Artware design method based on water flow numerical model simulation

Abstract

The invention discloses a handicraft design method based on water flow numerical model simulation, which relates to the technical field of process design, and comprises the following steps: taking structured flow field gradient data as geometric boundary evolution driving information, and carrying out continuous iteration updating on a parameterized initial geometric matrix by using an implicit topology evolution algorithm to obtain a parameterized initial geometric matrix; enabling the geometric boundary to generate directional evolution under the driving of the flow field gradient until a preset stable condition is reached, forming a stable three-dimensional topological structure model, and calling a flow direction vector field in the structured flow field gradient data to perform surface path tracking and parameter mapping on the basis of the stable three-dimensional topological structure model, projecting the flow direction path to the surface of the model and generating a continuous texture structure to obtain a textured three-dimensional structure model; according to the method, the implicit topology evolution algorithm is utilized to perform continuous iteration updating on the parameterized initial geometric matrix, so that the geometric boundary can automatically generate directional evolution under the driving of the flow field gradient, and the structural form can be gradually optimized along with the change of the flow characteristic.

Description

Technical Field

[0001] This invention relates to the field of process design technology, and in particular to a method for designing handicrafts based on numerical simulation of water flow models. Background Technology

[0002] With the continuous development of digital design and computer-aided design technologies, numerical simulation-driven morphology generation methods have gradually become an important research direction in industrial design, product styling design, and art and craft design. In traditional craft design, designers typically rely on experience or aesthetic rules to manually model and adjust geometric shapes, surface textures, and structural layouts. The design results largely depend on design experience and subjective judgment. In recent years, computational fluid dynamics (CFD) and computer-aided design technologies have gradually merged, making it possible to drive geometric shape generation through the laws of fluid motion such as water flow and airflow. Related research shows that by numerically simulating fluid flow patterns, velocity distribution, pressure distribution, and vortex structures, flow maps and gradient data reflecting fluid dynamics characteristics can be obtained. These flow characteristics can then be mapped onto three-dimensional structural forms or surface textures, thus forming morphological designs with natural flow characteristics. For example, in landscape water features, art sculptures, and functional structural designs, attempts have begun to explore using fluid simulation results to guide the layout of structural surfaces, the distribution of flow channels, and the generation of decorative textures.

[0003] However, existing fluid simulation-based design methods still have room for improvement in practical applications. First, most existing technologies only use fluid simulation results as design reference data and manually adjust the model shape. There is a lack of direct coupling mechanism between the flow field data and the geometric model, making it difficult to fully express the fluid dynamics characteristics in the three-dimensional structural form. This results in low design efficiency and insufficient stability of the results. Second, existing methods typically perform independent geometric modeling or texture design after completing the flow field analysis. There is a lack of a continuous iterative update mechanism between fluid information and geometric structure, making it difficult to achieve automatic evolution of geometric boundaries driven by the flow field gradient. This limits the improvement of design schemes in terms of morphological complexity and structural rationality. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a craft design method based on water flow numerical model simulation to solve the problem that most existing technologies only use fluid simulation results as design reference data and lack a continuous iterative update mechanism.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for designing handicrafts based on numerical simulation of water flow, comprising: An evolvable parametric initial geometric matrix is ​​constructed in computer-aided design software, and water flow numerical simulation is performed in the fluid computation domain where the parametric initial geometric matrix is ​​located to generate structured flow field gradient data. Structured flow field gradient data is used as the driving information for geometric boundary evolution. An implicit topological evolution algorithm is used to continuously update the parameterized initial geometric matrix, so that the geometric boundary undergoes directional evolution under the drive of the flow field gradient until a preset stability condition is reached, forming a stable three-dimensional topological structure model. In each iteration, the flow numerical simulation is performed again based on the current geometric boundary to update the structured flow field gradient data, which is used to drive the next round of geometric evolution. Based on a stable three-dimensional topological model, the flow direction vector field in the structured flow field gradient data is called to perform surface path tracing and parameter mapping, so that the flow direction path is projected onto the model surface to obtain a textured three-dimensional structure model. The texturized 3D structural model is subjected to manufacturing constraint analysis. Based on the constraint results obtained from the analysis, the local structure of the texturized 3D structural model is modified, and a digital manufacturing model that meets the manufacturing conditions is output.

[0007] As a preferred embodiment of the craft design method based on water flow numerical model simulation described in this invention, the evolvable parameterized initial geometric base is constructed by establishing a three-dimensional continuum model in computer-aided design software using implicit function expression, and setting an adjustable set of parameters for the three-dimensional continuum model.

[0008] As a preferred embodiment of the craft design method based on numerical simulation of water flow described in this invention, the generation of structured flow field gradient data specifically includes: Perform numerical flow calculations within the fluid computational domain of the parameterized initial geometric matrix to obtain velocity and pressure field data. Spatial gradient calculations are performed on the velocity components in the velocity field data and the pressure scalar values ​​in the pressure field data to form the flow field gradient matrix. The flow field gradient matrix is ​​encapsulated to generate structured flow field gradient data.

[0009] As a preferred embodiment of the craft design method based on numerical simulation of water flow described in this invention, the step of continuously iteratively updating the parameterized initial geometric base using an implicit topological evolution algorithm specifically involves: The parameterized initial geometric matrix is ​​converted into an implicit function expression, and boundary evolution velocity data is constructed based on the velocity gradient intensity values ​​in the structured flow field gradient data. The implicit function expression is numerically updated based on the boundary evolution rate data to obtain the updated implicit function expression, and the geometric boundary of the parameterized initial geometric matrix is ​​reconstructed based on the updated implicit function expression. Numerical flow calculations are performed on the fluid computation domain containing the reconstructed geometric boundary to generate updated structured flow field gradient data, and boundary evolution velocity data is reconstructed based on the updated structured flow field gradient data. The numerical update process of the implicit function expression is iteratively controlled. When the change in geometric boundary is lower than the preset update threshold in a series of preset iterations, the iteration is terminated and a stable three-dimensional topological structure model is output.

[0010] As a preferred embodiment of the craft design method based on numerical simulation of water flow described in this invention, the step of converting the parameterized initial geometric basis into an implicit function expression specifically involves: Spatial discretization is performed on the parameterized initial geometric base to obtain a set of discrete nodes for the geometric boundary. A symbolic distance function is constructed based on the discrete node set of the geometric boundary. The symbolic distance function is used as an implicit function expression, and a numerical correspondence is established between the implicit function expression and the discrete node set of the geometric boundary. The implicit function expression is encapsulated to generate an implicit function expression data structure.

[0011] As a preferred embodiment of the craft design method based on numerical simulation of water flow described in this invention, the construction of boundary evolution velocity data specifically includes: Extract velocity gradient intensity values ​​and flow direction vector fields from structured flow field gradient data; After normalizing the velocity gradient intensity value, the evolution direction and evolution rate of each discrete node of the geometric boundary are determined by combining the flow direction vector field. The evolution rate of each discrete node is numerically encapsulated to form boundary evolution rate data.

[0012] As a preferred embodiment of the craft design method based on water flow numerical model simulation described in this invention, the process of obtaining a textured three-dimensional structural model specifically includes: The flow direction vector field is extracted from the structured flow field gradient data. Based on the flow direction vector field, the flow direction path tracing calculation is performed on the surface nodes of the stable three-dimensional topology model to generate a flow direction path set. The flow direction path set is then mapped to the surface of the stable three-dimensional topology model. Based on the velocity gradient intensity values ​​corresponding to the set of flow paths, local offset calculations are performed on the surface of a stable three-dimensional topological structure model to generate a continuous texture structure. A textured 3D structural model is obtained by surface reconstruction of a stable 3D topological model with continuous texture structure.

[0013] As a preferred embodiment of the craft design method based on water flow numerical model simulation described in this invention, the constraint results are manufacturing constraint data generated by performing minimum wall thickness detection, overhang angle detection and internal cavity sealing detection on the textured three-dimensional structural model. The manufacturing constraint data includes minimum wall thickness detection results, overhang angle detection results, and internal cavity sealing detection results. The minimum wall thickness detection results include the grid node numbers corresponding to the insufficient wall thickness locations, the overhang angle detection results include the triangular grid center numbers corresponding to the overhang angle exceeding the limit locations, and the internal cavity sealing detection results include the spatial locations corresponding to the locations not connected to the external space.

[0014] As a preferred embodiment of the craft design method based on water flow numerical model simulation described in this invention, the output digital manufacturing model that satisfies the manufacturing conditions specifically includes: Perform 3D mesh reconstruction calculations on the textured 3D structural model to generate standard triangular mesh data; Perform mesh connectivity and normal consistency checks on standard triangular mesh data to form compliant triangular mesh data; The compliant triangular mesh data is converted into a target manufacturing format data file and encapsulated to output a digital manufacturing model that meets the manufacturing requirements.

[0015] As a preferred embodiment of the craft design method based on water flow numerical model simulation described in this invention, the preset update threshold is an iterative convergence judgment threshold set according to the distribution range of velocity gradient intensity values ​​in the structured flow field gradient data and the spatial scale parameters of the parameterized initial geometric matrix.

[0016] The beneficial effects of this invention are as follows: By using numerical simulation of water flow to generate structured flow field gradient data, and directly using the structured flow field gradient data to drive the topological evolution process of the geometric structure, the direct coupling between flow field information and geometric shape generation process is realized, so that the fluid dynamic characteristics can be effectively expressed in the three-dimensional structural shape, avoiding the need to repeatedly adjust the model based on human experience, and improving the automation of the design process; at the same time, the implicit topological evolution algorithm is used to continuously iterate and update the parameterized initial geometric matrix, so that the geometric boundary can automatically undergo directional evolution under the drive of the flow field gradient, and form a stable three-dimensional topological structure model in the process of evolution, so that the structural shape can be gradually optimized with the change of flow characteristics, improving the complexity and rationality of the structural shape, and improving the stability and consistency of the design results. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a craft design method based on numerical simulation of water flow.

[0019] Figure 2 A flowchart for generating structured flow field gradient data.

[0020] Figure 3 A flowchart for outputting a stable 3D topological structure model.

[0021] Figure 4 A flowchart for outputting a digital manufacturing model that meets manufacturing conditions. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for designing handicrafts based on numerical simulation of water flow, including the following steps: S1: Construct an evolvable parametric initial geometric matrix in computer-aided design software, and perform numerical simulation of water flow within the fluid computation domain where the parametric initial geometric matrix is ​​located to generate structured flow field gradient data.

[0026] S1.1: Create a new 3D modeling file in the computer-aided design software, select implicit function expression as the geometric representation of the continuum 3D model, and establish a spatial coordinate variable... The signed distance function of the independent variable Used to represent spatial points The signed distance relationship to the surface of a continuum 3D model is defined as follows: when the signed distance function is 0, it represents the geometric boundary of the continuum 3D model; when the signed distance function is greater than 0, it represents the outer region of the continuum 3D model; and when the signed distance function is less than 0, it represents the inner region of the continuum 3D model. The signed distance function can be expressed in a standard spherical form. ; in, This represents the scale parameter, with a value ranging from 1 to 10.

[0027] It can also be done in the symbolic distance function Curvature adjustment parameters or spatial offset parameters can be superimposed to change the shape and position of the 3D model of the continuum, for example, by introducing a position offset parameter into the signed distance function. Or morphological adjustment parameters The example value ranges are as follows: With parameters [-5,5] and morphological adjustment parameters [0.1,1], the numerical values ​​of the scale parameter, position offset parameter, and morphological adjustment parameter are modified in the computer-aided design software interface, causing changes in the functional expression of the symbolic distance function. This results in corresponding changes in the overall scale, spatial position, and local curvature morphology of the continuum 3D model. The symbolic distance function, which includes the scale parameter, position offset parameter, and morphological adjustment parameter, is discretely sampled to generate 3D geometric representation data. At the same time, the functional relationship between the symbolic distance function and the scale parameter, position offset parameter, and morphological adjustment parameter is preserved, so that the 3D geometric representation data and the symbolic distance function form a synchronously updated representation structure. This results in an evolvable parameterized initial geometric matrix that simultaneously possesses an implicit functional expression form and the ability to control the set of adjustable parameters.

[0028] S1.2: Establish a three-dimensional computational mesh within the fluid computation domain of the parameterized initial geometric matrix. Set water inlet boundary conditions (fixed velocity value), outlet boundary conditions (fixed pressure value), and wall no-slip boundary conditions for each mesh node in the three-dimensional computational mesh. The wall no-slip boundary condition sets the velocity components in all three directions of the velocity field data to zero at the surface of the parameterized initial geometric matrix and the boundary wall of the fluid computation domain. In the computer-aided design software, call the water flow numerical solution function to perform numerical solution on the three-dimensional computational mesh to obtain velocity field data and pressure field data covering the entire fluid computation domain. The velocity field data includes the velocity component values ​​in the three directions, and the pressure field data includes the pressure scalar values ​​of the corresponding mesh nodes.

[0029] In a three-dimensional computational grid, with each grid node as the computational center, the three velocity components in the velocity field data are respectively... direction, direction and The rate of change in direction is calculated using the difference between adjacent grid nodes. This involves dividing the velocity component difference between the current grid node and its adjacent grid nodes by the corresponding grid spacing to obtain the values. directional velocity gradient components, directional velocity gradient components and The directional velocity gradient components are combined to form a velocity gradient component set; the same method is used for the pressure scalar values ​​in the pressure field data to obtain the pressure gradient component set. The velocity gradient components in the velocity gradient component set and the pressure gradient components in the pressure gradient component set are combined according to the grid node number to form a flow field gradient matrix that corresponds one-to-one with the three-dimensional computational grid.

[0030] The flow field gradient matrix is ​​arranged and encapsulated in a structured manner according to the grid node number, spatial coordinate position, and gradient component order to generate structured flow field gradient data containing velocity gradient intensity values ​​and flow direction vector field information.

[0031] S2: Using structured flow field gradient data as the driving information for geometric boundary evolution, the parameterized initial geometric matrix is ​​continuously iteratively updated using an implicit topological evolution algorithm, so that the geometric boundary undergoes directional evolution under the drive of the flow field gradient until a preset stable condition is reached, forming a stable three-dimensional topological structure model. In each iteration, the flow numerical simulation is performed again based on the current geometric boundary to update the structured flow field gradient data, which is used to drive the next round of geometric evolution.

[0032] S2.1: Convert the parameterized initial geometric basis into an implicit function expression, specifically as follows: In computer-aided design software, the three-dimensional geometric representation data of the parametric initial geometric base will be discretized by voxelization or triangular meshing. A regular three-dimensional sampling mesh will be generated within the spatial range of the parametric initial geometric base according to a preset spatial resolution. The coordinates of the mesh nodes that intersect with the geometric boundary of the parametric initial geometric base will be extracted to form a set of discrete nodes of the geometric boundary.

[0033] To further explain, the preset spatial resolution is based on the minimum geometric feature size of the parameterized initial geometric matrix and the scale of the three-dimensional computational grid used in the subsequent numerical solution of the flow, so that the grid spacing of the regular three-dimensional sampling grid is no greater than half of the minimum geometric feature size of the parameterized initial geometric matrix; the preferred value is that the grid spacing of the regular three-dimensional sampling grid is equal to one-third of the minimum geometric feature size of the parameterized initial geometric matrix, which can ensure that the geometric boundary discretization accuracy meets the implicit function expression accuracy requirements, while maintaining consistency with the scale of the three-dimensional computational grid used in the numerical solution of the flow, thereby avoiding scale mismatch between the geometric expression accuracy and the flow numerical solution accuracy.

[0034] Using the set of discrete nodes at the geometric boundary as a reference, the shortest Euclidean distance from each sampling point to the set of discrete nodes at the geometric boundary is calculated on a regular 3D sampling grid. The distance is assigned a positive or negative sign based on whether the sampling point is located inside or outside the parameterized initial geometric base, resulting in a discrete numerical expression of the signed distance function on the regular 3D sampling grid. The value is positive for the outside, negative for the inside, and zero when located on the geometric boundary of the parameterized initial geometric base. The signed distance function is then used as an implicit function expression. The function value of the signed distance function at each sampling point is calculated within the regular 3D sampling grid, and the coordinates of the regular 3D sampling grid and the function value of the signed distance function at the corresponding sampling point are stored in a one-to-one correspondence. This establishes a numerical correspondence between the implicit function expression and the set of discrete nodes at the geometric boundary. Finally, the coordinates of the regular 3D sampling grid, the value of the signed distance function, and the adjustable parameter set of the parameterized initial geometric base are uniformly encapsulated to generate an implicit function expression data structure.

[0035] S2.2: Construct boundary evolution velocity data, specifically: The velocity gradient intensity value and flow direction vector field component corresponding to the coordinate positions of each node in the geometric boundary discrete node set are read from the structured flow field gradient data. The velocity gradient intensity value is linearly normalized according to the maximum velocity gradient intensity value in the structured flow field gradient data, so that the normalized velocity gradient intensity value is in the numerical range of 0 to 1. The normalized velocity gradient intensity value is used as the evolution rate scalar, and the flow direction vector field component at the corresponding position is used as the evolution direction vector. A corresponding evolution direction vector and evolution rate scalar are generated for each node in the geometric boundary discrete node set. When determining the evolution direction of the geometric boundary discrete node, the local normal direction vector of the surface node of the stable three-dimensional topological structure model is used as the reference, and the directional relationship between the flow direction vector field component and the local normal direction vector is determined. When the flow direction vector is aligned with the normal direction, the geometric boundary moves outward along the local normal direction; when the flow direction vector is opposite to the normal direction, the geometric boundary moves inward along the local normal direction. The velocity gradient intensity value is used to determine the rate at which the geometric boundary moves along the local normal direction, so that the evolution direction of the geometric boundary is consistent with the fluid flow direction, and the region with large flow changes produces corresponding structural morphology adjustments.

[0036] The evolution direction vector and evolution rate scalar of each node in the discrete node set of the geometric boundary are stored uniformly according to the node number to form boundary evolution velocity data.

[0037] S2.3: Based on the evolution direction vector and evolution rate scalar of the geometric boundary discrete node set recorded in the boundary evolution velocity data, the function values ​​of the implicit function expression are updated by time step on the regular three-dimensional sampling grid. For each regular three-dimensional sampling grid node, the function value of the sign distance function is incrementally updated along the normal direction according to the evolution direction vector and evolution rate scalar of the corresponding geometric boundary discrete node, so as to obtain the updated implicit function expression. Based on the equivalence condition that the updated implicit function expression is equal to 0, the geometric boundary point set is re-extracted to form the geometric boundary of the reconstructed parameterized initial geometric matrix, so that the change of the geometric boundary directly responds to the flow intensity distribution in the structured flow field gradient data, realizing the flow field-driven directional morphological evolution.

[0038] Based on the reconstructed geometric boundary, a new three-dimensional computational mesh for the fluid computation domain is established. Following the numerical solution method for water flow in S1.2, numerical solution calculations are performed within the fluid computation domain to generate updated structured flow field gradient data corresponding to the reconstructed geometric boundary. This updated structured flow field gradient data is then used as the output result after the current iteration round to drive the next round of geometric boundary evolution. The updated structured flow field gradient data is input into the boundary evolution velocity data construction step to form new boundary evolution velocity data. A fixed time step (0.01 seconds) and an iteration count counter are set for the continuous update process expressed by implicit functions. After each update round, the node displacement difference between the current set of geometric boundary points and the previous set of geometric boundary points is calculated. When the displacement difference of all discrete nodes of the geometric boundary is less than the preset update threshold during a preset number of iterations, the update process stops and a stable three-dimensional topology model is output. This allows the stable three-dimensional topology model to form an adaptive topology while maintaining consistent flow characteristics.

[0039] It should also be noted that for 3D craft models of moderate complexity (containing approximately 100,000 to 500,000 triangular mesh centers), the iterative process typically converges within a finite number of iterations. To reduce computational costs, the corresponding local 3D computational mesh is marked as needing updating only when the displacement of discrete nodes along the normal direction at the geometric boundary exceeds 5% of the average node spacing; otherwise, the original mesh is retained. This reduces the computational load per iteration while maintaining the validity of the structured flow field gradient data.

[0040] To further explain, the preset number of iterations is the number of consecutive convergence checks set before the iteration begins, preferably any value between 3 and 10, to avoid misjudging iteration convergence due to local fluctuations in a single iteration. The preset update threshold is an iteration convergence judgment threshold set based on the distribution range of velocity gradient intensity values ​​in the structured flow field gradient data and the spatial scale parameters of the parameterized initial geometric matrix. It is preferably taken as 1% of the average node spacing of the discrete node set of the geometric boundary of the parameterized initial geometric matrix. The reason for taking 1% is that the spatial distribution accuracy of the discrete node set of the geometric boundary is determined by the average node spacing. When the displacement change of the discrete nodes of the geometric boundary is less than 1% of the average node spacing, the displacement change is already lower than the discretization capability of the discrete expression. Continuing the iteration will not produce discretizable geometric differences in the implicit function expression. At the same time, the morphological changes driven by the velocity gradient intensity value have entered the numerically stable stage. Therefore, taking 1% of the average node spacing as the preset threshold can keep the iteration termination condition consistent with the geometric discretization accuracy and avoid invalid small updates.

[0041] After a stable 3D topological model is formed, the model's morphology can be moderately intervened by adjusting the adjustable parameter set of the initial geometric matrix to achieve aesthetic adjustment. During the physics-driven topological evolution process, the evolution amplitude of geometric boundaries or the range of local morphological changes can be limited to ensure that the generated structure is continuous, smooth, and meets design aesthetic requirements. Furthermore, for specific application scenarios, such as water feature sculptures, tea sets, or aquarium landscaping, additional morphological optimization can be performed on relevant local areas according to the design goals, thereby ensuring that the generated stable 3D topological model conforms to flow field characteristics while also meeting the aesthetic and functional requirements of the craft.

[0042] Preferably, compared to existing technologies that rely solely on a fixed geometric model for single-pass flow field analysis and rely on manual morphological adjustments, the above scheme converts the parameterized initial geometric matrix into an implicit function expression and combines it with structured flow field gradient data to construct boundary evolution velocity data. This enables automatic iterative updates of the geometric boundary driven by the flow field, creating a closed-loop coupling between the morphological evolution process and hydrodynamic characteristics. In each iteration, the flow numerical solution is recalculated based on the current geometric boundary, and the structured flow field gradient data is updated. This ensures that subsequent geometric boundary evolution is always based on the flow field state corresponding to the current morphology, thereby guaranteeing the dynamic consistency between the geometric boundary evolution process and hydrodynamic characteristics.

[0043] S3: Based on the stable three-dimensional topological model, the flow direction vector field in the structured flow field gradient data is called to perform surface path tracing and parameter mapping, so that the flow direction path is projected onto the model surface to obtain a textured three-dimensional structure model.

[0044] S3.1: Texture generation is based on a stable 3D topological model and its corresponding final flow field, performing a unidirectional mapping. Textures do not negatively affect topological evolution, ensuring computational controllability and simulation feasibility. The flow direction vector components covering the fluid computation region of the stable 3D topological model are read from the structured flow field gradient data. Several initial sampling points are selected on the surface of the stable 3D topological model. The corresponding flow direction vector components are extracted according to the spatial coordinates of these initial sampling points in the structured flow field gradient data. Stepwise integration is performed along the direction of the flow direction vector components on the surface of the stable 3D topological model. During each step, the direction of integration is restricted to the tangent plane of the stable 3D topological model surface, thus obtaining a set of flow paths distributed along the surface of the stable 3D topological model, ensuring that the flow paths are consistent with the flow field direction.

[0045] Each flow path in the flow path set is represented as a sequence of path points continuously distributed along the surface of a stable 3D topological model. For each path point in the sequence, the surface parameter coordinates (u, v) are calculated on the surface of the stable 3D topological model. Interpolation sampling is performed on (u, v) according to the constant step increment of the surface parameter coordinates of the stable 3D topological model, so that each flow path forms a resampled path point sequence with a fixed number of points on the surface of the stable 3D topological model. The resampled path point sequence is projected onto the surface mesh of the stable 3D topological model. A corresponding surface mesh node number is assigned to each resampled path point sequence using the nearest neighbor mesh node matching method. The surface mesh node numbers associated with the same flow path are connected in the order of the path points, so that the path point sequence in the flow path set corresponds to the surface mesh node of the stable 3D topological model, thereby ensuring that the flow path set can directly index the surface mesh node of the stable 3D topological model.

[0046] S3.2: At each surface mesh node corresponding to the flow path set, read the velocity gradient intensity value corresponding to the node's spatial coordinate position in the structured flow field gradient data. Normalize the velocity gradient intensity value according to the maximum velocity gradient intensity value in the structured flow field gradient data to obtain the normalized velocity gradient intensity value. Multiply the normalized velocity gradient intensity value with the preset texture displacement scale coefficient to obtain the node displacement amplitude. Calculate the local normal direction vector of the corresponding surface mesh node on the surface of the stable three-dimensional topology model. Apply the node displacement amplitude along the local normal direction vector to the surface mesh node coordinate update. Perform displacement update processing on the surface mesh nodes covered by the parameter control curve set, thereby forming a continuous texture structure whose fluctuation amplitude is controlled by the velocity gradient intensity value and is continuously distributed along the flow path.

[0047] To further explain, the preset texture displacement scale coefficient is set based on the average node spacing of the discrete node set of the geometric boundary of the stable three-dimensional topological structure model and the range of the surface curvature radius of the stable three-dimensional topological structure model. It is used to ensure that the displacement amplitude of the surface node does not exceed the surface discretization accuracy of the stable three-dimensional topological structure model. It is preferred to take 0.2 times the average node spacing of the discrete node set of the geometric boundary of the stable three-dimensional topological structure model as the preset texture displacement scale coefficient. The reason for this value is that 0.2 times the average node spacing can ensure that the texture undulation height is less than the upper limit of the surface discretization resolution, so that the continuous texture structure maintains the continuous surface characteristics during the surface reconstruction process, while avoiding geometric intersection or surface folding of adjacent surface nodes.

[0048] The updated stable 3D topological model surface mesh is subjected to surface reconstruction processing. While maintaining the surface mesh topological relationship, the average position of adjacent mesh nodes is calculated for the surface mesh nodes that have undergone displacement updates. The coordinates of the surface mesh nodes are then smoothly adjusted according to the Laplace smoothing method to ensure that the spatial transition between adjacent surface mesh nodes remains continuous. Subsequently, the surface normal direction is recalculated for the smoothed surface mesh nodes and the surface triangular mesh is corrected for consistency to ensure that the normal direction between adjacent triangular meshes is consistent. This generates a stable 3D topological model with a continuous texture structure, and the generated texture structure is superior to the traditional surface texturing method based on a preset texture template in terms of directional continuity and flow field consistency.

[0049] S4: Perform manufacturing constraint analysis on the textured 3D structural model, modify the local structure of the textured 3D structural model based on the constraint results obtained from the analysis, and output a digital manufacturing model that meets the manufacturing conditions.

[0050] S4.1: Perform manufacturing constraint analysis on the surface mesh of the textured 3D structural model. Extract the center of each triangular mesh in the surface mesh of the textured 3D structural model. Calculate the shortest distance between adjacent triangular mesh centers to obtain the local wall thickness value at each location of the textured 3D structural model. When the local wall thickness value is less than the preset minimum wall thickness requirement, record the corresponding mesh node number and form the minimum wall thickness detection result. Calculate the angle between the normal direction vector of each triangular mesh center and the manufacturing direction in the surface mesh of the textured 3D structural model. When the angle is greater than the preset overhang angle threshold, record the corresponding triangular mesh center number and form the overhang angle detection result. Detect the internal space connectivity status of the textured 3D structural model using voxel scanning. When it is detected that the center of a closed space cannot connect with the external space, record the corresponding spatial position and form the internal cavity closure detection result. Organize the minimum wall thickness detection result, overhang angle detection result, and internal cavity closure detection result according to spatial coordinates to form manufacturing constraint data.

[0051] To further explain, the preset minimum wall thickness requirement is set based on the minimum forming thickness of the target manufacturing process, preferably 1mm. This is because CNC machining and 3D printing processes can ensure structural strength and processing stability at a thickness of 1mm, while avoiding breakage or deformation of thin-walled structures during manufacturing. The preset overhang angle threshold is set based on the material's self-supporting capacity in the manufacturing direction, preferably 45°. This is because surface tilt angles within 45° can be formed by the material's own structure in 3D printing and common additive manufacturing processes, without the need for additional support structures, thus reducing manufacturing complexity.

[0052] Based on the manufacturing constraint data, local structural corrections are performed on the corresponding positions of the textured 3D structural model. For positions with insufficient minimum wall thickness, the local thickness is increased along the surface normal direction. For positions with excessive overhang angle, the coordinates of the surface mesh nodes are adjusted along the manufacturing direction. For closed positions of internal cavities, through channels are generated to form an opening structure that connects with the external space, so that the textured 3D structural model can meet the manufacturing constraint conditions while maintaining a continuous texture structure.

[0053] S4.2: After completing the local structural correction, perform 3D mesh reconstruction processing on the textured 3D structural model. Generate standard triangular mesh data by triangulating the surface nodes of the textured 3D structural model. Check the shared edge relationship between the centers of the triangular meshes one by one to complete the mesh connectivity verification. Complete the normal consistency verification by unifying the normal direction vector of the center of the triangular mesh to obtain compliant triangular mesh data.

[0054] The compliant triangular mesh data is written into the target manufacturing format data file according to the data structure requirements of the target manufacturing format, and the target manufacturing format data file is subjected to data encapsulation processing to generate a digital manufacturing model that meets the manufacturing conditions. This enables the textured 3D structure model to be directly used for CNC machining or 3D printing manufacturing, while maintaining the continuous texture structure formed based on the flow field evolution.

[0055] This embodiment also provides a computer device applicable to the design method of handicrafts based on numerical simulation of water flow, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the design method of handicrafts based on numerical simulation of water flow as proposed in the above embodiment.

[0056] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0057] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the craft design method based on water flow numerical model simulation as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0058] In summary, this invention achieves direct coupling between flow field information and geometric morphology generation by generating structured flow field gradient data through numerical simulation of water flow and directly using this data to drive the topological evolution process of the geometric structure. This allows for the effective expression of fluid dynamics characteristics within the three-dimensional structural morphology, avoiding the need for repeated adjustments to the model based on human experience and improving the automation of the design process. Simultaneously, an implicit topological evolution algorithm is used to continuously iterate and update the parameterized initial geometric matrix, enabling the geometric boundaries to automatically undergo directional evolution under the drive of the flow field gradient. During this evolution, a stable three-dimensional topological structure model is formed, allowing the structural morphology to be gradually optimized according to changes in flow characteristics. This enhances the complexity and rationality of the structural morphology, while also improving the stability and consistency of the design results.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for designing handicrafts based on numerical simulation of water flow, characterized in that: include, An evolvable parametric initial geometric matrix is ​​constructed in computer-aided design software, and water flow numerical simulation is performed in the fluid computation domain where the parametric initial geometric matrix is ​​located to generate structured flow field gradient data. Structured flow field gradient data is used as the driving information for geometric boundary evolution. An implicit topological evolution algorithm is used to continuously update the parameterized initial geometric matrix, so that the geometric boundary undergoes directional evolution under the drive of the flow field gradient until a preset stability condition is reached, forming a stable three-dimensional topological structure model. In each iteration, the flow numerical simulation is performed again based on the current geometric boundary to update the structured flow field gradient data, which is used to drive the next round of geometric evolution. Based on a stable three-dimensional topological model, the flow direction vector field in the structured flow field gradient data is called to perform surface path tracing and parameter mapping, so that the flow direction path is projected onto the model surface to obtain a textured three-dimensional structure model. The texturized 3D structural model is subjected to manufacturing constraint analysis. Based on the constraint results obtained from the analysis, the local structure of the texturized 3D structural model is modified, and a digital manufacturing model that meets the manufacturing conditions is output.

2. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The evolvable parameterized initial geometric matrix is ​​constructed by establishing a continuum 3D model in computer-aided design software using implicit function expression and setting an adjustable set of parameters for the continuum 3D model.

3. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The generation of structured flow field gradient data specifically involves: Perform numerical flow calculations within the fluid computational domain of the parameterized initial geometric matrix to obtain velocity and pressure field data. Spatial gradient calculations are performed on the velocity components in the velocity field data and the pressure scalar values ​​in the pressure field data to form the flow field gradient matrix. The flow field gradient matrix is ​​encapsulated to generate structured flow field gradient data.

4. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The method of using an implicit topological evolution algorithm to continuously iteratively update the parameterized initial geometric base is as follows: The parameterized initial geometric matrix is ​​converted into an implicit function expression, and boundary evolution velocity data is constructed based on the velocity gradient intensity values ​​in the structured flow field gradient data. The implicit function expression is numerically updated based on the boundary evolution rate data to obtain the updated implicit function expression, and the geometric boundary of the parameterized initial geometric matrix is ​​reconstructed based on the updated implicit function expression. Numerical flow calculations are performed on the fluid computation domain containing the reconstructed geometric boundary to generate updated structured flow field gradient data, and boundary evolution velocity data is reconstructed based on the updated structured flow field gradient data. The numerical update process of the implicit function expression is iteratively controlled. When the change in geometric boundary is lower than the preset update threshold in a series of preset iterations, the iteration is terminated and a stable three-dimensional topological structure model is output.

5. The craft design method based on numerical simulation of water flow as described in claim 4, characterized in that: The process of converting the parameterized initial geometric basis into an implicit function expression is as follows: Spatial discretization is performed on the parameterized initial geometric base to obtain a set of discrete nodes for the geometric boundary. A symbolic distance function is constructed based on the discrete node set of the geometric boundary. The symbolic distance function is used as an implicit function expression, and a numerical correspondence is established between the implicit function expression and the discrete node set of the geometric boundary. The implicit function expression is encapsulated to generate an implicit function expression data structure.

6. The craft design method based on numerical simulation of water flow as described in claim 4, characterized in that: The construction of boundary evolution velocity data specifically includes: Extract velocity gradient intensity values ​​and flow direction vector fields from structured flow field gradient data; After normalizing the velocity gradient intensity value, the evolution direction and evolution rate of each discrete node of the geometric boundary are determined by combining the flow direction vector field. The evolution rate of each discrete node is numerically encapsulated to form boundary evolution rate data.

7. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The obtained textured 3D structural model is specifically as follows: The flow direction vector field is extracted from the structured flow field gradient data. Based on the flow direction vector field, the flow direction path tracing calculation is performed on the surface nodes of the stable three-dimensional topology model to generate a flow direction path set. The flow direction path set is then mapped to the surface of the stable three-dimensional topology model. Based on the velocity gradient intensity values ​​corresponding to the set of flow paths, local offset calculations are performed on the surface of a stable three-dimensional topological structure model to generate a continuous texture structure. A textured 3D structural model is obtained by surface reconstruction of a stable 3D topological model with continuous texture structure.

8. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The constraint results are manufacturing constraint data generated by performing minimum wall thickness detection, overhang angle detection, and internal cavity sealing detection on the textured 3D structural model. The manufacturing constraint data includes minimum wall thickness detection results, overhang angle detection results, and internal cavity sealing detection results. The minimum wall thickness detection results include the grid node numbers corresponding to the insufficient wall thickness locations, the overhang angle detection results include the triangular grid center numbers corresponding to the overhang angle exceeding the limit locations, and the internal cavity sealing detection results include the spatial locations corresponding to the locations not connected to the external space.

9. The craft design method based on numerical simulation of water flow as described in claim 1, characterized in that: The output digital manufacturing model that satisfies the manufacturing conditions is specifically as follows: Perform 3D mesh reconstruction calculations on the textured 3D structural model to generate standard triangular mesh data; Perform mesh connectivity and normal consistency checks on standard triangular mesh data to form compliant triangular mesh data; The compliant triangular mesh data is converted into a target manufacturing format data file and encapsulated to output a digital manufacturing model that meets the manufacturing requirements.

10. The craft design method based on numerical simulation of water flow as described in claim 4, characterized in that: The preset update threshold is an iterative convergence determination threshold set based on the distribution range of velocity gradient intensity values ​​in the structured flow field gradient data and the spatial scale parameters of the parameterized initial geometric matrix.

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