A method for generating structured hexahedral meshes by intelligent sweeping

Abstract

The present invention discloses a method for intelligently sweeping and generating structured hexahedral meshes, and specifically relates to the technical field of structured hexahedral meshes. By subdividing the characteristic regions of an aircraft model and applying different mesh generation parameters, it is ensured that the mesh density, unit size and growth rate of each region are optimized, the overall quality and accuracy of mesh generation are improved, and a sweeping path is defined. When the sweeping path passes through adjacent characteristic regions, the boundary lines of adjacent characteristic regions are obtained, and a transition region is divided according to the boundary lines of adjacent characteristic regions. When the generation region of the hexahedral mesh belongs to the transition region, the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh are obtained, and a correction judgment model is constructed according to the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, and it is determined whether to construct a mesh generation parameter correction model, and the generation parameters of the hexahedral mesh in the transition region are corrected in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of structured hexahedral meshes, and more specifically, to a method for generating structured hexahedral meshes by intelligent sweeping. Background Art

[0002] With the development of artificial intelligence and computing technology, the intelligent sweeping method for generating hexahedral meshes has emerged. This method combines the advantages of the traditional sweeping method with the flexibility of the intelligent algorithm. By intelligently analyzing and optimizing the geometric body, the quality and efficiency of generating hexahedral meshes for complex geometric bodies are improved. The core of the intelligent sweeping method is to use machine learning algorithms and optimization techniques to identify and analyze the shape characteristics of the geometric body, thereby determining the optimal sweeping path and parameters. By adaptively adjusting the sweeping direction and step size, the intelligent sweeping method can generate more regular and high-quality hexahedral meshes. The intelligent sweeping hexahedral mesh generation method has broad application prospects in engineering design, simulation and scientific computing. Aircraft shape optimization is an important task in aerospace engineering. Optimizing the shape through numerical simulation can reduce air resistance, improve flight performance, reduce fuel consumption and improve flight safety. Aircraft shape optimization depends on high-quality meshes. The intelligent sweeping hexahedral mesh generation method can generate high-quality meshes that adapt to complex shapes and ensure the accuracy and stability of numerical simulation. To generate high-quality hexahedral meshes through the intelligent sweeping hexahedral mesh generation method, you first need to use CAD software to create or import the geometric model of the aircraft, and divide the surface of the aircraft geometric model into feature areas of different scales, define the sweep Sweep path, since there are both large-scale (such as wings, fuselage) and small-scale (such as boundary layer, vortex, wingtip winglets, etc.) geometric feature areas in the aircraft model, and these feature areas have different requirements for mesh density and quality, different mesh generation parameters are usually preset for feature areas of different scales, and hexahedral unit meshes are gradually generated along the swept path. Since the mesh generation parameters preset for feature areas of different scales are different, the quality of the generated hexahedral unit meshes is also different, resulting in obvious quality gaps in the mesh quality between adjacent feature areas with large scale differences, especially for the mesh units generated at the region boundaries, distortion, distortion, non-positivity and other problems may occur, resulting in inaccurate simulation results and reduced computational efficiency.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for generating a structured hexahedral mesh by intelligent sweeping to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for generating a structured hexahedral mesh by intelligent sweeping includes the following steps:

[0007] Step S1, establishing a three-dimensional geometric model of the aircraft, and importing the three-dimensional geometric model into a mesh generation tool;

[0008] Step S2, dividing the surface of the aircraft model into a plurality of characteristic regions according to the characteristics of different parts of the aircraft model, and presetting different mesh generation parameters for different characteristic regions, the mesh generation parameters including but not limited to mesh density, mesh unit size, and growth rate;

[0009] Step S3, defining a sweep path, when the sweep path passes through adjacent feature areas, obtaining the boundary lines of the adjacent feature areas, and dividing the transition area according to the boundary lines of the adjacent feature areas;

[0010] Step S4, generating a hexahedral mesh along the defined sweeping path, when the generation area of ​​the hexahedral mesh belongs to the transition area, obtaining the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, and constructing a correction judgment model based on the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, generating a correction judgment index, comparing the correction judgment index with a preset correction judgment index threshold, determining whether to construct a mesh generation parameter correction model, and performing real-time correction on the generation parameters of the hexahedral mesh in the transition area.

[0011] In a preferred embodiment, the part features of the aircraft model include but are not limited to the fuselage, wings, boundary layer, vortex area, air inlet and engine.

[0012] In a preferred embodiment, step S3, defining a sweep path, obtaining a boundary line of adjacent feature areas when the sweep path passes through adjacent feature areas, and dividing the transition area according to the boundary line of adjacent feature areas, further includes the following steps:

[0013] Step S3-1, defining a sweeping path;

[0014] Step S3-11, determining the starting point and end point of the sweeping path;

[0015] Step S3-12, drawing a sweeping path;

[0016] Step S3-13, optimizing the sweep path;

[0017] Step S3-2, when the sweeping path passes through adjacent feature areas, obtaining the boundary line of the adjacent feature areas;

[0018] Step S3-21, identifying adjacent feature areas;

[0019] Step S3-22, extracting the boundary line;

[0020] Step S3-3, dividing the transition area according to the boundary line of adjacent feature areas;

[0021] Step S3-31, defining the transition area range;

[0022] Step S3-31, dividing the transition area.

[0023] In a preferred embodiment, the deviation information of the hexahedral mesh generation parameters includes a mesh generation parameter deviation coefficient, and the quality information of the hexahedral mesh includes a non-orthogonality anomaly coefficient and a scale gradient fluctuation coefficient;

[0024] The grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient are marked as wgs, fzy and cdb respectively.

[0025] In a preferred embodiment, the logic for obtaining the grid generation parameter deviation coefficient is as follows:

[0026] Obtain the grid generation parameters at time t in the transition area and the preset grid generation parameters in the target area, including grid density, grid cell size, and growth rate, mark the grid density, grid cell size, and growth rate at time t as wg1, dy1, and zz1, respectively, and mark the preset grid density, grid cell size, and growth rate in the target area as wg2, dy2, and zz2, respectively;

[0027] Calculate the grid generation parameter deviation coefficient, the expression is as follows

[0028] In a preferred embodiment, the logic for obtaining the non-orthogonality anomaly coefficient is as follows:

[0029] Get the vertex coordinates of each face of the newly generated hexahedral mesh unit and calculate the normal vector of each face in the hexahedral mesh unit. The expression is as follows Where F represents the normal vector of each face, AB and AC are the vectors of the two non-collinear edges that constitute the face, and × represents the cross product operation;

[0030] Calculate the angle between each pair of adjacent faces. The expression is as follows where · represents the dot product operation, θ ij Represents the angle between each pair of adjacent faces i and j;

[0031] Calculate the non-orthogonality anomaly coefficient, the expression is as follows Where e represents the order number of the pairs of adjacent faces, e = {1, 2, ..., N}, N is a positive integer, Represents the angle between the e-th pair of adjacent faces.

[0032] In a preferred embodiment, the acquisition logic of the scale gradient fluctuation coefficient is as follows:

[0033] Get each newly generated hexahedral mesh unit k, and calculate the relationship between each hexahedral mesh unit and its adjacent hexahedral mesh unit k ～ The size is expressed by volume V;

[0034] Calculate the size gradient of each hexahedral mesh element as follows Where N k represents the number of adjacent hexahedral mesh cells of hexahedral mesh cell k, sgt k represents the size gradient of the hexahedral mesh element k, V k represents the volume of hexahedral mesh unit k, V k～ represents the volume of the hexahedral mesh unit adjacent to the hexahedral mesh unit k;

[0035] Calculate the scale gradient fluctuation coefficient, the expression is as follows Wherein, M represents the number of each newly generated hexahedral mesh unit, k = {1, 2, ..., M}, and M is a positive integer.

[0036] In a preferred embodiment, the grid generation parameter deviation coefficient, the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient are normalized, and a correction judgment model is constructed based on the normalized grid generation parameter deviation coefficient, the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient to generate a correction judgment index xpz, and the formula based on which the model is as follows: Where a 1 、a 2 、a 3 They represent the preset proportional coefficients of the grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient, respectively, and a 1 、a 2 、a 3 Both are greater than 0.

[0037] In a preferred embodiment, the correction judgment index is compared with a preset correction judgment index threshold to determine whether to construct a mesh generation parameter correction model, and the generation parameters of the hexahedral mesh in the transition area are corrected in real time, as follows:

[0038] If the correction judgment index is greater than the correction judgment index threshold, a grid generation parameter correction model is constructed. The output formula of the model is as follows: Where WG represents the corrected grid density, DY represents the corrected grid unit size, ZZ represents the corrected growth rate, and xpz yz It represents the preset correction judgment index threshold value, and the generation parameters of the hexahedral mesh in the transition area are corrected in real time according to the output formula of the mesh generation parameter correction model;

[0039] If the correction judgment index is less than or equal to the correction judgment index threshold, there is no need to generate a mesh generation parameter correction model to perform real-time correction on the generation parameters of the hexahedral mesh in the transition area.

[0040] Technical effects and advantages of the present invention:

[0041] 1. The present invention subdivides the characteristic regions of the aircraft model and applies different grid generation parameters to ensure that the grid density, unit size and growth rate of each region are optimized, thereby improving the overall quality and accuracy of grid generation, and defining a sweep path. When the sweep path passes through adjacent characteristic regions, the boundary lines of the adjacent characteristic regions are obtained, and the transition region is divided according to the boundary lines of the adjacent characteristic regions. When the generation region of the hexahedral grid belongs to the transition region, the deviation information of the hexahedral grid generation parameters and the quality information of the hexahedral grid are obtained, and a correction judgment model is constructed according to the deviation information of the hexahedral grid generation parameters and the quality information of the hexahedral grid, and a correction judgment index is generated. The correction judgment index is compared with a preset correction judgment index threshold to determine whether to construct a grid generation parameter correction model, and the generation parameters of the hexahedral grid in the transition region are corrected in real time. By correcting the grid generation parameters in real time, grid quality problems such as distortion, distortion and non-positivity caused by changes in grid density and unit size are avoided, the accuracy and stability of numerical simulation are improved, unnecessary high or low grid density is avoided, the amount of calculation is reduced, the calculation efficiency of numerical simulation is improved, and the simulation time is shortened.

[0042] 2. The present invention is applicable to aircraft geometric models of different types and complexities. By adjusting the mesh generation parameters in real time through the mesh generation parameter correction model, it can flexibly adapt to different engineering needs and simulation scenarios, and process large-scale and small-scale feature areas in the aircraft shape, such as wings, fuselage, boundary layer, eddy current area, etc., so that the aircraft shape with complex geometric shapes can generate more adaptable hexahedral meshes. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0044] Figure 1 Schematic diagram of the structure of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example

[0047] Figure 1 The present invention provides a method for generating a structured hexahedral mesh by intelligent sweeping, comprising the following steps:

[0048] Step S1, establishing a three-dimensional geometric model of the aircraft, and importing the three-dimensional geometric model into a mesh generation tool;

[0049] Obtain the 2D drawings of the aircraft, import them into the CAD software, and start to build the 3D geometric model of the aircraft from the 2D drawings, including basic components such as the fuselage, wings, tail, air intake, and engine;

[0050] Add details and features such as boundary layer features, vortex generators, winglets, and more.

[0051] Surface and edge processing, smooth transition areas, process complex surfaces and edges, and ensure that the model meets aerodynamic design requirements;

[0052] It should be noted that the two-dimensional drawings of the aircraft are provided by the manufacturer. Common CAD software includes CATIA, SolidWorks, AutoCAD, Pro / ENGINEER, etc., which can be selected according to actual conditions. The construction method of the three-dimensional geometric model of the aircraft includes manual or automatic construction method;

[0053] In an alternative example:

[0054] Manual build method:

[0055] Sketching: In CAD software, sketch according to the view in the 2D drawing, and use sketch tools to draw the outlines and key feature lines of each component;

[0056] Extrude and Revolve: For the fuselage, you can use the Extrude or Revolve function to generate a 3D solid from a 2D sketch. For the wings and tail, you can use the Extrude function to generate a 3D geometry from the airfoil profile.

[0057] Boolean operations: Use Boolean operations (addition, subtraction, intersection) to combine individual components into a complete 3D model, for example, combining the air intake and the fuselage and removing the redundant parts;

[0058] Automatic build mode:

[0059] Use automatic modeling tools: If the selected CAD software provides automatic modeling tools, you can generate a 3D model directly from a 2D drawing. After importing the 2D drawing, use the automatic modeling function to generate a 3D geometric model based on the dimensions and feature lines in the drawing.

[0060] Select a suitable export format to export the constructed aircraft 3D geometry model, start the mesh generation tool, import the exported aircraft 3D geometry model into the mesh generation tool, and check the imported aircraft 3D geometry model to ensure that the model is complete and correct without any missing or deformation.

[0061] Step S2, dividing the surface of the aircraft model into a plurality of characteristic regions according to the characteristics of different parts of the aircraft model, and presetting different mesh generation parameters for different characteristic regions, the mesh generation parameters including but not limited to mesh density, mesh unit size, and growth rate;

[0062] The part features of the aircraft model include but are not limited to the fuselage, wings, boundary layer, vortex area, air inlet and engine;

[0063] Use the marking function in the mesh generation tool to manually or automatically divide the surface of the aircraft model into different feature areas according to the features of different parts of the aircraft model, and assign a unique label or color to each feature area;

[0064] Different grid generation parameters are preset for different feature areas, and the grid generation parameters include but are not limited to grid density, grid cell size, and growth rate;

[0065] In the mesh generation tool, enter the preset mesh generation parameters for each feature area;

[0066] Mesh density: refers to the distribution density of mesh units in the characteristic area. Critical areas (such as boundary layers and eddy regions) require higher mesh density.

[0067] Grid cell size: refers to the size of a single grid cell. Small-scale feature areas require smaller grid cells, while large-scale feature areas can use larger grid cells.

[0068] Growth rate: refers to the rate of change of the grid cell size from one feature area to another;

[0069] Step S3, defining a sweep path, when the sweep path passes through adjacent feature areas, obtaining the boundary lines of the adjacent feature areas, and dividing the transition area according to the boundary lines of the adjacent feature areas;

[0070] Step S3-1, defining a sweeping path;

[0071] Step S3-11, determine the starting point and end point of the sweep path: select appropriate starting point and end point according to the structure of the three-dimensional geometric model of the aircraft. For example, the sweep path of the fuselage can be from the nose to the tail, and the sweep path of the wing can be from the wing root to the wing tip.

[0072] Step S3-12, drawing the sweep path: Use the sweep path definition function in the mesh generation tool to draw the path, ensuring that the path is smooth and continuous, and try to follow the main streamline direction of the aircraft. For complex geometric shapes, the sweep path can be defined in segments to ensure that the path can cover all feature areas;

[0073] Step S3-13, optimizing the sweep path: using the optimization function of the mesh generation tool to adjust the sweep path, ensuring that the path fits the surface of the aircraft well and avoiding the path being too tortuous or discrete;

[0074] Step S3-2, when the sweeping path passes through adjacent feature areas, obtaining the boundary line of the adjacent feature areas;

[0075] Step S3-21, identifying adjacent feature regions: in the mesh generation tool, determine the boundary of each feature region, identify the boundary line of adjacent feature regions, use feature region labels or colors to distinguish adjacent regions, and ensure that the boundary line is clearly visible;

[0076] Step S3-22, extracting boundary lines: using the boundary extraction function of the mesh generation tool to extract boundary lines between adjacent feature areas, and marking the extracted boundary lines as feature lines for subsequent processing;

[0077] Step S3-3, dividing the transition area according to the boundary line of adjacent feature areas;

[0078] Step S3-31, defining the range of the transition region: determining the width of the transition region according to the boundary line of adjacent feature regions. Usually, the transition region is located on both sides of the boundary line, and the width can be determined according to the difference in grid density and grid unit size;

[0079] In an alternative example, the transition region width is calculated as follows Where W represents the width of the transition area, K represents the adjustment factor, which is used to balance the influence of the grid density and cell size difference on the width of the transition area according to the actual situation, ΔD represents the difference value of the grid density of adjacent feature areas, and ΔS represents the difference value of the grid cell size of adjacent feature areas;

[0080] Step S3-31, dividing the transition area: dividing the transition area on both sides of the boundary line of adjacent feature areas according to the acquired transition area width, and using transition area labels or colors to distinguish the feature areas;

[0081] Step S4, generating a hexahedral mesh along the defined sweep path, when the generation area of ​​the hexahedral mesh belongs to the transition area, obtaining the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, and constructing a correction judgment model according to the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, generating a correction judgment index, comparing the correction judgment index with a preset correction judgment index threshold, determining whether to construct a mesh generation parameter correction model, and performing real-time correction on the generation parameters of the hexahedral mesh in the transition area;

[0082] The deviation information of the hexahedral mesh generation parameters includes the mesh generation parameter deviation coefficient, and the quality information of the hexahedral mesh includes the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient;

[0083] The grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient, and scale gradient fluctuation coefficient are marked as wgs, fzy, and cdb, respectively;

[0084] The mesh generation parameter deviation coefficient is used to measure the real-time deviation of the mesh generation parameters of the hexahedral mesh generated from one side of the transition area to the other side, so as to adjust the mesh generation parameters in real time to adapt to the requirements of the transition area. Specifically, the deviation coefficient measures the deviation of the parameters of the hexahedral mesh generated from one side of the transition area to the other side, which helps to ensure the consistency and quality of the hexahedral mesh while smoothly transitioning between feature areas.

[0085] The mesh generation parameter deviation coefficient is usually a dimensionless value, which can be determined by calculating the difference of the hexahedral mesh parameters in the transition area. The smaller the mesh generation parameter deviation coefficient is, the smaller the mesh generation parameter deviation is, the higher the quality and stability of the mesh is, the smoother the transition between its characteristic areas is, and the smaller the probability of correcting the mesh generation parameters is. Conversely, the larger the mesh generation parameter deviation coefficient is, the larger the mesh generation parameter deviation is, the lower the quality and stability of the mesh is, the less smooth the transition between its characteristic areas is, and the greater the probability of correcting the mesh generation parameters is.

[0086] The logic for obtaining the grid generation parameter deviation coefficient is as follows:

[0087] Obtain the grid generation parameters at time t in the transition area and the preset grid generation parameters in the target area, including grid density, grid cell size, and growth rate, mark the grid density, grid cell size, and growth rate at time t as wg1, dy1, and zz1, respectively, and mark the preset grid density, grid cell size, and growth rate in the target area as wg2, dy2, and zz2, respectively;

[0088] It should be noted that the mesh generation parameters at time t refer to the real-time mesh generation parameters that need to generate the hexahedral mesh in the transition area at the current time. If the mesh generation parameters have been corrected, the mesh generation parameters at the current time are the corrected values; if the mesh generation parameters have not been corrected, the mesh generation parameters at the current time are the values ​​at time t-1; the target area refers to the feature area on the other side of the hexahedral mesh generation area at this time;

[0089] Calculate the grid generation parameter deviation coefficient, the expression is as follows

[0090] The non-orthogonality anomaly coefficient is used to measure the degree of anomaly of the angles between each face of the generated hexahedral mesh unit and the adjacent hexahedral mesh units. Hexahedral meshes that are close to orthogonality usually have better quality and stability in numerical simulations.

[0091] Specifically, the non-orthogonality anomaly coefficient is a numerical indicator that describes the degree to which the angles between the faces of the hexahedral mesh unit deviate from orthogonality. If the faces of the hexahedral mesh unit are close to perpendicular to the faces of the adjacent units, the non-orthogonality anomaly coefficient is low, the quality and stability of the mesh are higher, the transitions between its feature regions are smoother, and the probability of correcting the mesh generation parameters is lower. On the contrary, if there is a large angle deviation, the non-orthogonality anomaly coefficient is high, indicating that the quality of the mesh may be poor. The lower the quality and stability of the mesh, the less smooth the transitions between its feature regions, and the greater the probability of correcting the mesh generation parameters.

[0092] The logic for obtaining the non-orthogonality anomaly coefficient is as follows:

[0093] Get the vertex coordinates of each face of the newly generated hexahedral mesh unit and calculate the normal vector of each face in the hexahedral mesh unit. The expression is as follows Where F represents the normal vector of each face, AB and AC are the vectors of the two non-collinear edges that constitute the face, and × represents the cross product operation;

[0094] In an alternative example, suppose that for three points B on a surface 1 (x 1 ,y 1 ,z 1 ), B 2 (x 2 ,y 2 ,z 2 ) and B 3 (x 3 ,y 3 ,z 3 ), then AB=(x 2 -x 1 ,y 2-y 1 ,z 2 -z 1 ), AC=(x 3 -x 1 ,y 3 -y 1 ,z 3 -z 1 );

[0095] Calculate the angle between each pair of adjacent faces. The expression is as follows where · represents the dot product operation, θ ij Represents the angle between each pair of adjacent faces i and j;

[0096] Calculate the non-orthogonality anomaly coefficient, the expression is as follows Where e represents the order number of the pairs of adjacent faces, e = {1, 2, ..., N}, N is a positive integer, represents the angle between the e-th pair of adjacent faces;

[0097] The scale gradient fluctuation coefficient is used to measure the degree of fluctuation of the size change of adjacent hexahedral grid units, reflecting the smoothness and consistency of the size change of the hexahedral grid. A smaller scale gradient fluctuation coefficient means that the size change of adjacent hexahedral grid units is more uniform, the grid quality is better, the transition between its characteristic areas is smoother, and the probability of correcting the grid generation parameters is smaller; a larger scale gradient fluctuation coefficient indicates that the size change of the hexahedral grid units is uneven, which may lead to a decrease in the accuracy and stability of the numerical simulation. The less smooth the transition between its characteristic areas, the greater the probability of correcting the grid generation parameters.

[0098] The logic for obtaining the scale gradient fluctuation coefficient is as follows:

[0099] Get each newly generated hexahedral mesh unit k, and calculate the relationship between each hexahedral mesh unit and its adjacent hexahedral mesh unit k ～ The size is expressed by volume V;

[0100] In an optional example, calculating the volume of a hexahedral grid cell includes the following steps:

[0101] A hexahedral mesh unit is usually defined by eight vertices. Assume that the coordinates of these eight vertices are P 1 , P 2 , P 3 , P 4 , P 5 , P 6 , P 7 , P 8 ;

[0102] Decompose the hexahedron into five tetrahedrons, and calculate the sum of the volumes of the five tetrahedrons to get the volume of the entire hexahedron.

[0103] Assume that the vertices of the hexahedron are connected as follows:

[0104] Vertex P 1 , P 2 , P 3 , P 4 Constitute a face, vertex P 5 , P 6 , P 7 , P 8 To form a face, press P between vertices 1 --P 5 , P 2 --P 6 , P 3 --P 7 , P 4 --P 8 connect;

[0105] Select the following five tetrahedrons and calculate the volume of each tetrahedron:

[0106] 1. {P 1 , P 2 , P 3 , P 5},

[0107] 2. {P 2 , P 3 , P 6 , P 5},

[0108] 3.{P 3 , P 4 , P 8 , P 5},

[0109] 4. {P 3 , P 6 , P 7 , P 8},

[0110] 5.{P 3 , P 5 , P 6 , P 8},

[0111] Calculate the volume of the hexahedral grid unit, the expression is as follows V = V 1 +V2 +V 3 +V 4 +V 5 ;

[0112] Calculate the size gradient of each hexahedral mesh element as follows Where N k represents the number of adjacent hexahedral mesh cells of hexahedral mesh cell k, sgt k represents the size gradient of the hexahedral mesh element k, V k represents the volume of hexahedral mesh unit k, V k～ represents the volume of the hexahedral mesh unit adjacent to the hexahedral mesh unit k;

[0113] Calculate the scale gradient fluctuation coefficient, the expression is as follows Where M represents the number of newly generated hexahedral mesh units, k = {1, 2, ..., M}, M is a positive integer;

[0114] The grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient are normalized, and a correction judgment model is constructed based on the normalized grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient to generate a correction judgment index xpz. The model is based on the following formula: Where a 1 、a 2 、a 3 They represent the preset proportional coefficients of the grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient, respectively, and a 1 、a 2 、a 3 All are greater than 0;

[0115] From the above calculation expression, it can be seen that the larger the grid generation parameter deviation coefficient, the larger the non-orthogonality anomaly coefficient, and the larger the scale gradient fluctuation coefficient, the larger the correction judgment index, indicating that the transition between the feature areas is less smooth, and the probability of correcting the grid generation parameters is greater. On the contrary, the smaller the grid generation parameter deviation coefficient, the smaller the non-orthogonality anomaly coefficient, and the smaller the scale gradient fluctuation coefficient, the smaller the correction judgment index, indicating that the transition between the feature areas is smoother, and the probability of correcting the grid generation parameters is smaller.

[0116] The correction judgment index is compared with the preset correction judgment index threshold to determine whether to build a mesh generation parameter correction model and perform real-time correction on the generation parameters of the hexahedral mesh in the transition area, as follows:

[0117] If the correction judgment index is greater than the correction judgment index threshold, it means that the transition between the feature areas is not smoother. A mesh generation parameter correction model is constructed. The output formula of the model is as follows: Where WG represents the corrected grid density, DY represents the corrected grid unit size, ZZ represents the corrected growth rate, and xpz yz It represents the preset correction judgment index threshold value, and the generation parameters of the hexahedral mesh in the transition area are corrected in real time according to the output formula of the mesh generation parameter correction model;

[0118] If the correction judgment index is less than or equal to the correction judgment index threshold, it indicates that the transition between the feature regions is smoother, and there is no need to generate a mesh generation parameter correction model to perform real-time correction on the generation parameters of the hexahedral mesh in the transition region;

[0119] The present invention subdivides the characteristic area of ​​the aircraft model and applies different grid generation parameters to ensure that the grid density, unit size and growth rate of each area are optimized, improve the overall quality and accuracy of grid generation, and define a sweep path. When the sweep path passes through adjacent characteristic areas, the boundary lines of the adjacent characteristic areas are obtained, and the transition area is divided according to the boundary lines of the adjacent characteristic areas. When the generation area of ​​the hexahedral grid belongs to the transition area, the deviation information of the hexahedral grid generation parameters and the quality information of the hexahedral grid are obtained, and a correction judgment model is constructed according to the deviation information of the hexahedral grid generation parameters and the quality information of the hexahedral grid, and a correction judgment index is generated. The correction judgment index is compared with a preset correction judgment index threshold to determine whether to construct a grid generation parameter correction model, and the generation parameters of the hexahedral grid in the transition area are corrected in real time. By correcting the grid generation parameters in real time, grid quality problems such as distortion, distortion and non-positivity caused by changes in grid density and unit size are avoided, the accuracy and stability of numerical simulation are improved, unnecessary high or low grid density is avoided, the amount of calculation is reduced, the calculation efficiency of numerical simulation is improved, and the simulation time is shortened.

[0120] The present invention is applicable to aircraft geometric models of different types and complexities. By adjusting the mesh generation parameters in real time through the mesh generation parameter correction model, it can flexibly adapt to different engineering needs and simulation scenarios, and process large-scale and small-scale feature areas in the aircraft shape, such as wings, fuselage, boundary layer, eddy current area, etc., so that the aircraft shape with complex geometric shapes can generate more adaptable hexahedral meshes.

[0121] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0122] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for generating structured hexahedral meshes by intelligent sweeping, characterized in that: The steps include: Step S1, establishing a three-dimensional geometric model of the aircraft, and importing the three-dimensional geometric model into a mesh generation tool; Step S2, dividing the surface of the aircraft model into a plurality of characteristic regions according to the characteristics of different parts of the aircraft model, and presetting different mesh generation parameters for different characteristic regions, the mesh generation parameters including but not limited to mesh density, mesh unit size, and growth rate; Step S3, defining a sweep path, when the sweep path passes through adjacent feature areas, obtaining the boundary lines of the adjacent feature areas, and dividing the transition area according to the boundary lines of the adjacent feature areas; Step S4, generating a hexahedral mesh along the defined sweeping path, when the generation area of ​​the hexahedral mesh belongs to the transition area, obtaining the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, and constructing a correction judgment model based on the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, generating a correction judgment index, comparing the correction judgment index with a preset correction judgment index threshold, determining whether to construct a mesh generation parameter correction model, and performing real-time correction on the generation parameters of the hexahedral mesh in the transition area.

2. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 1, characterized in that: The part features of the aircraft model include but are not limited to the fuselage, wings, boundary layer, vortex area, air inlet and engine.

3. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 2, characterized in that: Step S3, defining a sweep path, when the sweep path passes through adjacent feature areas, obtaining the boundary lines of the adjacent feature areas, and dividing the transition area according to the boundary lines of the adjacent feature areas, further comprising the following steps: Step S3-1, defining a sweeping path; Step S3-11, determining the starting point and the end point of the sweeping path; Step S3-12, drawing a sweeping path; Step S3-13, optimizing the sweep path; Step S3-2, when the sweeping path passes through adjacent feature areas, obtaining the boundary line of the adjacent feature areas; Step S3-21, identifying adjacent feature areas; Step S3-22, extracting the boundary line; Step S3-3, dividing the transition area according to the boundary line of adjacent feature areas; Step S3-31, defining the transition area range; Step S3-31, dividing the transition area.

4. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 1, characterized in that: The deviation information of the hexahedral mesh generation parameters includes the mesh generation parameter deviation coefficient, and the quality information of the hexahedral mesh includes the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient; The grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient are marked as wgs, fzy and cdb respectively.

5. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 4, characterized in that: The logic for obtaining the grid generation parameter deviation coefficient is as follows: Obtain the grid generation parameters at time t in the transition area and the preset grid generation parameters in the target area, including grid density, grid cell size, and growth rate, mark the grid density, grid cell size, and growth rate at time t as wg1, dy1, and zz1, respectively, and mark the preset grid density, grid cell size, and growth rate in the target area as wg2, dy2, and zz2, respectively; Calculate the grid generation parameter deviation coefficient, the expression is as follows 6. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 5, characterized in that: The logic for obtaining the non-orthogonality anomaly coefficient is as follows: Get the vertex coordinates of each face of the newly generated hexahedral mesh unit and calculate the normal vector of each face in the hexahedral mesh unit. The expression is as follows Where F represents the normal vector of each face, AB and AC are the vectors of the two non-collinear edges that constitute the face, and × represents the cross product operation; Calculate the angle between each pair of adjacent faces. The expression is as follows where · represents the dot product operation, θ ij Represents the angle between each pair of adjacent faces i and j; Calculate the non-orthogonality anomaly coefficient, the expression is as follows Where e represents the order number of the pairs of adjacent faces, e = {1, 2, ..., N}, N is a positive integer, Represents the angle between the e-th pair of adjacent faces.

7. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 6, characterized in that: The logic for obtaining the scale gradient fluctuation coefficient is as follows: Get each newly generated hexahedral mesh unit k, and calculate the relationship between each hexahedral mesh unit and its adjacent hexahedral mesh unit k ～ The size is expressed by volume V; Calculate the size gradient of each hexahedral mesh element as follows Where N k represents the number of adjacent hexahedral mesh cells of hexahedral mesh cell k, sgt k represents the size gradient of the hexahedral mesh element k, V k represents the volume of hexahedral mesh unit k, V k～ represents the volume of the hexahedral mesh unit adjacent to the hexahedral mesh unit k; Calculate the scale gradient fluctuation coefficient, the expression is as follows Wherein, M represents the number of each newly generated hexahedral mesh unit, k = {1, 2, ..., M}, and M is a positive integer.

8. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 7, characterized in that: The grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient are normalized, and a correction judgment model is constructed based on the normalized grid generation parameter deviation coefficient, non-orthogonality anomaly coefficient and scale gradient fluctuation coefficient to generate a correction judgment index xpz. The model is based on the following formula: Where a1, a2, and a3 represent the preset proportional coefficients of the grid generation parameter deviation coefficient, the non-orthogonality anomaly coefficient, and the scale gradient fluctuation coefficient, respectively, and a1, a2, and a3 are all greater than 0.

9. The method for generating structured hexahedral mesh by intelligent sweeping according to claim 8, characterized in that: The correction judgment index is compared with the preset correction judgment index threshold to determine whether to build a mesh generation parameter correction model and perform real-time correction on the generation parameters of the hexahedral mesh in the transition area, as follows: If the correction judgment index is greater than the correction judgment index threshold, a grid generation parameter correction model is constructed. The output formula of the model is as follows: Where WG represents the corrected grid density, DY represents the corrected grid unit size, ZZ represents the corrected growth rate, and xpz yz It represents the preset correction judgment index threshold value, and the generation parameters of the hexahedral mesh in the transition area are corrected in real time according to the output formula of the mesh generation parameter correction model; If the correction judgment index is less than or equal to the correction judgment index threshold, there is no need to generate a mesh generation parameter correction model to perform real-time correction on the generation parameters of the hexahedral mesh in the transition area.

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