A Method for Generating Curved Surface Meshes and Adaptive Remeshing for Fluid Simulation

Abstract

The present invention discloses a method for generating and adaptively re - meshing curved surface grids for fluid simulation, including: obtaining CAD fluid model data; processing the CAD fluid model to handle problems such as closed surfaces, short edges, parameter domain distortion, and geometric interference to obtain a defect - free continuous geometric model; performing curved surface discretization on the defect - free continuous geometric model to generate an initial grid; and performing grid re - meshing on the initial grid to generate optimized anisotropic curved surface grids. It solves the problems of high sensitivity to geometric defects, large redundancy of isotropic grids, and sudden size changes at the ends of the parameter domain existing in related fluid simulation technologies.

Description

Technical Field

[0001] The present invention belongs to the technical field of computational fluid dynamics simulation, and in particular relates to a surface mesh generation and adaptive re-division method for fluid simulation. Background Art

[0002] Mesh generation is a core basic step in numerical simulation. Its quality directly determines the accuracy and convergence efficiency of downstream flow field solutions. It is also the key link with the most intensive manual intervention and the longest time in engineering practice. In the field of fluid simulation, the surface mesh of complex geometric models (such as aircraft engine blades and aircraft wing surfaces) must meet two stringent requirements: one is the high-fidelity discretization of geometric features, and the other is the efficient adaptation of flow anisotropic characteristics. However, traditional mesh generation methods face significant challenges in such scenarios.

[0003] Traditional methods lack the ability to effectively handle closed surfaces, short edges, and parameter domain distortion in geometric models. For example, since the parameter domain boundary ring of a closed surface is composed of only a single closed curve, if it is not divided into multiple parameter curves, it will lead to abnormal distribution of sampling points when the surface is discretized, causing non-manifold mesh errors; local geometric distortion caused by short edges will destroy the continuity of the parameter domain and cause a sudden change in mesh size; and parameter domain distortion (such as NURBS surface distortion) will cause the normal deviation of the physical space to exceed the standard, directly affecting the reliability of flow field calculations. In addition, the uniform grid generated by the existing method produces a large number of redundant units in the curvature mutation area (such as the leading edge of the blade and the wing joint), which significantly increases the consumption of computing resources, and cannot dynamically adjust the unit size along the main curvature direction, making it difficult to efficiently capture anisotropic flow characteristics such as shock boundary layer and detached vortex.

[0004] When the curve is discretized, if the residual of the short side at the end of the parameter domain is forcibly truncated, it will cause a sudden change in the mesh size, which will in turn cause numerical oscillations in the velocity and pressure gradient of the flow field, affecting the convergence of the simulation. For example, if the residual of the short side at the end of the discrete curve of the leading edge of the wing is not reasonably allocated, the mutation rate of the local mesh size may exceed the threshold, causing the flow field calculation to diverge.

[0005] Although traditional surface mesh generation methods can handle simple geometric models, they still have obvious shortcomings when facing the above problems: the mesh normal deviation in the parameter domain mapping distortion area is difficult to control, the redundant units in the curvature rapid change area cannot be adaptively optimized, and there is a lack of effective strategy for the residual allocation of the short edge at the end of the curve. Therefore, there is an urgent need for a surface mesh generation method that can automatically repair geometric defects, suppress size mutations, and adapt to anisotropic characteristics to improve the accuracy and efficiency of fluid simulation. Summary of the invention

[0006] An embodiment of the present application provides a method for generating and adaptively re - meshing curved surfaces for fluid simulation to solve the problems of high sensitivity to geometric defects, large redundancy of isotropic meshes, and sudden size changes at the end of the parameter domain in related fluid simulation technologies.

[0007] According to an embodiment of the present application, a method for generating and adaptively re - meshing curved surfaces for fluid simulation is provided, including:

[0008] S1: Obtain CAD fluid model data;

[0009] S2: Process the CAD fluid model for problems such as closed surfaces, short edges, parameter domain distortion, and geometric interference to obtain a defect - free continuous geometric model;

[0010] S3: Perform surface discretization on the defect - free continuous geometric model to generate an initial mesh;

[0011] S4: Perform mesh re - meshing on the initial mesh to generate an optimized anisotropic curved - surface mesh.

[0012] Optionally, processing the CAD fluid model for problems such as closed surfaces, short edges, parameter domain distortion, and geometric interference to obtain a defect - free continuous geometric model includes:

[0013] S21: Detect closed surfaces of the CAD fluid model. If there are closed surfaces, split their parameter domains and construct relevant parameter curves to obtain a continuous geometric model without closed surfaces;

[0014] S22: Detect short edges of the continuous geometric model without closed surfaces. If there are short edges with lengths less than a preset threshold, merge adjacent short edges to obtain a continuous geometric model without short edges;

[0015] S23: Detect parameter domain distortion of the continuous geometric model without short edges. If there are surfaces with parameter domain distortion, perform surface interpolation and reconstruction to obtain a continuous geometric model without parameter domain distortion;

[0016] S24: Detect geometric interference of the continuous geometric model without parameter domain distortion. If there are geometric interference regions, correct the topological structure through Boolean operations to finally generate a defect - free continuous geometric model.

[0017] Optionally, performing surface discretization on the defect - free continuous geometric model to generate an initial mesh includes:

[0018] S31: Extract boundary curves of the defect - free continuous geometric model to obtain a set of boundary curves of the surface;

[0019] S32: Perform adaptive curve discretization on the set of boundary curves. If there is an end short side problem during curve discretization, use a smoothing algorithm to treat the end short side as a residual quantity and evenly distribute it to other discrete curve segments to generate a discrete point set of the physical space boundary curve;

[0020] S33: Perform parameter domain mapping processing on the discrete point set of the physical space boundary curve to generate a discrete point set of the parameter space boundary curve;

[0021] S34: Construct an initial bounding box based on the discrete point set of the parameter space boundary curve as the root node of the quadtree;

[0022] S35: Perform anisotropic subdivision processing on the root node of the quadtree, and perform encryption operations based on the combined criterion of zero-order geometric deviation, first-order tangential tolerance, and second-order normal tolerance to generate an anisotropic quadtree encrypted lattice;

[0023] S36: Perform Delaunay triangulation processing with hole constraints based on the anisotropic quadtree encrypted lattice and the discrete point set of the parameter space boundary curve to generate an initial surface mesh.

[0024] Optionally, perform mesh redivision processing on the initial mesh to generate an optimized anisotropic surface mesh, including:

[0025] S41: Perform low-quality short side detection on the initial surface mesh. If there are short sides with side lengths less than the length threshold and the dihedral angles of adjacent cells greater than the angle threshold, perform topology optimization based on edge collapse to generate a topology-optimized mesh;

[0026] S42: Perform local curvature gradient analysis on the topology-optimized mesh, and dynamically adjust the vertex distribution density according to the comparison result between the curvature gradient value and the density threshold to generate a density-optimized mesh;

[0027] S43: Perform element quality evaluation on the density-optimized mesh. If there are elements with quality factors lower than the quality threshold, perform anisotropic local subdivision to generate an optimized anisotropic surface mesh.

[0028] The technical solution provided by the present invention has the following beneficial effects:

[0029] By addressing the issues of closed surfaces, short edges, parameter domain distortion, and geometric interference in the original CAD fluid model, the present invention transforms the defective input data into a defect-free continuous geometric model, effectively avoiding mesh generation failures caused by geometric quality problems and enhancing the robustness of preprocessing for complex fluid simulations. Based on anisotropic surface discretization and mesh rezoning techniques, the mesh density is dynamically adjusted along the feature gradient direction in regions of abrupt curvature change to generate high-resolution anisotropic surface meshes. This not only accurately captures the flow field gradient features but also significantly reduces the number of redundant elements, thereby reducing computational resource consumption. The fully automated design of this method from geometric repair to mesh output, without relying on manual intervention, can efficiently adapt to the engineering simulation requirements of complex fluid shapes and significantly shorten the simulation preprocessing cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are incorporated herein and form a part of this specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0031] Figure 1 FIG. is a flowchart of a method for generating and adaptively rezoning surface meshes for fluid simulation shown in an embodiment.

[0032] Figure 2 FIG. is a schematic diagram of the original geometric model of a passenger aircraft shown in an embodiment.

[0033] Figure 3 FIG. is a schematic diagram of the treatment effect of a closed surface shown in an embodiment, showing the parameter domain segmentation process of the closed surface on the side of a cylindrical model.

[0034] Figure 4 FIG. is a schematic diagram for comparing the treatment of wing-fuselage interference of a passenger aircraft shown in an embodiment.

[0035] Figure 5 FIG. is a schematic diagram of parameter domain projection and initial quadtree construction shown in an embodiment.

[0036] Figure 6 FIG. is a schematic diagram of Delaunay triangulation shown in an embodiment.

[0037] Figure 7 FIG. is an optimized anisotropic surface mesh shown in an embodiment.

[0038] Figure 8 FIG. is a schematic diagram showing the variation of the skin friction coefficient and lift coefficient of the fuselage surface with the number of iterations shown in an embodiment.

[0039] Figure 9 FIG. is a schematic diagram of the flow field distribution (static pressure, turbulent kinetic energy, and velocity) in the cabin area of a passenger aircraft shown in an embodiment.

[0040] Figure 10Schematic diagram of the flow field distribution in the wing root region of a passenger aircraft (pressure, turbulent kinetic energy, and velocity) shown for an embodiment. Detailed implementation manners

[0041] The method of the present invention will be elaborated in detail below in conjunction with the accompanying drawings and embodiments.

[0042] The present invention supports the efficient processing of complex CAD fluid models, where the CAD fluid model refers to a three-dimensional geometric model constructed through CAD technology for fluid mechanics simulation. Typical applications include engineering objects such as passenger aircraft, aeroengine blades, aircraft wing surfaces, and turbomachinery groups. Such models need to meet strict geometric accuracy and topological integrity requirements to avoid problems such as failed mesh generation or distorted flow field simulation due to closed surfaces, short edges, or sudden curvature changes. This method can directly process the original models with the above defects through geometric repair and anisotropic surface mesh generation, and output high-fidelity surface meshes that strictly conform to the geometric features to meet the high-precision flow field solution requirements.

[0043] Figure 1 A method for generating and adaptively remeshing a surface mesh for fluid simulation shown for an embodiment. The method includes the following steps:

[0044] S1: Obtain CAD fluid model data:

[0045] Specifically, parse the CAD fluid model data through the geometric engine Open CASCADE. Taking the CAD model data of the Figure 2 shown passenger aircraft as an example, 145 boundary curves are extracted, and 54 geometric surfaces are parsed. Among them, the passenger aircraft wing is composed of 8 groups of NURBS surfaces, and the fuselage is spliced by 12 groups of B-spline surfaces. During the parsing process, the geometric topological relationship between components is completely retained.

[0046] S2: Process the problems of closed surfaces, short edges, parameter domain distortion, and geometric interference of the CAD fluid model to obtain a defect-free continuous geometric model; this step may include the following sub-steps:

[0047] S21: Detect the closed surfaces of the CAD fluid model. If there are closed surfaces, divide their parameter domains and construct relevant parameter curves to obtain a continuous geometric model without closed surfaces;

[0048] For the annular closed surface of the passenger aircraft engine nacelle (its parameter domain is a single closed curve loop, the logic is the same as Figure 3Consistent with the cylindrical model shown, first, it is split into two symmetric sub-intervals by the parameter domain splitting algorithm: u = [0, 0.5] and u = [0.5, 1]. Here, the parameter u ∈ [0, 1] represents the normalized curve parameter domain, where u = 0 and u = 1 coincide due to the closed characteristic. Subsequently, relevant new B-spline parameter curves are independently generated within each sub-interval. This method significantly improves the uniformity of the parameter distribution on the inner and outer walls of the nacelle and eliminates possible mesh distortion during the parameterization of the original closed surface.

[0049] S22: Perform short-edge detection on the continuous geometric model without a closed surface. If there are short edges with lengths less than a preset threshold, merge adjacent short edges to obtain a continuous geometric model without short edges.

[0050] Specifically, short edges with lengths less than the threshold are detected at the connection between the winglet and the main wing of the airliner. By performing vertex merging operations, geometric discontinuity regions are eliminated, and a smooth transition surface is generated to ensure uniform distribution of subsequent mesh sizes at the connection.

[0051] S23: Perform parameter domain distortion detection on the continuous geometric model without short edges. If there are surfaces with parameter domain distortion, perform surface interpolation and reconstruction to obtain a continuous geometric model without parameter domain distortion.

[0052] Specifically, the parameter domain of the airliner wing surface is detected, and it is found that there is distortion in the local surface parameter domain. The NURBS interpolation algorithm is used to recalculate the control point weights, and the parameter domain of the reconstructed surface is evenly distributed, restoring the linear mapping relationship between the physical domain and the parameter domain.

[0053] S24: Perform geometric interference detection on the continuous geometric model without parameter domain distortion. If there are geometric interference regions, correct the topological structure through Boolean operations to generate the defect-free continuous geometric model.

[0054] Specifically, for the geometric interference region between the airliner wing and the fuselage (such as Figure 4 shown), perform Boolean operations to trim the intersecting surfaces. After processing, a smooth transition topological structure is formed at the wing-fuselage connection to ensure the physical rationality of subsequent mesh generation.

[0055] S3: Perform surface discretization on the defect-free continuous geometric model to generate an initial mesh; this step may include the following sub-steps:

[0056] S31: Perform boundary curve extraction on the defect-free continuous geometric model to obtain a set of surface boundary curves.

[0057] Specifically, based on the "face-edge-vertex" topological hierarchy relationship, all boundary curves associated with the curved surface are extracted. For example, the set of boundary curves of the leading edge surface of an airliner wing includes the leading edge line, the wing root connection line, and the wing tip transition line, which completely cover the geometric contour of the surface.

[0058] S32: Perform adaptive curve discretization on the set of boundary curves. If there is a problem of short end edges during curve discretization, use a smoothing algorithm to treat the short end edges as residual amounts and evenly distribute them to other discrete curve segments to generate a set of discrete points of the boundary curve in physical space;

[0059] Specifically, during the discretization of the NURBS curve of the leading edge of an airliner wing, if there are short end edges, they are regarded as residual amounts and weighted and distributed to the previous discrete segments according to the size ratio. Update the node parameters as:

[0060]

[0061] In the formula represents the total number of discrete points, represents the index of the current discrete point, represents the original node parameter value, represents the updated node parameter value, thus eliminating the size mutation caused by traditional truncation and achieving a smooth transition of the discrete node spacing.

[0062] S33: Perform parameter domain mapping processing on the set of discrete points of the boundary curve in physical space to generate a set of discrete points of the boundary curve in parameter space;

[0063] Specifically, project the discrete points on the boundary curve in physical space into the parameter domain of the surface to be discretized to obtain a set of discrete points of the boundary curve in parameter space.

[0064] S34: Construct an initial bounding box based on the set of discrete points of the boundary curve in parameter space as the root node of the quadtree;

[0065] Specifically, as Figure 5 shown, construct an initial bounding box with the minimum circumscribed rectangle of the discrete points in parameter space, and this bounding box directly serves as the root node of the quadtree.

[0066] S35: Perform anisotropic subdivision processing on the root node of the quadtree, and perform encryption operations based on the joint criterion of zero-order geometric deviation, first-order tangential tolerance, and second-order normal tolerance to generate an anisotropic quadtree encrypted dot matrix;

[0067] Specifically, define the zero-order geometric deviation criterion, take the center point of the quadtree node as the test point, and calculate its shortest distance to the original geometric surface, and constrain this distance not to exceed a preset threshold. If If the geometric deviation from the physical surface exceeds the limit, it is determined that the quadtree node needs to be subdivided;

[0068] Specifically, a first-order tangential tolerance criterion is defined. Taking the center point of the quadtree node as a reference, calculate the maximum value of the included angle of the tangential vectors in the parameter direction between it and the center points of the adjacent four child nodes , and constrain not to exceed the preset threshold. If the tangential change angle exceeds the limit, it is determined that the node needs to be subdivided along the parameter direction.

[0069] Specifically, a second-order normal tolerance criterion is defined. Taking the center point of the quadtree node as a reference, calculate the normal vector deviation angle between it and the center points of the adjacent four child nodes, and constrain the maximum deviation angle not to exceed the preset threshold. If the normal deviation exceeds the limit, it is determined that the node needs to be subdivided along the normal sensitive direction.

[0070] S36: Based on the anisotropic quadtree encryption lattice and the discrete point set of the parameter space boundary curve, perform Delaunay triangulation with hole constraints to generate an initial surface mesh;

[0071] Specifically, for the hole area of the airliner fuselage (such as the connection with the wing), generate a constraint point set in the parameter domain, and perform Delaunay triangulation with hole constraints in combination with the anisotropic quadtree encryption lattice and the discrete point set of the parameter space boundary curve, as Figure 6 is a schematic diagram of Delaunay triangulation. The generated initial mesh strictly follows the geometric contour and meets the stability requirements of the CFD solver.

[0072] S4: Perform mesh redivision on the initial mesh and output an optimized anisotropic surface mesh; This step may include the following sub-steps:

[0073] S41: Detect low-quality short edges in the initial surface mesh. If there are short edges with a length less than the length threshold and the dihedral angle between adjacent cells greater than the angle threshold, perform topology optimization based on edge collapse to generate a topology-optimized mesh;

[0074] Specifically, detect low-quality short edges in the initial mesh at the rear of the airliner nacelle. When it is detected that the edge length is less than the length threshold and the dihedral angle between adjacent cells is greater than the angle threshold, perform an edge collapse operation to merge adjacent vertices.

[0075] S42: Perform local curvature gradient analysis on the topology-optimized mesh, and dynamically adjust the vertex distribution density according to the comparison result between the curvature gradient value and the density threshold to generate a density-optimized mesh;

[0076] Specifically, for the high-curvature region of the leading edge of the airliner wing, calculate the local curvature gradient value. When the curvature gradient exceeds the density threshold, adjust the vertex density.

[0077] S43: Evaluate the element quality of the density-optimized mesh. If there are elements with a quality factor lower than the quality threshold, perform anisotropic local subdivision to generate an optimized anisotropic surface mesh;

[0078] Specifically, evaluate the quality factor of the mesh elements in the airliner wing root region. For the elements with a quality factor lower than the threshold, improve the quality of the mesh elements by inserting new nodes and topological transformation, as Figure 7 shown, the quality of the optimized airliner mesh meets the stability requirements of fluid simulation.

[0079] Taking the airliner model as an example, 30,272 anisotropic surface meshes and 472,564 volume meshes are generated by this method. Figure 8 In (a) of [reference], it shows that the drag coefficient shows a monotonically decreasing trend with the increase of the number of iterations and finally stabilizes within the fluctuation range of ±0.5%; Figure 8 In (b) of [reference], it shows that the lift coefficient converges rapidly in the initial stage of iteration and the fluctuation amplitude is less than 0.3% in the later stage, verifying the computational stability of the mesh generation process.

[0080] The static pressure distribution in the engine nacelle region is as shown in Figure 9 (a) of [reference], and its gradient characteristics are highly consistent with the curvature change of the fluid shape, verifying the accurate mapping of the mesh to the geometric characteristics; Figure 9 The turbulent kinetic energy distribution shown in (b) of [reference] shows that there is no abnormal energy accumulation at the leading edge of the blade, and the energy dissipation trend conforms to the physical law; Figure 9 The velocity vector diagram in (c) of [reference] accurately analyzes the three-dimensional vortex structure of the lip separation vortex, proving the ability of the mesh to capture local flow details. The verification in the wing root region shows that: Figure 10 The pressure gradient distribution of the static pressure contour map in (a) of [reference] is in good agreement with the theoretical prediction, Figure 10 The turbulent kinetic energy in (b) of [reference] and Figure 10 The velocity distribution in (c) of [reference] both show the boundary layer evolution characteristics matching the high Reynolds number flow. The above verification results of multi-dimensional flow field characteristics confirm that the mesh generated by this method has the high-fidelity analysis ability for complex fluid phenomena.

[0081] From the above technical solutions, it can be seen that the present invention has the following beneficial effects:

[0082] Geometric defect repair ability: By dividing the boundary loop of the parameter domain of the closed surface into multiple parameter curves, merging short edges, and reconstructing the distorted surface of the parameter domain, this method can eliminate the geometric defects of the input model and ensure the rationality of the sampling point distribution during the surface discretization process. Boolean operations effectively handle geometric interference regions and avoid the generation of non-manifold meshes, thus providing topologically complete geometric input for subsequent flow field solving.

[0083] Anisotropic feature adaptation: Based on the combined criterion of zero-order geometric deviation, first-order tangential tolerance, and second-order normal tolerance, drive the dynamic subdivision of quadtree nodes along the direction with the largest feature transformation. This anisotropic encryption strategy significantly reduces redundant elements in regions with abrupt curvature changes (such as the leading edge of the blade), and at the same time improves the grid resolution along the direction with the largest feature transformation, efficiently capturing the gradient features of the flow field. When short-edge problems occur in curve discretization, the residual at the end of the parameter domain is weighted and distributed to the previous discretization segments according to the size ratio, avoiding the size mutation problem caused by traditional forced truncation and ensuring the smooth transition of the grid size.

[0084] Mesh quality and computational efficiency optimization: The Delaunay triangulation algorithm with hole constraints combines the winding number theory to accurately identify the topological relationship between the inner and outer loops, generate uniform constraint points within the bounding box of the inner loop, and effectively suppress the misjudgment of meshes in the hole region. Eliminate low-quality short edges through the edge collapse algorithm, and dynamically adjust the vertex density in combination with the local curvature gradient, significantly improving the element aspect ratio and normal consistency. During the mesh redivision stage, perform local subdivision on isotropic elements, greatly reducing the proportion of elements with a quality factor lower than the set threshold, and meeting the high-precision requirements of the CFD solver.

[0085] After considering the specification and the content disclosed herein in practice, those skilled in the art will readily think of other implementation schemes of this application. This application aims to cover any variations, uses, or adaptive changes of this application, and these variations, uses, or adaptive changes follow the general principles of this application and include the common general knowledge or conventional technical means in this technical field not disclosed in this application. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of this application are pointed out by the claims.

[0086] It should be understood that this application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended claims.

Claims

1. A surface mesh generation and adaptive re-meshing method for fluid simulation, characterized in that: include: S1: Obtain CAD fluid model data; S2: Processing the closed surface, short edge, parameter domain distortion and geometric interference problems of the CAD fluid model to obtain a defect-free continuous geometric model; S3: performing surface discretization processing on the defect-free continuous geometric model to generate an initial mesh; S4: re-meshing the initial mesh to generate an optimized anisotropic surface mesh; The defect-free continuous geometric model is subjected to surface discretization processing to generate an initial mesh, including: S31: performing boundary curve extraction processing on the defect-free continuous geometric model to obtain a boundary curve set of the surface; S32: performing adaptive curve discretization processing on the boundary curve set. If there is a problem of short ends when the curve is discretized, a smoothing algorithm is used to regard the short ends as residuals and evenly distribute them to other discrete curve segments to generate a discrete point set of the physical space boundary curve. S33: performing parameter domain mapping processing on the discrete point set of the physical space boundary curve to generate a discrete point set of the parameter space boundary curve; S34: constructing an initial bounding box based on the discrete point set of the parameter space boundary curve as a quadtree root node; S35: performing anisotropic subdivision processing on the root node of the quadtree, performing encryption operation based on the joint criterion of zero-order geometric deviation, first-order tangent tolerance, and second-order normal tolerance, and generating an anisotropic quadtree encryption lattice; S36: Based on the anisotropic quadtree encrypted point lattice and the parameter space boundary curve discrete point set, perform Delaunay triangulation processing with hole constraints to generate an initial surface mesh.

2. The method according to claim 1, characterized in that The CAD fluid model is processed for closed surfaces, short edges, parameter domain distortion and geometric interference problems to obtain a defect-free continuous geometric model, including: S21: performing closed surface detection on the CAD fluid model, and if a closed surface exists, segmenting its parameter domain and constructing related parameter curves to obtain a continuous geometric model without a closed surface; S22: performing short edge detection on the continuous geometric model without closed surfaces, and if there is a short edge whose length is less than a preset threshold, merging adjacent short edges to obtain a continuous geometric model without short edges; S23: performing parameter domain distortion detection on the continuous geometric model without short edges, and if there is a surface with parameter domain distortion, performing surface interpolation reconstruction to obtain a continuous geometric model without parameter domain distortion; S24: performing geometric interference detection on the continuous geometric model without parameter domain distortion, and if there is a geometric interference area, correcting the topological structure through Boolean operation, and finally generating a defect-free continuous geometric model.

3. The method according to claim 1, characterized in that: The initial mesh is re-meshed to generate an optimized anisotropic surface mesh, including: S41: performing low-quality short edge detection on the initial mesh, and if there is a short edge whose edge length is less than a length threshold and whose adjacent unit dihedral angle is greater than an angle threshold, performing edge-folding-based topology optimization to generate a topology optimized mesh; S42: performing local curvature gradient analysis on the topology optimized mesh, dynamically adjusting vertex distribution density according to a comparison result between a curvature gradient value and a density threshold, and generating a density optimized mesh; S43: Performing unit quality evaluation on the density optimized grid, if there are units with quality factors lower than the quality threshold, performing anisotropic local subdivision to generate an optimized anisotropic surface grid.

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