructed, which is based on the divided grid cells and is used to associate obstacle identifiers and corresponding obstacle spatial coordinate information.

4. The method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to claim 3, characterized in that, The construction of the second spatial grid index includes: For each obstacle, obtain all the mesh cells covered by its three-dimensional geometry; Register the obstacle's obstacle identifier to every grid cell covered by its three-dimensional geometry.

5. The method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to claim 3, characterized in that, The microscopic geometric model is spatially divided to generate at least one subspace region; Fiber generation, collision detection based on dual spatial grid indexing, and fiber registration are performed cyclically within each of the aforementioned subspace regions, including: According to the preset volume division rules, the microscopic geometric model is divided into several non-overlapping subspace regions; Based on the preset total number of fibers and the volume ratio of each subspace region, the target number of a certain fiber angle group in each subspace region is obtained. The target number of a certain fiber angle group in the subspace region is the product of the total number of fibers, the volume ratio of the subspace region and the fiber ratio of the certain fiber angle group. Based on the fiber angle group, a corresponding fiber is generated, and the spatial node coordinates of the fiber are determined. The spatial node coordinates include the coordinates of the center point and the coordinates of both ends of the fiber. Collision detection is performed based on the spatial node coordinates of fibers and dual spatial grid indexes to obtain non-collision fibers; After dividing the non-collision fibers, register them into the grid cells corresponding to the dual spatial grid index; The fiber generation, collision detection, and registration steps described above are repeated until the number of registered fibers in each fiber angle group in each of the subspace regions reaches the target number of fibers.

6. The method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to claim 5, characterized in that, The step of generating corresponding fibers based on the fiber angle set and determining the spatial node coordinates of the fiber includes: Set the length and center point coordinates of the fiber to be generated; The generation direction (ang_a, ang_b) of the fiber to be generated is determined. The generation direction (ang_a, ang_b) is determined based on the angle range [min_deg, max_deg] of the fiber angle group, where ang_a is the angle between the fiber to be generated and the Z-axis, and ang_a is randomly selected within the angle range [min_deg, max_deg]. ang_b is the rotation angle of the fiber to be generated around the Z-axis, and ang_b is randomly selected within the range [0, 360°). A single fiber is generated based on the set length, center point coordinates, and generation direction, and the spatial node coordinates of the generated fiber are determined.

7. The method for microscopic modeling of chopped fiber reinforced cementitious composite materials for obstacle avoidance according to claim 5, characterized in that, The collision detection based on fiber spatial node coordinates and dual spatial grid indexing to obtain non-collision fibers includes: First collision detection: Calculate the axial bounding box of the newly generated fiber based on the spatial node coordinates, and take the grid cell covered by the axial bounding box of the newly generated fiber as the designated grid cell; obtain all obstacle identifiers registered in the designated grid cell from the second spatial grid index to form a candidate obstacle set; The Separation Axis Theorem algorithm is used to project the newly generated fiber and the bounding boxes of obstacles in the candidate obstacle set onto the three coordinate axes of the mesoscopic geometric model coordinate system, respectively, to obtain the projection intervals of the newly generated fiber and the bounding boxes of obstacles along the three coordinate axes. If the projection interval of the newly generated fiber is separated from the projection interval of the bounding box of the obstacle along any coordinate axis, it is determined that the newly generated fiber has not collided with the obstacle; otherwise, it is determined that there is a potential collision risk between the newly generated fiber and the obstacle. All obstacles in the candidate obstacle set are traversed. If any obstacle in the candidate obstacle set has a potential collision risk with the newly generated fiber, the newly generated fiber is discarded; otherwise, the second collision detection is performed. Second collision detection: Obtain all fiber identifiers registered within the specified grid cell from the first spatial grid index to form a candidate fiber set; sequentially perform axial bounding box intersection detection, coplanarity check, and geometric intersection detection on the newly generated fiber and a registered fiber in the candidate fiber set; if the newly generated fiber and the registered fiber satisfy the corresponding collision determination conditions in the above axial bounding box intersection detection, coplanarity check, and geometric intersection detection, it is determined that the newly generated fiber and the registered fiber have a fiber-to-fiber collision; otherwise, it is determined that the newly generated fiber and the registered fiber have not a fiber-to-fiber collision; traverse all registered fibers in the candidate fiber set; if any registered fiber in the candidate fiber set has a fiber-to-fiber collision with the newly generated fiber, discard the newly generated fiber; otherwise, treat the newly generated fiber as a non-collision fiber.

8. The method for microscopic modeling of short-cut fiber reinforced cementitious composite materials for obstacle avoidance according to claim 5, characterized in that, The step of dividing and registering non-collision fibers into the grid cells corresponding to the dual spatial grid index includes: Assign a unique fiber identifier to each non-collision fiber; The non-collision fibers are divided into several fiber sub-units, and interpolation point coordinates are generated. The interpolation points are the endpoints of each fiber sub-unit. The endpoints and interpolation points of the fiber before division are used as nodes. A mapping relationship between each node and the fiber subunit is created. The fiber identifier, all nodes contained in the fiber and the information of the corresponding fiber subunit are used as the basic information of the fiber. Based on the coordinates of all nodes of the fiber, the grid cell to which each node belongs is determined, and the basic information of the fiber is registered and written into the first spatial grid index of the corresponding grid cell to complete the update of the first spatial grid index.

9. A microscopic modeling system for obstacle avoidance of chopped fiber reinforced cementitious composite materials, characterized in that, include: A configuration module is used to construct a micro-geometric model of chopped fiber reinforced cementitious composite material and to set the parameter information and obstacle information of the micro-geometric model. An initialization module is used to perform mesh generation on the micro-geometric model based on the parameter information, forming mesh cells; Based on the divided grid cells and obstacle information, a dual spatial grid index is constructed, which includes a first spatial grid index for managing the spatial distribution of fibers and a second spatial grid index for managing the spatial distribution of obstacles. The fiber generation engine module is used to spatially divide the micro-geometric model and generate at least one sub-space region. Fiber generation, collision detection based on dual spatial grid indexing, and fiber registration are performed cyclically within each of the aforementioned subspace regions; The output module is used to output the basic information of the registered fibers as a geometric model file in a preset file format.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-8.

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