CN118644638B - A method for generating structured hexahedral meshes by intelligent sweeping - Google Patents
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Abstract
The invention discloses an intelligent sweep generation structured hexahedral mesh method, which particularly relates to the technical field of structured hexahedral meshes, and ensures that the mesh density, the cell size and the growth rate of each area are optimized by subdividing the characteristic area of an aircraft model and applying different mesh generation parameters, improves the overall quality and the precision of mesh generation, defines a sweep path, acquires the boundary line of the adjacent characteristic area when the sweep path passes through the adjacent characteristic area, divides a transition area according to the boundary line of the adjacent characteristic area, acquires the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh when the generation area of the hexahedral mesh belongs to the transition area, constructs a correction judgment model according to the deviation information of the hexahedral mesh generation parameters and the quality information of the hexahedral mesh, determines whether to construct a mesh generation parameter correction model, and corrects the generation parameters of the hexahedral mesh in the transition area in real time.
Description
Technical Field
The invention relates to the technical field of structured hexahedral meshes, in particular to a method for generating a structured hexahedral mesh by intelligent sweeping.
Background
With the development of artificial intelligence and computing technology, intelligent sweep generation hexahedral mesh methods have been developed. The method combines the advantages of the traditional sweeping method and the flexibility of an intelligent algorithm, improves the quality and efficiency of generating the hexahedral mesh of the complex geometry by intelligently analyzing and optimizing the geometry, and is characterized in that the shape characteristics of the geometry are identified and analyzed by utilizing a machine learning algorithm and an optimizing technology, so that the optimal sweeping path and parameters are determined. By adaptively adjusting the sweep direction and step size, the intelligent sweep method can generate a hexahedral mesh with more regularity and high quality. The intelligent sweep generation hexahedral mesh method has wide application prospect in the fields of engineering design, simulation, scientific calculation and the like, the appearance optimization of an aircraft is an important task in aerospace engineering, the air resistance can be reduced, the flight performance can be improved, the fuel consumption can be reduced and the flight safety can be improved through numerical simulation optimization of the appearance, the appearance optimization of the aircraft depends on high-quality meshes, the intelligent sweep generation hexahedral mesh method can generate high-quality meshes adapting to complex appearance, the accuracy and the stability of numerical simulation are ensured, the hexahedral mesh method is generated by the intelligent sweep generation hexahedral mesh method, firstly, a geometric model of the aircraft is required to be created or imported through CAD software, the surface of the geometric model of the aircraft is divided into characteristic areas with different dimensions, a sweep path is defined, and because the geometric characteristic areas with large dimensions (such as wings, fuselages and wings and the like) and small dimensions (such as boundary layers, vortex and wingtips and the like) exist in the aircraft model at the same time, the characteristic areas have different requirements on the mesh density and the quality, different generation parameters can be preset for the characteristic areas with different dimensions, the hexahedral mesh areas with different dimensions can be generated gradually along the sweep path, the geometric areas with different generation parameters, the different quality characteristics of the hexahedral mesh units can be generated, the different mesh areas with different quality distortion can be generated, and the accurate distortion can be caused, especially, the problem of the grid area with different quality distortion can be generated, and the quality distortion can be reduced, and the quality distortion of the adjacent mesh area is caused.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides a method for generating a structured hexahedral mesh by intelligent sweep, so as to solve the above-mentioned problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
An intelligent sweep generation structured hexahedral mesh method, comprising the steps of:
Step S1, a three-dimensional geometric model of an aircraft is established, and the three-dimensional geometric model is imported into a grid generating tool;
Step S2, dividing the surface of the aircraft model into a plurality of characteristic areas according to different part characteristics of the aircraft model, and presetting different grid generation parameters for different characteristic areas, wherein the grid generation parameters comprise, but are not limited to, grid density, grid unit size and growth rate;
step S3, defining a sweep path, acquiring the boundary line of the adjacent characteristic areas when the sweep path passes through the adjacent characteristic areas, and dividing a transition area according to the boundary line of the adjacent characteristic areas;
And S4, generating a hexahedral mesh along a defined sweep path, when a generation area of the hexahedral mesh is affiliated to a transition area, acquiring deviation information of generation parameters of the hexahedral mesh and quality information of the hexahedral mesh, constructing a correction judgment model according to the deviation information of the generation parameters of the hexahedral mesh 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 value, determining whether to construct a mesh generation parameter correction model, and carrying out real-time correction on the generation parameters of the hexahedral mesh in the transition area.
In a preferred embodiment, the site features of the aircraft model include, but are not limited to, fuselage, wing, boundary layer, vortex region, air intake, and engine.
In a preferred embodiment, step S3, defining a sweep path, when the sweep path passes through adjacent feature areas, obtaining the boundary line of the adjacent feature areas, and dividing the transition area according to the boundary line of the adjacent feature areas, further includes the steps of:
Step S3-1, defining a sweep path;
S3-11, determining a starting point and an end point of a sweeping path;
s3-12, drawing a sweep path;
s3-13, optimizing a sweep path;
Step S3-2, when the sweep path passes through the adjacent characteristic areas, acquiring the boundary lines of the adjacent characteristic areas;
s3-21, identifying adjacent characteristic areas;
S3-22, extracting boundary lines;
S3-3, dividing a transition region according to the boundary line of the adjacent characteristic regions;
s3-31, defining a transition area range;
and step S3-31, dividing the transition area.
In a preferred embodiment, the deviation information of the hexahedral mesh generating parameter includes a mesh generating parameter deviation coefficient, and the quality information of the hexahedral mesh includes a non-orthogonality anomaly coefficient and a scale gradient fluctuation coefficient;
the grid generation parameter deviation coefficient, the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient are respectively marked as wgs, fzy, cdb.
In a preferred embodiment, the grid-generated parameter deviation factor acquisition logic is as follows:
Acquiring grid generation parameters at the time t in a transition area and preset grid generation parameters in a target area, wherein the grid generation parameters comprise grid density, grid cell size and growth rate, the grid density, the grid cell size and the growth rate at the time t are respectively marked as wg1, dy1 and zz1, and the grid density, the grid cell size and the growth rate preset in the target area are respectively marked as wg2, dy2 and zz2;
Calculating the deviation coefficient of the grid generation parameter, and the expression is as follows
In a preferred embodiment, the non-orthogonality anomaly coefficient acquisition logic is as follows:
obtaining vertex coordinates of each face of the newly generated hexahedral mesh unit, calculating a normal vector of each face in the hexahedral mesh unit, and expressing the normal vector as follows Where F represents the normal vector of each face, AB and AC are vectors of two non-collinear edges that make up the face, x represents the cross product operation;
calculating the included angle value between each pair of adjacent surfaces, and the expression is as follows Wherein, represents dot product operation, and theta ij represents the included angle value of each pair of adjacent surfaces i and j;
calculating non-orthogonality anomaly coefficients, expressed as follows Where e denotes the order number of the logarithm of the adjacent faces, e= {1, 2..once., N }, N being a positive integer,Representing the value of the angle between the e-th pair of adjacent faces.
In a preferred embodiment, the acquisition logic for the scale gradient fluctuation coefficients is as follows:
Obtaining each newly generated hexahedral mesh unit k, calculating the size of each hexahedral mesh unit k ～ adjacent to the hexahedral mesh unit k, and representing the size by using a volume V;
the size gradient of each hexahedral mesh unit is calculated as follows Where N k denotes the number of adjacent hexahedral mesh cells of the hexahedral mesh cell k, sgt k denotes the size gradient of the hexahedral mesh cell k, V k denotes the volume of the hexahedral mesh cell k, and V k～ denotes the volume of the hexahedral mesh cell adjacent to the hexahedral mesh cell k;
calculating the scale gradient fluctuation coefficient, and the expression is as follows Where M represents the number of each hexahedral mesh unit newly generated, k= {1, 2,..and M }, and M is a positive integer.
In a preferred embodiment, the grid generation parameter deviation coefficient, the non-orthogonality abnormal coefficient and the scale gradient fluctuation coefficient are normalized, and a correction judgment model is constructed according to the normalized grid generation parameter deviation coefficient, the normalized non-orthogonality abnormal coefficient and the scale gradient fluctuation coefficient, and a correction judgment index xpz is generated according to the following formulaWherein a 1、a2、a3 respectively represents a preset proportionality coefficient of a grid generation parameter deviation coefficient, a non-orthogonality abnormal coefficient and a scale gradient fluctuation coefficient, and a 1、a2、a3 is larger than 0.
In a preferred embodiment, the correction judgment index is compared with a preset correction judgment index threshold value 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, which is specifically as follows:
If the correction judgment index is larger than the correction judgment index threshold, constructing a grid generation parameter correction model according to the following output formula Wherein WG represents the corrected grid density, DY represents the corrected grid cell size, ZZ represents the corrected growth rate, xpz yz represents a preset correction judgment index threshold value, and the generation parameters of the hexahedral grid in the transition region are corrected in real time according to the output formula of the grid generation parameter correction model;
If the correction judgment index is smaller than or equal to the correction judgment index threshold, the generation parameters of the hexahedral mesh in the transition region are corrected in real time without generating a mesh generation parameter correction model.
The invention has the technical effects and advantages that:
1. According to the invention, the characteristic regions of the aircraft model are subdivided and different grid generation parameters are applied, so that the grid density, the unit size and the growth rate of each region are optimized, the overall quality and the accuracy of grid generation are improved, a sweep path is defined, when the sweep path passes through the adjacent characteristic regions, the boundary line of the adjacent characteristic regions is obtained, the transition region is divided according to the boundary line 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, 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, a correction judgment index is generated, the correction judgment index is compared with a preset correction judgment index threshold value, whether the grid generation parameter correction model is constructed is determined, the generation parameters of the hexahedral grid in the transition region are corrected in real time, the grid quality problems caused by the grid density and the unit size change, such as distortion, distortion and non-positive are avoided, the accuracy and stability of numerical simulation are improved, unnecessary density is avoided, the calculated calculation quantity is reduced, and the simulation calculation time is shortened.
2. The method is suitable for geometric models of aircrafts with different types and complexity, and the grid generation parameters are adjusted in real time through the grid generation parameter correction model, so that the method can flexibly adapt to different engineering requirements and simulation scenes, and large-scale and small-scale characteristic areas such as wings, fuselages, boundary layers, vortex areas and the like existing in the appearance of the aircrafts are processed, so that the appearance of the aircrafts with complex geometric shapes can generate hexahedral grids with stronger adaptability.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a schematic diagram of a method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Fig. 1 shows a method for generating a structured hexahedral mesh by intelligent sweep according to the present invention, comprising the steps of:
Step S1, a three-dimensional geometric model of an aircraft is established, and the three-dimensional geometric model is imported into a grid generating tool;
Acquiring a two-dimensional drawing of an aircraft, and importing the two-dimensional drawing of the aircraft into CAD software, wherein the two-dimensional drawing of the aircraft begins to construct a three-dimensional geometric model of the aircraft, and the three-dimensional geometric model comprises basic components such as a fuselage, wings, tail wings, an air inlet, an engine and the like;
Details and features are added such as boundary layer features, vortex generators, winglets, etc.
The curved surface and the edge are processed, the transition area is smoothed, the complex curved surface and the edge are processed, and the model is ensured to meet the aerodynamic design requirement;
It should be noted that, the two-dimensional drawing of the aircraft is provided by a manufacturer, common CAD software comprises CATIA, solidWorks, autoCAD, pro/ENGINEER and the like, which can be selected according to actual conditions, and the construction mode of the three-dimensional geometric model of the aircraft comprises a manual or automatic construction mode;
in an alternative example:
the manual construction mode comprises the following steps:
Drawing a sketch, namely drawing the sketch according to the view in the two-dimensional drawing in CAD software, and drawing the outline and key characteristic lines of each component by using a sketch tool;
stretching and rotation, wherein for the fuselage, a stretching or rotation function can be used to generate a three-dimensional entity from a two-dimensional sketch, and for the wing and tail wing, a stretching function can be used to generate a three-dimensional geometry from an airfoil profile;
Boolean operation, namely combining each component into a complete three-dimensional model by using Boolean operation (addition, subtraction and intersection), for example, combining an air inlet and a machine body to remove redundant parts;
the automatic construction mode comprises the following steps:
and using an automatic modeling tool, namely if the selected CAD software provides the automatic modeling tool, directly generating a three-dimensional model from the two-dimensional drawing, importing the two-dimensional drawing, and then using an automatic modeling function to generate a three-dimensional geometric model according to the size and the characteristic line in the drawing.
Selecting a proper export format to export the constructed three-dimensional geometric model of the aircraft, starting a grid generating tool, importing the exported three-dimensional geometric model of the aircraft into the grid generating tool, and checking the imported three-dimensional geometric model of the aircraft to ensure that the model is complete and free from errors and missing or deformation;
Step S2, dividing the surface of the aircraft model into a plurality of characteristic areas according to different part characteristics of the aircraft model, and presetting different grid generation parameters for different characteristic areas, wherein the grid generation parameters comprise, but are not limited to, grid density, grid unit size and growth rate;
site features of an aircraft model include, but are not limited to, fuselage, wing, boundary layer, vortex region, air intake, and engine;
Marking functions in a grid generating tool are used according to different part characteristics of the aircraft model, the surface of the aircraft model is divided into different characteristic areas manually or automatically, and unique labels or colors are distributed to each characteristic area;
presetting different grid generation parameters for different characteristic areas, wherein the grid generation parameters comprise, but are not limited to, grid density, grid cell size and growth rate;
In a grid generation tool, inputting grid generation parameters preset for each characteristic region;
Grid density, which is to say, the distribution density of grid units in a characteristic area, and a critical area (such as a boundary layer and a vortex area) needs higher grid density;
Grid cell size, which refers to the size of a single grid cell, small-scale feature areas require smaller grid cells, and large-scale feature areas can use larger grid cells;
growth rate, which is the rate of change of the grid cell size from one feature region to another;
step S3, defining a sweep path, acquiring the boundary line of the adjacent characteristic areas when the sweep path passes through the adjacent characteristic areas, and dividing a transition area according to the boundary line of the adjacent characteristic areas;
Step S3-1, defining a sweep path;
and S3-11, determining a starting point and an ending point of a sweeping path, namely selecting a proper starting point and an end point according to the structure of the three-dimensional geometric model of the aircraft, wherein for example, the sweeping path of the aircraft body can be from a nose to a tail, and the sweeping path of the wing can be from a wing root to a wing tip.
S3-12, drawing a sweep path, namely using a sweep path definition function in a grid generating tool to draw the path, ensuring that the path is smooth and continuous and is as far as possible along the main streamline direction of the aircraft, and for complex geometric shapes, defining the sweep path in a segmented way, and ensuring that the path can cover all characteristic areas;
S3-13, optimizing the sweep path, namely adjusting the sweep path by using an optimization function of a grid generating tool, ensuring high fitting degree of the path and the surface of the aircraft, and avoiding the path from being excessively bent or scattered;
Step S3-2, when the sweep path passes through the adjacent characteristic areas, acquiring the boundary lines of the adjacent characteristic areas;
S3-21, identifying adjacent characteristic areas, namely determining the boundary of each characteristic area in a grid generating tool, identifying the boundary line of the adjacent characteristic areas, and distinguishing the adjacent areas by using characteristic area labels or colors to ensure that the boundary line is clearly visible;
S3-22, extracting boundary lines, namely extracting boundary lines between adjacent characteristic areas by using a boundary extraction function of a grid generation tool, and marking the extracted boundary lines as characteristic lines so as to facilitate subsequent processing;
S3-3, dividing a transition region according to the boundary line of the adjacent characteristic regions;
step S3-31, defining a transition region range, namely determining the width of the transition region according to the boundary line of the adjacent characteristic regions, wherein the transition region is positioned at two sides of the boundary line, and the width can be determined according to the grid density and the grid cell size difference;
In an alternative example, the calculation formula for the transition region width is as follows Wherein W represents the width of the transition region, K represents an adjustment factor for balancing the influence of the grid density and the cell size difference on the width of the transition region according to actual conditions, deltaD represents the difference value of the grid density of the adjacent characteristic region, deltaS represents the difference value of the cell size of the adjacent characteristic region;
s3-31, dividing a transition region, namely dividing the transition region at two sides of the boundary line of adjacent characteristic regions according to the acquired width of the transition region, and distinguishing the characteristic regions by using a transition region label or a color;
s4, generating a hexahedral mesh along a defined sweep path, when a generation area of the hexahedral mesh is affiliated to a transition area, acquiring deviation information of generation parameters of the hexahedral mesh and quality information of the hexahedral mesh, constructing a correction judgment model according to the deviation information of the generation parameters of the hexahedral mesh 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 value, determining whether to construct a mesh generation parameter correction model, and carrying out real-time correction on the generation parameters of the hexahedral mesh in the transition area;
The deviation information of the hexahedral mesh generation parameters comprises mesh generation parameter deviation coefficients, and the quality information of the hexahedral mesh comprises non-orthogonality abnormal coefficients and scale gradient fluctuation coefficients;
marking a grid generation parameter deviation coefficient, a non-orthogonality abnormal coefficient and a scale gradient fluctuation coefficient as wgs, fzy, cdb respectively;
The grid generation parameter deviation coefficient is used for measuring the real-time deviation degree of the grid generation parameters of the hexahedral grid generated from one side of the transition region to the other side so as to adjust the grid generation parameters in real time to adapt to the requirements of the transition region. In particular, the deviation factor measures the degree of deviation in terms of parameters of the hexahedral mesh generated from one side of the transition region to the other, which helps to ensure the consistency and quality of the hexahedral mesh while smoothly transitioning between the feature regions.
The deviation coefficient of the grid generation parameter is usually a dimensionless value, and can be determined by calculating the difference of hexahedral grid parameters in a transition area, wherein the smaller the deviation coefficient of the grid generation parameter is, the higher the deviation of the grid generation parameter is, the higher the quality and stability of the grid are, the smoother the transition between the characteristic areas is, the smaller the probability of correcting the grid generation parameter is, otherwise, the larger the deviation coefficient of the grid generation parameter is, the larger the deviation of the grid generation parameter is, the lower the quality and stability of the grid are, the smoother the transition between the characteristic areas is, and the probability of correcting the grid generation parameter is the larger;
the acquisition logic of the grid generation parameter deviation coefficient is as follows:
Acquiring grid generation parameters at the time t in a transition area and preset grid generation parameters in a target area, wherein the grid generation parameters comprise grid density, grid cell size and growth rate, the grid density, the grid cell size and the growth rate at the time t are respectively marked as wg1, dy1 and zz1, and the grid density, the grid cell size and the growth rate preset in the target area are respectively marked as wg2, dy2 and zz2;
The grid generation parameter at the time t refers to a real-time grid generation parameter of a hexahedral grid to be generated in a transition region at the current time, and if the grid generation parameter is corrected, the grid generation parameter at the current time is a corrected numerical value;
Calculating the deviation coefficient of the grid generation parameter, and the expression is as follows
The non-orthogonality anomaly coefficient is a measure of the degree of anomaly in the angle between each face of the generated hexahedral mesh unit and the adjacent hexahedral mesh unit, and a nearly orthogonal hexahedral mesh generally has better quality and stability in numerical simulation.
Specifically, the non-orthogonality anomaly coefficient is a numerical index describing the degree of angular deviation orthogonality between the faces of the hexahedral mesh cells. If each face of the hexahedral mesh unit is nearly perpendicular to the faces of the adjacent units, the non-orthogonality anomaly coefficient is lower, the higher the quality and stability of the mesh, the smoother the transition between its characteristic regions, and the less probability of modifying the mesh generation parameters. Conversely, if there is a large angular deviation, the non-orthogonality anomaly coefficient is higher, the lower the quality and stability of the grid, which means that the quality of the grid may be worse, the less smooth the transition between its feature regions, the greater the probability of modifying the grid generation parameters;
the non-orthogonality anomaly coefficient acquisition logic is as follows:
obtaining vertex coordinates of each face of the newly generated hexahedral mesh unit, calculating a normal vector of each face in the hexahedral mesh unit, and expressing the normal vector as follows Where F represents the normal vector of each face, AB and AC are vectors of two non-collinear edges that make up the face, x represents the cross product operation;
In an alternative example, assume for three points B 1(x1,y1,z1)、B2(x2,y2,z2) and B 3(x3,y3,z3) on a face), then AB＝(x2-x1,y2-y1,z2-z1),AC＝(x3-x1,y3-y1,z3-z1);
Calculating the included angle value between each pair of adjacent surfaces, and the expression is as followsWherein, represents dot product operation, and theta ij represents the included angle value of each pair of adjacent surfaces i and j;
calculating non-orthogonality anomaly coefficients, expressed as follows Where e denotes the order number of the logarithm of the adjacent faces, e= {1, 2..once., N }, N being a positive integer,Representing the included angle value of the e-th pair of adjacent surfaces;
The scale gradient fluctuation coefficient is used for measuring the fluctuation degree of the size change of the adjacent hexahedral grid cells and reflecting the smoothness and consistency of the size change of the hexahedral grid cells, the smaller scale gradient fluctuation coefficient means that the size change of the adjacent hexahedral grid cells is uniform, the grid quality is good, the smoother the transition between the characteristic areas is, the smaller the probability of correcting the grid generation parameters is, the larger scale gradient fluctuation coefficient means that the size change of the hexahedral grid cells is uneven, the accuracy and stability of numerical simulation are possibly reduced, the smoother the transition between the characteristic areas is, the larger the probability of correcting the grid generation parameters is,
The acquisition logic of the scale gradient fluctuation coefficient is as follows:
Obtaining each newly generated hexahedral mesh unit k, calculating the size of each hexahedral mesh unit k ～ adjacent to the hexahedral mesh unit k, and representing the size by using a volume V;
in an alternative example, calculating the volume of the hexahedral mesh unit includes the steps of:
The hexahedral mesh unit is generally defined by eight vertices, assuming coordinates of the eight vertices are P 1、P2、P3、P4、P5、P6、P7、P8, respectively;
the hexahedron is decomposed into five tetrahedrons, and the volume of the whole hexahedron is obtained by calculating the sum of the volumes of the five tetrahedrons.
It is assumed that the vertices of hexahedrons are connected as follows:
Vertex P 1、P2、P3、P4 forms a plane, vertex P 5、P6、P7、P8 forms a plane, and the vertices are connected according to P 1--P5,P2--P6,P3--P7,P4--P8;
the following five tetrahedrons were selected and the volumes of the tetrahedrons were calculated separately:
1.{P1,P2,P3,P5},
2.{P2,P3,P6,P5},
3.{P3,P4,P8,P5},
4.{P3,P6,P7,P8},
5.{P3,P5,P6,P8},
calculating the volume of the hexahedral mesh unit, wherein the expression is as follows v=v 1+V2+V3+V4+V5;
the size gradient of each hexahedral mesh unit is calculated as follows Where N k denotes the number of adjacent hexahedral mesh cells of the hexahedral mesh cell k, sgt k denotes the size gradient of the hexahedral mesh cell k, V k denotes the volume of the hexahedral mesh cell k, and V k～ denotes the volume of the hexahedral mesh cell adjacent to the hexahedral mesh cell k;
calculating the scale gradient fluctuation coefficient, and the expression is as follows Wherein M represents the number of each hexahedral mesh unit newly generated, k= {1, 2,..once, M }, M being a positive integer;
Normalizing the grid generation parameter deviation coefficient, the non-orthogonality abnormal coefficient and the scale gradient fluctuation coefficient, constructing a correction judgment model according to the normalized grid generation parameter deviation coefficient, the normalized non-orthogonality abnormal coefficient and the scale gradient fluctuation coefficient, and generating a correction judgment index xpz, wherein the model is based on the following formula Wherein a 1、a2、a3 respectively represents a preset proportionality coefficient of a grid generation parameter deviation coefficient, a non-orthogonality abnormal coefficient and a scale gradient fluctuation coefficient, and a 1、a2、a3 is larger than 0;
As can be seen from the above calculation expression, the larger the grid generation parameter deviation coefficient, the larger the non-orthogonality abnormal coefficient and the larger the scale gradient fluctuation coefficient, the larger the correction judgment index, which indicates that the transition between the feature areas is not smooth, the larger the probability of correcting the grid generation parameters, otherwise, the smaller the grid generation parameter deviation coefficient, the smaller the non-orthogonality abnormal coefficient and the smaller the scale gradient fluctuation coefficient, the smaller the correction judgment index, which indicates that the transition between the feature areas is smooth, the smaller the probability of correcting the grid generation parameters;
comparing the correction judgment index with a preset correction judgment index threshold value, determining whether to construct a grid generation parameter correction model, and carrying out real-time correction on the generation parameters of the hexahedral grid in the transition region, wherein the method comprises the following steps of:
if the correction judgment index is larger than the correction judgment index threshold, the transition between the characteristic areas is smoother, a grid generation parameter correction model is constructed, and the output formula according to the model is as follows Wherein WG represents the corrected grid density, DY represents the corrected grid cell size, ZZ represents the corrected growth rate, xpz yz represents a preset correction judgment index threshold value, and the generation parameters of the hexahedral grid in the transition region are corrected in real time according to the output formula of the grid generation parameter correction model;
If the correction judgment index is smaller than or equal to the correction judgment index threshold, the smoother transition between the characteristic areas is indicated, and the generation parameters of the hexahedral mesh in the transition areas are not required to be corrected in real time by the mesh generation parameter correction model;
According to the invention, the characteristic regions of the aircraft model are subdivided and different grid generation parameters are applied, so that the grid density, the unit size and the growth rate of each region are optimized, the overall quality and the accuracy of grid generation are improved, a sweep path is defined, when the sweep path passes through the adjacent characteristic regions, the boundary line of the adjacent characteristic regions is obtained, the transition region is divided according to the boundary line 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, 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, a correction judgment index is generated, the correction judgment index is compared with a preset correction judgment index threshold value, whether the grid generation parameter correction model is constructed is determined, the generation parameters of the hexahedral grid in the transition region are corrected in real time, the grid quality problems caused by the grid density and the unit size change, such as distortion, distortion and non-positive are avoided, the accuracy and stability of numerical simulation are improved, unnecessary density is avoided, the calculated calculation quantity is reduced, and the simulation calculation time is shortened.
The method is suitable for geometric models of aircrafts with different types and complexity, and the grid generation parameters are adjusted in real time through the grid generation parameter correction model, so that the method can flexibly adapt to different engineering requirements and simulation scenes, and large-scale and small-scale characteristic areas such as wings, fuselages, boundary layers, vortex areas and the like existing in the appearance of the aircrafts are processed, so that the appearance of the aircrafts with complex geometric shapes can generate hexahedral grids with stronger adaptability.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The intelligent sweeping and generating method for the structured hexahedral mesh is characterized by comprising the following steps of:
Step S1, a three-dimensional geometric model of an aircraft is established, and the three-dimensional geometric model is imported into a grid generating tool;
Step S2, dividing the surface of the aircraft model into a plurality of characteristic areas according to different part characteristics of the aircraft model, and presetting different grid generation parameters for different characteristic areas, wherein the grid generation parameters comprise, but are not limited to, grid density, grid unit size and growth rate;
step S3, defining a sweep path, acquiring the boundary line of the adjacent characteristic areas when the sweep path passes through the adjacent characteristic areas, and dividing a transition area according to the boundary line of the adjacent characteristic areas;
And S4, generating a hexahedral mesh along a defined sweep path, when a generation area of the hexahedral mesh is affiliated to a transition area, acquiring deviation information of generation parameters of the hexahedral mesh and quality information of the hexahedral mesh, constructing a correction judgment model according to the deviation information of the generation parameters of the hexahedral mesh 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 value, determining whether to construct a mesh generation parameter correction model, and carrying out real-time correction on the generation parameters of the hexahedral mesh in the transition area.
2. A method of generating a structured hexahedral mesh by intelligent sweep according to claim 1 wherein the part features of the aircraft model include, but are not limited to, fuselage, wing, boundary layer, vortex region, air intake and engine.
3. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 2, wherein step S3, defining a sweep path, acquiring the boundary line of adjacent feature regions when the sweep path passes through the adjacent feature regions, and dividing the transition region according to the boundary line of the adjacent feature regions, further comprises the steps of:
Step S3-1, defining a sweep path;
S3-11, determining a starting point and an end point of a sweeping path;
s3-12, drawing a sweep path;
s3-13, optimizing a sweep path;
Step S3-2, when the sweep path passes through the adjacent characteristic areas, acquiring the boundary lines of the adjacent characteristic areas;
s3-21, identifying adjacent characteristic areas;
S3-22, extracting boundary lines;
S3-3, dividing a transition region according to the boundary line of the adjacent characteristic regions;
s3-31, defining a transition area range;
and step S3-31, dividing the transition area.
4. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 1, wherein 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;
the grid generation parameter deviation coefficient, the non-orthogonality anomaly coefficient and the scale gradient fluctuation coefficient are respectively marked as wgs, fzy, cdb.
5. The method for generating a structured hexahedral grid by intelligent sweep according to claim 4, wherein the logic for obtaining the grid generating parameter deviation coefficients is as follows:
Acquiring grid generation parameters at the time t in a transition area and preset grid generation parameters in a target area, wherein the grid generation parameters comprise grid density, grid cell size and growth rate, the grid density, the grid cell size and the growth rate at the time t are respectively marked as wg1, dy1 and zz1, and the grid density, the grid cell size and the growth rate preset in the target area are respectively marked as wg2, dy2 and zz2;
Calculating the deviation coefficient of the grid generation parameter, and the expression is as follows
6. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 5, wherein the acquiring logic of the non-orthogonality anomaly coefficients is as follows:
obtaining vertex coordinates of each face of the newly generated hexahedral mesh unit, calculating a normal vector of each face in the hexahedral mesh unit, and expressing the normal vector as follows Where F represents the normal vector of each face, AB and AC are vectors of two non-collinear edges that make up the face, x represents the cross product operation;
calculating the included angle value between each pair of adjacent surfaces, and the expression is as follows Wherein, represents dot product operation, and theta ij represents the included angle value of each pair of adjacent surfaces i and j;
calculating non-orthogonality anomaly coefficients, expressed as follows Where e denotes the order number of the logarithm of the adjacent faces, e= {1, 2..once., N }, N being a positive integer,Representing the value of the angle between the e-th pair of adjacent faces.
7. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 6, wherein the acquiring logic of the scale gradient fluctuation coefficient is as follows:
Obtaining each newly generated hexahedral mesh unit k, calculating the size of each hexahedral mesh unit k ～ adjacent to the hexahedral mesh unit k, and representing the size by using a volume V;
the size gradient of each hexahedral mesh unit is calculated as follows Where N k denotes the number of adjacent hexahedral mesh cells of the hexahedral mesh cell k, sgt k denotes the size gradient of the hexahedral mesh cell k, V k denotes the volume of the hexahedral mesh cell k, and V k～ denotes the volume of the hexahedral mesh cell adjacent to the hexahedral mesh cell k;
calculating the scale gradient fluctuation coefficient, and the expression is as follows Where M represents the number of each hexahedral mesh unit newly generated, k= {1, 2,..and M }, and M is a positive integer.
8. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 7, wherein the mesh generation parameter deviation coefficient, the non-orthogonality anomaly coefficient, and the scale gradient fluctuation coefficient are normalized, and a correction judgment model is constructed according to the normalized mesh generation parameter deviation coefficient, the normalized non-orthogonality anomaly coefficient, and the scale gradient fluctuation coefficient, to generate a correction judgment index xpz, the model of which is based on the following formulaWherein a 1、a2、a3 respectively represents a preset proportionality coefficient of a grid generation parameter deviation coefficient, a non-orthogonality abnormal coefficient and a scale gradient fluctuation coefficient, and a 1、a2、a3 is larger than 0.
9. The method for generating a structured hexahedral mesh by intelligent sweep according to claim 8, wherein the method is characterized by comparing the correction judgment index with a preset correction judgment index threshold value, 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 region, and specifically comprises the following steps:
If the correction judgment index is larger than the correction judgment index threshold, constructing a grid generation parameter correction model according to the following output formula Wherein WG represents the corrected grid density, DY represents the corrected grid cell size, ZZ represents the corrected growth rate, xpz yz represents a preset correction judgment index threshold value, and the generation parameters of the hexahedral grid in the transition region are corrected in real time according to the output formula of the grid generation parameter correction model;
If the correction judgment index is smaller than or equal to the correction judgment index threshold, the generation parameters of the hexahedral mesh in the transition region are corrected in real time without generating a mesh generation parameter correction model.
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