A method and device for automatically generating large-scale unstructured grids for commercial aircraft

Abstract

The present application discloses a method and device for automatically generating a large-scale unstructured grid for a commercial aircraft. The computational domain enclosed by the near-field bounding box and the far-field bounding box is geometrically segmented by the constructed inscribed geometric surface to obtain each far-field region; a first surface grid is generated on the surface of the aircraft body and each component of the aircraft in the near-field region, a second surface grid is generated on the inscribed geometric surface between the near-field region and the far-field region, and a third surface grid is generated on the surface of the far-field bounding box; an unstructured grid is generated based on the second surface grid and the third surface grid; a basic unstructured hybrid grid is automatically generated in the near-field region based on the first surface grid; the unstructured grids in each computational sub-domain are automatically merged to obtain a large-scale unstructured hybrid grid for the commercial aircraft. The present application realizes the automatic generation of grids from the division of the computational domain to each computational sub-domain, and then to the automatic merging of grids, effectively solving technical bottlenecks such as difficulties in generating ultra-large-scale unstructured grids and low automation, and improving the simulation efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of computational fluid dynamics simulation, and more specifically to a method and device for automatically generating large-scale unstructured grids for commercial aircraft. Background Art

[0002] The fluid dynamics simulation technology based on computational fluid dynamics (CFD) is a technology that comprehensively utilizes high-performance computers, mathematics, computer graphics, etc. to obtain aerodynamic performance by solving the Navier-Stokes equations. With the rise of artificial intelligence and digital twin technologies, achieving high-fidelity fluid dynamics simulation of the entire aircraft is becoming the main driving force for the development of CFD technology. The National Aeronautics and Space Administration (NASA) and Boeing Company pointed out in their released CFD development vision for 2030 that by 2030, the future development goal of CFD technology is to make full use of the performance of E-class high-performance computers to achieve high-fidelity LES simulation of the entire aircraft with engines; for this purpose, continuous efforts are needed in the fields of high-performance computing, mesh generation and adaptation, numerical algorithms, etc.

[0003] It is generally believed that mesh generation accounts for 70% or even more of the entire work to achieve complete fluid dynamics simulation. How to generate large-scale computational grids required to support high-fidelity fluid dynamics simulation of complex aircraft configurations is one of the challenging problems that the current and future CFD technology development must face. For the generation of large-scale unstructured grids required for high-fidelity fluid dynamics simulation at the entire aircraft level of commercial aircraft, the current technical solutions based on open-source or commercial software generally have problems of low automation and efficiency in the generation process, especially for the generation of grids with a scale of hundreds of millions of grids, which is very time-consuming and laborious. In addition, the cross-media storage and transmission of data also bring the problem of disconnection between mesh generation and fluid dynamics simulation calculation. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the prior art. To this end, a first aspect of this application proposes a method for automatically generating large-scale unstructured grids for commercial aircraft, including: determining a computational domain based on the geometric models of the aircraft fuselage and components, constructing an inscribed geometric surface, a near-field bounding box, and a far-field bounding box, and using the inscribed geometric surface to geometrically divide the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain each far-field region, where the computational domain within the near-field bounding box is the near-field region; generating first surface grids on the surfaces of the aircraft fuselage and each component in the approach region using similarity rules, generating second surface grids on the inscribed geometric surfaces of the near-field region and the far-field region, and generating third surface grids on the surface of the far-field bounding box; using an improved Delaunay algorithm to generate unstructured grids for each far-field region based on the second surface grids and the third surface grids; using a boundary layer grid automatic generation algorithm to automatically generate a basic unstructured hybrid grid with boundary layer grids in the near-field region based on the first surface grids; processing the unstructured grids in the far-field region and the basic unstructured hybrid grid with boundary layer grids based on a local grid omnidirectional dissection algorithm to obtain each computational subdomain with uniform unstructured grid density; and automatically merging the unstructured grids in each computational subdomain based on an arbitrary subregion unstructured hybrid grid automatic merging algorithm to obtain large-scale unstructured grids for commercial aircraft.

[0005] Optionally, the step of determining a computational domain based on the geometric models of the aircraft fuselage and components, constructing an inscribed geometric surface and a far-field bounding box, and using the inscribed geometric surface to geometrically divide the computational domain enclosed by the far-field bounding box to obtain each computational subdomain includes: importing the geometric digital model of the aircraft fuselage using a geometric modeling tool, and creating a far-field rectangular bounding box and a near-field rectangular bounding box according to a preset computational domain size; using the geometric modeling tool to create an inscribed geometric surface within the computational domain without the aircraft fuselage according to preset positions and sizes, so as to divide the computational domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple computational subdomains.

[0006] Optionally, after using the geometric modeling tool to create an inscribed geometric surface within the computational domain without the aircraft fuselage according to preset positions and sizes to divide the computational domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple computational subdomains, the method further includes: uniformly naming the near-field rectangular bounding box and the inscribed geometric surface, and saving the geometric data of each computational subdomain.

[0007] Optionally, before generating the first surface grids on the surfaces of the aircraft fuselage and each component in the approach region using similarity rules, the method further includes: performing watertightness treatment on the non-watertight regions in the geometric digital model of the aircraft fuselage by means of geometric simplification and adding geometric surfaces to obtain an aircraft fuselage with good watertightness.

[0008] Optionally, generating the first surface mesh on the aircraft body and the surfaces of various aircraft components in the approach area, generating the second surface mesh on the inscribed geometric surface in the near-field area and the far-field area, and generating the third surface mesh on the surface of the far-field bounding box according to the similarity rule includes: adopting the same grid point distribution for the line elements and surface elements on the inscribed geometric surface and on the surface of the near-field bounding box to obtain the second surface mesh and the third surface mesh respectively; performing triangulation on the parameter plane to obtain the triangulation of the plane, and transforming the triangulation of the plane to the curved surfaces in the space of the aircraft body and various aircraft components to obtain the first surface mesh.

[0009] Optionally, performing triangulation on the parameter plane to obtain the triangulation of the plane, and transforming the triangulation of the plane to the curved surfaces in the space of the aircraft body and various aircraft components to obtain the parameterized plane includes: constructing an original grid plane covering the entire computational domain; obtaining the boundary points of the aircraft body and various aircraft components, and inserting the boundary points into the original grid by using the Bowyer-Watson algorithm; recovering the boundaries of the aircraft body and various aircraft components based on the boundary points and deleting the elements outside the boundaries to obtain the initial mesh surface; calculating the grid distribution function values of each boundary point, and determining the grid metric matrix based on the grid distribution function values; generating the points on the curved surfaces in the space of the aircraft body and various aircraft components based on the grid metric matrix, and generating the first surface mesh based on the initial mesh surface by using the Bowyer-Watson algorithm.

[0010] Optionally, adopting the improved Delaunay algorithm to generate the unstructured meshes of each far-field area based on the second surface mesh and the third surface mesh includes: reading the second surface mesh and the third surface mesh, and using the second surface mesh and the third surface mesh as the boundary meshes; adopting the volume mesh generation algorithm based on the improved Delaunay to generate the unstructured meshes of each far-field area that automatically contain the boundary condition information based on the second surface mesh and the third surface mesh.

[0011] Optionally, the boundary layer grid automatic generation algorithm is used to automatically generate a basic unstructured hybrid grid with boundary layer grids in the near-field region based on the first surface grid, including: obtaining each grid point of the first surface grid, using each grid point as an array point, and calculating the advancement vectors of each array point on different surface grids; performing weighted averaging on the advancement vectors of each array point on different grids; advancing the first layer in the direction away from the aircraft body or the surface of each aircraft component along the advancement vector based on each array point; connecting each grid point on the first layer to obtain new array points, and continuing to advance a new layer in the direction away from the aircraft body or the surface of each aircraft component along the advancement vector based on the new array points until a preset number of layers is reached to obtain the boundary layer grid; wherein, the layer height of each layer and the total number of advancement layers are determined based on preset values; using an improved Delaunay algorithm to generate a basic unstructured hybrid grid with boundary layer grids between the boundary layer grid and the near-field bounding box.

[0012] Optionally, the far-field region unstructured grid and the basic unstructured hybrid grid with boundary layer grids are processed based on the local grid omnidirectional dissection algorithm to obtain each computational subdomain with a uniform unstructured grid density, including: obtaining all the edges of each grid in the far-field region unstructured grid and the basic unstructured hybrid grid with boundary layer grids; using the octree dissection method to perform encryption marking on each edge, and dissecting each edge into two parts based on the encryption marking to obtain each volume grid after octree dissection; based on each volume grid after octree dissection, obtaining each computational subdomain with a desired unstructured grid density.

[0013] Optionally, based on the arbitrary sub-region unstructured hybrid grid automatic merging algorithm, the unstructured grids in each computational subdomain are automatically merged to obtain a large-scale unstructured grid for commercial aircraft, including: obtaining each inscribed surface of each computational subdomain based on a preset search algorithm; determining the matching relationship between the cells and the matching relationship between the points of the inscribed surfaces of adjacent computational subdomains based on the backward scanning algorithm; determining the true matching relationship between the points on the inscribed surface according to the matching relationship between the cells and the matching relationship between the points of the inscribed surfaces of adjacent computational subdomains; automatically clearing the redundant points in each computational subdomain and automatically merging the redundant unstructured grids in each computational subdomain based on a predefined global array to obtain a large-scale unstructured grid for commercial aircraft.

[0014] To achieve the above object, a second aspect of the present application further provides a method and device for automatically generating large-scale unstructured grids for commercial aircraft, including a computational domain segmentation module for determining a computational domain based on the geometric models of the aircraft body and components, constructing an inscribed geometric surface, a near-field bounding box, and a far-field bounding box, and geometrically segmenting the computational domain enclosed by the near-field bounding box and the far-field bounding box using the inscribed geometric surface to obtain each far-field region, wherein the computational domain within the near-field bounding box is the near-field region; a surface grid generation module for generating a first surface grid on the surfaces of the aircraft body and each component of the aircraft in the approach region using a similarity rule, generating a second surface grid on the inscribed geometric surface between the near-field region and the far-field region, and generating a third surface grid on the surface of the far-field bounding box; a volume grid generation module for generating unstructured grids for each far-field region based on the second surface grid and the third surface grid using an improved Delaunay algorithm; a hybrid volume grid generation module for automatically generating a basic unstructured hybrid volume grid with a boundary layer grid in the near-field region based on the first surface grid using a boundary layer grid automatic generation algorithm; a density determination module for processing the unstructured grids of the far-field regions and the basic unstructured hybrid volume grid with a boundary layer grid based on a local grid omnidirectional dissection algorithm to obtain each computational subdomain with unstructured grids of a desired density; and a grid merging module for automatically merging the unstructured grids in each computational subdomain based on an arbitrary subregion unstructured hybrid grid automatic merging algorithm to obtain large-scale unstructured grids for commercial aircraft.

[0015] The embodiments of the present application provide a method and device for automatically generating large-scale unstructured grids for commercial aircraft. Compared with the prior art, the beneficial effects of this method are as follows: determining the computational domain based on the geometric models of the aircraft body and components, constructing an inscribed geometric surface, a near-field bounding box, and a far-field bounding box, and using the inscribed geometric surface to geometrically divide the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain each far-field region. Among them, the computational domain within the near-field bounding box is the near-field region, realizing the geometric modeling of the computational domain; generating first surface grids on the surfaces of the aircraft body and each component of the aircraft in the approach region using similarity rules, generating second surface grids on the inscribed geometric surfaces of the near-field region and the far-field region, and generating third surface grids on the surface of the far-field bounding box; using an improved Delaunay algorithm to generate unstructured grids for each far-field region based on the second surface grids and the third surface grids; using a boundary layer grid automatic generation algorithm to automatically generate a basic unstructured hybrid grid with boundary layer grids in the near-field region based on the first surface grids; processing the unstructured grids in the far-field region and the basic unstructured hybrid grid with boundary layer grids based on the local grid omnidirectional dissection algorithm to obtain each computational sub-domain with uniform unstructured grid density; based on the arbitrary sub-region unstructured hybrid grid automatic merging algorithm, automatically merging the unstructured grids in each computational sub-domain to obtain large-scale unstructured grids for commercial aircraft. Through the above technical solutions, the present application realizes the automatic generation of grids, the adaptive adjustment of grid density, and the automatic merging of grids in each computational sub-domain. The present application can not only meet the requirements for generating large-scale unstructured hybrid grids for high-fidelity fluid dynamics simulation of the entire commercial aircraft, but also, as a common technology, meet the requirements for generating large-scale computational grids for high-fidelity fluid dynamics simulation of other types of aircraft. Through the above technical solutions of the present application, unstructured hybrid grids in the order of tens of millions to hundreds of millions of grids can be automatically generated as required. The present application can realize the automatic generation of grids from computational domain division to each computational sub-domain, and then to grid automatic merging, effectively solving technical bottlenecks such as difficulties in generating ultra-large-scale unstructured grids and low automation, and improving the simulation efficiency. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the present application, the accompanying drawings required for describing the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is the flowchart of the implementation of the method for automatically generating large-scale unstructured hybrid grids for high-fidelity fluid dynamics simulation of commercial aircraft in the embodiments of the present application;

[0018] Figure 2It is a schematic diagram of the automatic division of the high-fidelity hydrodynamic simulation calculation domain of a commercial aircraft in the embodiment of the present application;

[0019] Figure 3 It is a typical triangular meshing of a spatial curved surface generated by the surface mesh generation algorithm in the embodiment of the present application;

[0020] Figure 4 It is a flow chart of the volume mesh generation algorithm in the embodiment of the present application;

[0021] Figure 5 It is a flow chart of the boundary layer mesh generation algorithm in the embodiment of the present application;

[0022] Figure 6 It is a schematic diagram of the automatic remeshing of tetrahedral meshes for secondary refinement in the embodiment of the present application;

[0023] Figure 7 It is a simple example of mesh merging in the embodiment of the present application;

[0024] Figure 8 It is the mesh merging result of a simple test case in the embodiment of the present application;

[0025] Figure 9 It is a schematic diagram of the symmetric plane mesh of the NASA-CRM wing-body model before merging in the embodiment of the present application;

[0026] Figure 10 It is a schematic diagram of the mesh of the NASA-CRM wing-body model generated after merging in the embodiment of the present application. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0028] This specification provides method operation steps such as in the embodiments or flowcharts, but may include more or fewer operation steps based on routine or non-creative labor. When actually executed in a system or server product, it may be executed sequentially or in parallel according to the methods shown in the embodiments or the drawings (for example, in an environment of parallel processors or multi-threaded processing).

[0029] With the advent of the era of digital twin and artificial intelligence technologies, in the field of aerospace technology, the demand for high-fidelity fluid dynamics simulations based on LES and RANS / LES hybrids has become more explicit. Thus, large-scale mesh generation has increasingly become one of the bottleneck problems for high-fidelity fluid dynamics simulations at the whole-aircraft level of commercial aircraft. For the large-scale unstructured mesh generation required for high-fidelity fluid dynamics simulations at the whole-aircraft level of commercial aircraft, the current technical solutions based on open-source or commercial software generally have problems such as low automation and efficiency in the generation process, and it is very time-consuming and laborious to generate meshes on the order of hundreds of millions of meshes. In addition, the cross-media storage and transmission of data also bring problems of disconnection between mesh generation and fluid dynamics simulation calculations.

[0030] To solve the above problems, this application comprehensively utilizes technologies such as geometry management, automatic mesh generation, adaptive adjustment of mesh density, and efficient automatic merging of multi-zone meshes, and invents a large-scale unstructured hybrid mesh generation method, laying a technical foundation for the "in-situ" mesh generation for high-fidelity fluid dynamics simulations at the whole-aircraft level of commercial aircraft.

[0031] Reference Figure 1 , Figure 1 FIG. is a flowchart of a method for automatically generating large-scale unstructured meshes for commercial aircraft provided by this application. This method can be executed by a processor of a terminal or a server. This method may include:

[0032] S10. Determine the computational domain based on the geometric model of the aircraft body and components, construct an inscribed geometric surface, a near-field bounding box, and a far-field bounding box, and use the inscribed geometric surface to geometrically divide the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain each far-field region, where the computational domain within the near-field bounding box is the near-field region.

[0033] Specifically, step S10 may include the following execution process:

[0034] S101. Import the geometric digital model of the aircraft body using a geometric modeling tool, and create a far-field rectangular bounding box and a near-field rectangular bounding box according to the preset computational domain size.

[0035] S102. Use the geometric modeling tool to create an inscribed geometric surface within the computational domain without the aircraft body according to the preset position and size, so as to divide the computational domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple computational sub-domains.

[0036] In an embodiment of this application, after step S102, this method may further include the following execution process:

[0037] Uniformly name the near-field rectangular bounding box and the inscribed geometric surface, and save the geometric data of each computational sub-domain.

[0038] In the specific implementation process, the present application automatically plans the computational domain and computational sub-domains through the processor of a computer terminal or a server terminal, and automatically creates a far-field region, a near-field region, and an inscribed geometric surface through the computer terminal or the server terminal. The present application proposes an automatic positioning method for automatically dividing the computational domain, and the method may include the following steps:

[0039] First, the processor can obtain the geometric models of the aircraft body and components. In this embodiment, an STL file is used as the geometric description model, and its typical definition form is as follows:

[0040]

[0041] Then, based on the read geometric data, the processor calculates the center point of the geometric model, as well as the maximum and minimum values in the X, Y, and Z directions by circularly scanning the geometric patches and nodes:

[0042]

[0043]

[0044] Among them, is the total number of geometric nodes. Thus, the corner coordinates of the near-field bounding box of the aircraft body and components can be determined by introducing the near-field computational domain scaling parameter :

[0045] (3)

[0046] (4)

[0047] Similarly, the corner coordinates of the far-field bounding box of the aircraft body and components can be determined by introducing the far-field computational domain scaling parameter :

[0048]

[0049]

[0050] Then, using the above corner coordinates, the processor can automatically divide the entire computational domain into 1 near-field computational sub-domain and 8 far-field computational sub-domains, as Figure 2 shown.

[0051] Finally, the processor can perform a secondary division of the computational sub-domains by artificially adding inscribed geometric surfaces to the above computational sub-domains, so as to form a new combination of different computational sub-domains for the entire computational domain. This combination can be expressed in an abstract form as:

[0052]

[0053] Here, represents the computational domain, represents the computational sub - domain, and

[0054] S20. Generate the first surface mesh on the surface of the aircraft body and each component of the aircraft in the approach area using the similarity rule, generate the second surface mesh on the inscribed geometric surface in the near - field area and the far - field area, and generate the third surface mesh on the surface of the far - field bounding box.

[0055] It should be noted that: the generation of the surface mesh may include geometric repair and watertight treatment of the geometric digital model of the aircraft body, the generation of the surface mesh of the far - field and the inscribed geometric surface, etc.

[0056] In an embodiment of the present application, step S20 may include the following execution process:

[0057] S201. Adopt the same grid point distribution for the line elements and surface elements on the inscribed geometric surface and on the surface of the near - field bounding box, and obtain the second surface mesh and the third surface mesh respectively.

[0058] Among them, the similarity rule refers to adopting the same grid point distribution for the line elements and surface elements on the inscribed geometric surface to ensure rules such as strict docking of the inscribed geometric surface. Among them, the inscribed geometric surface mainly includes the near - field bounding box of the near - field computational sub - domain ( Figure 2 in the area labeled "9"), and the common inscribed geometric surface between the far - field computational sub - domains ( Figure 2 in the areas labeled from "1" to "8"). Adopting the same grid point distribution for the line elements and surface elements on the inscribed geometric surface and on the surface of the near - field bounding box, and obtaining the second surface mesh and the third surface mesh respectively, which can ensure that the finally generated computational grid is a strictly point - docking grid, so as to ensure that the above - mentioned inscribed geometric surface should be the same surface mesh in different computational sub - domains.

[0059] It should be noted that, before step S20, the method may further include the following execution process:

[0060] For the non - watertight areas in the geometric digital model of the aircraft body, perform watertight treatment by means of geometric simplification and adding geometric surfaces to obtain an aircraft body with good watertightness.

[0061] The watertight treatment of the aircraft fuselage and aircraft components is a prerequisite for automatically generating surface meshes. It should be noted that in this application, both the far-field region and the inscribed geometric surfaces of different computational sub-domains are defined as regular rectangular regions with explicit analytical expressions. Therefore, there are no geometric repair and watertight treatment issues. However, for the regions of the aircraft fuselage and aircraft components, geometric repair and watertight treatment are required in necessary cases.

[0062] The relevant methods for watertight treatment can include: performing necessary geometric simplification, adding geometric surfaces, etc. Specifically, the processor can perform watertight treatment by calling professional geometric modeling tool software such as CATIA and UG, or mesh generation software with geometric processing functions such as ICEM-CFD and PointWise. After the watertight treatment is completed, an independent STL format geometric file can be output and transferred to the next step for automated surface mesh generation.

[0063] S202. Perform triangulation on the parameter plane to obtain the triangulation of the plane, and transform the triangulation of the plane to the curved surfaces in the space of the aircraft fuselage and each aircraft component to obtain the first surface mesh.

[0064] Specifically, step S202 can include the following execution process:

[0065] S2021. Construct an original mesh plane covering the entire computational domain.

[0066] S2022. Obtain the boundary points of the aircraft fuselage and each aircraft component, and insert the boundary points into the original mesh using the Bowyer-Watson algorithm.

[0067] S2023. Restore the boundaries of the aircraft fuselage and each aircraft component based on the boundary points, and delete the elements outside the boundaries to obtain the initial mesh surface.

[0068] S2024. Calculate the mesh distribution function values of each boundary point, and determine the mesh metric matrix based on the mesh distribution function values.

[0069] S2025. Generate points on the curved surfaces in the space of the aircraft fuselage and each aircraft component based on the mesh metric matrix, and generate the first surface mesh based on the initial mesh surface using the Bowyer-Watson algorithm.

[0070] Currently, the Delaunay triangulation method for planar regions is very mature, but the triangulation method for space curved surfaces has been in development. The triangulation of space curved surfaces can be performed either on the space curved surface or through the parametric expression of the surface. Triangulation is performed on the parameter plane and then transformed to the space curved surface to form the triangulation of the space curved surface. This application uses the latter method.

[0071] Take triangulation in the computational subdomain as an example, the basic steps are as follows: (1) Establish a coarse initial mesh that covers the entire computational domain Take triangulation in the computational subdomain as an example, the basic steps are as follows: (1) Establish a coarse initial mesh that covers the entire computational domain of the aircraft body and aircraft components, and use the Bowyer-Watson algorithm to insert the boundary points into the coarse initial mesh. (3) Check and restore the boundary edges determined by the aircraft body and aircraft components, delete the out-of-domain elements in the coarse initial mesh, and form an initial mesh composed only of boundary points. (4) Calculate the mesh distribution function values of the boundary points and define the metric matrix, and its expression is:

[0072]

[0073] In the formula, , is the mesh distribution function, , represents the tangent plane of any point on the aircraft body or aircraft component. (5) Generate the in-domain points of the aircraft body or aircraft component and insert them point by point using the Bowyer-Watson algorithm. (6) Post-processing of mesh generation, including topological compatibility check, mesh optimization, mesh quality analysis, etc. Figure 3 The schematic diagram of the triangular mesh generation of a typical spatial surface using this method is given.

[0074] In the specific implementation process, the above algorithm needs to be recycled, and the triangular meshing of the aircraft body and aircraft component boundary surfaces is carried out by classification, including: 1) First, generate the surface meshes of the aircraft body and each component. The relevant work includes setting the grid point distribution and size of the line elements and surface elements of the aircraft body and aircraft components, and using the above surface mesh triangular meshing algorithm to realize the automatic generation of the surface meshes of the aircraft body and each aircraft component. 2) Subsequently, generate the surface meshes of the interfaces of each computational subdomain. The relevant work includes setting the grid point distribution and grid size of the line elements and surface elements on the interfaces of each computational subdomain, and using the above surface mesh triangular meshing algorithm to automatically generate the surface meshes of each regular inscribed geometric surface. 3) Again, generate the surface meshes of the far field, including setting the grid point distribution and size of the line elements and surface elements in the far field regions of each computational subdomain, and using the above surface mesh triangular meshing algorithm to automatically generate the surface meshes of the far field regions. 4) Finally, save the surface mesh data. At this time, it is necessary to independently name the surface meshes of the aircraft body, inscribed geometric surfaces, and far field, and store them in different CGNS format data files in the form of independent files. For each type of surface mesh data, here, the following naming rules are adopted to name each type of surface mesh data file. The mesh of the aircraft body and aircraft components is in the form of "body + number.cgns", the inscribed geometric surface is in the form of "innerface + number.cgns", and the far field region is in the form of "far + number.cgns". These data will be used as the input required for the next volume mesh generation.

[0075] S30. Adopt an improved Delaunay algorithm to generate unstructured meshes for each far field region based on the second surface mesh and the third surface mesh.

[0076] In an embodiment of the present application, step S30 may include the following execution process:

[0077] S301. Read the second surface mesh and the third surface mesh, and use the second surface mesh and the third surface mesh as boundary meshes.

[0078] S302. Adopt a volume mesh generation algorithm based on the improved Delaunay to generate unstructured meshes for each far field region that automatically includes boundary condition information based on the boundary meshes.

[0079] Figure 4A schematic diagram of improving the Delaunay algorithm to generate unstructured grids is given. The basic steps include: (1) Reading in the triangular surface grid defining the boundary; (2) comprehensively applying the Delaunay algorithm and the Bowyer-Watson point insertion algorithm to initialize the Delaunay tetrahedral grid; (3) restoring the boundary of the volume grid according to the boundaries defined by the aircraft body and aircraft components; (4) performing post-processing of the volume grid generation, including automatically analyzing the grid quality and optimizing the tetrahedral grid quality, etc.

[0080] After the volume grid is classified and generated by the improved Delaunay algorithm described above in this application, the volume grid data is saved. The classification and generation of the volume grid include the automatic generation of the volume grid in the near-field calculation subdomain and the automatic generation of the volume grid in the far-field calculation subdomain.

[0081] Among them, the automatic generation of the volume grid in the far-field calculation subdomain is achieved by circularly assembling the inscribed geometric surface and the surface grid in the far field to form different far-field regions, and using the Delaunay-based volume grid generation algorithm in this application to automatically generate the basic unstructured grid of the far-field region including boundary condition information. Its grid scale is mainly determined by the inscribed geometric surface and the surface grid scale in the far field. In the above process, the automatically generated boundary condition information is that the aircraft body and components are BCWall (physical surface boundary), the far-field bounding box is BCFarfield (far-field boundary), and the near-field bounding box and other inscribed geometric surfaces are BCGeneral (general boundary).

[0082] The automatic generation of the volume grid in the near-field calculation subdomain is achieved by assembling the surface grids of the aircraft body and components and the near-field bounding box to establish the calculation subdomain, and using the improved Delaunay-based volume grid generation algorithm in this application to automatically generate the basic unstructured grid of the near-field region including boundary condition information. Its grid scale is mainly determined by the surface grid scale of the aircraft body.

[0083] The volume grid data saving names the grids of each calculation subdomain and stores them in different data files in the standard CGNS data format in the form of independent files. For various types of volume grid data files, the following rules are used for data file naming. The near-field grid is in the form of "nearbodyZone.cgns", and the far-field grid is in the form of "farZone + number.cgns". In this way, according to the method of automatically dividing the calculation subdomain described above, a total of 1 near-field subdomain volume grid and 8 far-field subdomain volume grids will be generated.

[0084] In the above CGNS data file, the information that must be saved includes: grid coordinates (GridCoordinates), volume mesh elements and their connection relationships (ElementConnectivity), boundary surface mesh elements and their connection relationships, boundary surface patches (ZoneBC), etc.

[0085] For the computational sub-region containing the body surface of the aircraft, the present application provides an automatic algorithm for generating boundary layer meshes below, which automatically generates a basic unstructured hybrid volume mesh with boundary layer meshes.

[0086] S40. Adopt the automatic algorithm for generating boundary layer meshes to automatically generate a basic unstructured hybrid volume mesh with boundary layer meshes in the near-field region based on the first surface mesh.

[0087] Specifically, step S40 may include the following execution process:

[0088] S401. Obtain each grid point of the first surface mesh, use each grid point as an array point, and calculate the advancement vectors of each array point on different surface meshes.

[0089] S402. Weighted-average the advancement vectors of each array point on different meshes.

[0090] S403. Based on each array point, advance the first layer in the direction away from the surface of the aircraft body or each component of the aircraft along the advancement vector.

[0091] S404. Connect each grid point on the first layer to obtain new array points, and based on the new array points, continue to advance a new layer in the direction away from the surface of the aircraft body or each component of the aircraft along the advancement vector until the preset number of layers is reached to obtain the boundary layer mesh. Among them, the height of each layer and the total number of advancement layers are determined based on preset values.

[0092] S405. Adopt an improved Delaunay algorithm to generate a basic unstructured hybrid volume mesh with boundary layer meshes between the boundary layer mesh and the near-field bounding box.

[0093] Specifically, the automatic algorithm for generating boundary layer meshes includes setting the wall distance of the expected first layer of meshes, setting the number of layers for advancing the boundary layer mesh, and automatically generating the boundary layer mesh according to the set parameters. The flow chart of this algorithm is as Figure 5 shown, and specifically includes the following steps: 1) The processor reads in the surface triangular mesh of the aforementioned aircraft body and aircraft components; 2) The processor uses the surface points or new grid points of the surface triangular mesh as the advancing array points; 3) The processor loops by point to calculate the advancement vectors of the array points. Here, for the same grid point, it may be involved in different surface mesh elements. Therefore, the processor needs to calculate its vectors on different surfaces and perform weighted averaging. The expression for weighted averaging is:

[0094]

[0095] 4) The processor smoothes the propulsion vector; 5) The processor advances the array points along the propulsion vector; 6) The processor connects the new grid points to form the grid of the current layer; 7) The processor determines whether the desired layer height or the number of layers is reached. If so, the layer advancement ends, and the prismatic grid obtained by the advancement is output; otherwise, it returns to step 2) to enter the next round of the advancement process. It is not difficult to see that the generation of the boundary layer grid requires setting the grid spacing of the first layer and the number of layers for advancement, which are the key parameters of the above method.

[0096] Furthermore, the basic unstructured hybrid grid with a boundary layer grid refers to the boundary layer grid mainly composed of prisms and pyramids generated by the above method, and the space is filled mainly with tetrahedral grids to form a grid. The grid scale can be medium-sized for convenient automatic generation.

[0097] S50. Process the unstructured body grid in the far-field region and the basic unstructured hybrid body grid with a boundary layer grid based on the local grid omnidirectional dissection algorithm to obtain each computational subdomain with a uniform density of the unstructured body grid.

[0098] In an embodiment of the present application, step S50 may include the following execution process:

[0099] S501. Obtain all the edges of each grid in the unstructured body grid in the far-field region and the basic unstructured hybrid body grid with a boundary layer grid.

[0100] S502. Use the octant dissection method to encrypt and mark each edge, and based on the encryption mark, dissect each edge into two parts to obtain each volume grid after octant dissection.

[0101] S503. Based on each volume grid after octant dissection, obtain each computational subdomain with a uniform density of the unstructured body grid.

[0102] In the specific execution process, the processor uses the local grid omnidirectional dissection algorithm, specifically the octant mode, to generate an unstructured grid with the desired grid density. The local grid omnidirectional dissection algorithm includes the unstructured body grid in the far-field region and the basic unstructured hybrid body grid with a boundary layer grid on the computational subdomain. By setting the density box range, and then using the omnidirectional dissection algorithm of the volume grid unit, the local grid encryption is automatically realized. By using this method, the processor can achieve the desired grid density in key flow field regions such as the wake region and the shock wave region. Figure 6The possible omnidirectional subdivision methods of tetrahedral meshes are given. The possible subdivision methods may include binary mode, quarter mode, octal mode, etc. For the omnidirectional encryption of tetrahedrons in this application, the octal mode is adopted, that is, all edges of the volume mesh elements are encrypted and marked, and then, based on this, the secondary subdivision of the volume mesh elements is carried out. Using this method, the further encryption of the basic mesh can be realized, so as to obtain the computational subdomain mesh of the expected scale.

[0103] This application uses a standard CGNS data file in a special marking form to store the subdivided mesh. The standard CGNS data file in a special marking form includes special output markings for the near-field envelope surface and the inscribed geometric surfaces within each computational subdomain, such as using a unified name or a unified generalized boundary condition BCGeneral, etc., with the aim of facilitating the subsequent search and positioning of the common interface.

[0104] In this example, the inscribed geometric surfaces are uniformly named in the form of "innerface + label number". For example, if there are 2 innerface geometric surfaces in a computational subdomain, they can be combined into one boundary surface in the CGNS file and named "innerface"; or they can be stored independently as two boundary surfaces and named "innerface1" and "innerface2" in sequence. The subsequent operations can locate the inscribed boundary surfaces by searching for the boundary surface names. In actual operations, the boundary condition types of the inscribed geometric surfaces can also be uniformly defined according to the BCGeneral condition in the CGNS standard. Next, it is necessary to locate the inscribed boundary surfaces by judging the boundary condition types.

[0105] S60. Based on the automatic merging algorithm for unstructured hybrid meshes in any sub-region, automatically merge the unstructured meshes in each computational subdomain to obtain a large-scale unstructured mesh of a commercial aircraft.

[0106] In an embodiment of this application, step S60 may include the following execution process:

[0107] S601. Based on a preset search algorithm, obtain each inscribed surface of each computational subdomain.

[0108] S602. Based on the backward scanning algorithm, determine the matching relationship between the units of the inscribed surfaces of adjacent computational subdomains and the matching relationship between points and points.

[0109] S603. Determine the true matching relationship between points and points on the inscribed surface according to the matching relationship between the units of the inscribed surfaces of adjacent computational subdomains and the matching relationship between points and points.

[0110] S604. Clear redundant points in each computational sub-domain based on a predefined global array and automatically merge redundant unstructured grids in each computational sub-domain to obtain a large-scale unstructured grid for commercial aircraft.

[0111] Specifically, the automatic merging of any multi-zone unstructured hybrid grid includes locating and searching for the internal interface according to a unified name, or special marks such as a unified boundary type, automatically calculating the overlapping relationship between surfaces and the overlapping relationship between points in the internal interface data, automatically backtracking the overlapping relationship between points, and automatically clearing redundant points and redundant cells after grid merging.

[0112] Furthermore, locating and searching for the internal interface according to a unified name, or special marks such as a unified boundary type, the pseudo-code of its algorithm can be expressed as:

[0113]

[0114] Here, the relevant preprocessing operations refer to the relevant preprocessing operations for the internal interface carried out in advance to facilitate the implementation of internal interface matching, including: (1) calculating the centroid of each internal interface surface element, and the calculation method is:

[0115]

[0116] Here, is the defined number of nodes of the surface element. For triangular surface elements, its value is 3, and for quadrilateral surface elements, its value is 4. is the node coordinate; (2) calculating the area of each surface element. For triangular surface elements, according to Heron's formula for calculating the area of any triangle, the calculation method is:

[0117]

[0118] Here, are the side lengths of the 3 sides; for quadrilateral surface elements, the quadrilateral element can be divided into diagonal sides and converted into the sum of the areas of 2 triangular surface elements for calculation. The calculation method is:

[0119]

[0120] Unify and output the internal interface node definition information, node coordinates, and their attribute information such as the center point and area to an intermediate file common_surf.x. Here, the purpose of introducing the intermediate file common_surf.x is to define a database about the internal interface information through this file, which is convenient for subsequent matching of elements and nodes based on this database. The pseudo-code of the database building process is as follows:

[0121]

[0122] Furthermore, for the automatic calculation of the lap relationship between surfaces and the lap relationship between points in the inscribed surface data, that is, by retrieving the inscribed surface information library common_surf.x, obtaining the comprehensive inscribed surfaces, and then establishing the surface matching relationship and the point matching relationship. The specific matching can adopt the following backward scanning algorithm:

[0123]

[0124] In the above pseudocode, represents the total number of inscribed surfaces to be retrieved, represents the expected deviation threshold. It is worth noting that the last step of the above operation is to update the information in the inscribed surface information library and append the matching information of surfaces and nodes, with the aim of facilitating subsequent operations such as backtracking of point matching relationships and clearing of redundant points.

[0125] Furthermore, for the automatic backtracking of the lap relationship between points, the method is to perform a forward scan of the element node matching relationship established in the previous step to establish the matching relationship starting from the root node, so as to ensure that the point matching relationship still truly exists after the subdomain meshes are merged. Taking the following matching relationship found in an actual test case as an example, through retrieval, it is found that there is a point matching relationship between computational subdomain 2 and computational subdomain 1 as , and at the same time, it is found that there is a point matching relationship between computational subdomain 3 and computational subdomain 2 as . In this way, during the actual merger, the point of computational subdomain 2 is automatically cleared after the mesh merger, and the matching point of computational subdomain 3 cannot be matched to a real point. Based on the aforementioned matching relationship, a real matching relationship is established through backtracking: .

[0126] Furthermore, for the automatic clearing of inscribed redundant points and redundant elements, it includes the following steps:

[0127] 1) Introduce a global array to record the global numbers of the mesh points after the merger, where is the number of computational subdomains, is the maximum number of mesh points in a computational subdomain;

[0128] 2) Perform the calculation of the global node numbers and the clearing of redundant points. Loop through the computational subdomains to scan the mesh points, using the backward scanning method to determine whether the mesh point is a real mesh point in the merged mesh, and then obtain the global number of the mesh point. The specific method is as follows:

[0129] For the first computational subdomain, all mesh points are marked as real mesh points, and the node numbers remain unchanged, that is, the global numbers of the mesh points are:

[0130]

[0131] Here, is for calculating the local grid point numbers in the sub-domain.

[0132] For the second computational sub-domain ( ), perform a sequential scan in point-by-point loop. Excluding the points that have already appeared in the first computational sub-domain (i.e., the points on the common interface between the second computational sub-domain and the first computational sub-domain), the remaining grid points are normally counted. For such counted grid points, their global numbers are:

[0133]

[0134] In the formula, is the cumulative sequence number of the grid points when accumulating to this grid point; for the points that have already appeared in the first computational sub-domain, they are assigned values using the existing node numbers according to the node matching relationship on the interface.

[0135] More generally, for the th computational sub-domain, perform a sequential scan in point-by-point loop. Excluding the points that have already appeared in the th computational sub-domain (i.e., the points on the common interface between this computational sub-domain and other computational sub-domains), the remaining grid points are normally counted, and the calculation method of their global numbers is the same as in formula (14); for the points on the interface, they are assigned values using the existing node numbers according to the node matching relationship on the interface.

[0136] Perform volume mesh element merging and node number updating. Loop sequentially according to the computational sub-domains. For each volume mesh element, replace the element node numbers (local numbers ) with the global numbers .

[0137] Perform boundary surface element merging, node number updating, and redundant surface removal. Adopt the same method as the node number updating of the volume mesh elements. Loop sequentially according to the computational sub-domains. For each boundary surface mesh element, replace the element node numbers (local numbers ) with the global numbers . At the same time, judge whether this surface mesh element is a redundant boundary surface (internal interface). If so, directly remove this mesh element from the boundary surface mesh element list.

[0138] Through the above operations, the merging of the meshes of different computational sub-domains is substantially completed. The number of mesh points and the number of volume mesh elements after merging are respectively:

[0139]

[0140] Here, Denotes the number of grid points in the computational subdomain, Denotes the number of grid points on the internal interface (redundant grid points), Denotes the number of volume grid cells in the computational subdomain.

[0141] It should be noted that the above algorithm proposed in this application has universality and can handle the free merger of any number of computational subdomains. Taking Figures 7 to 8 a measured case of Figure 7 as an example, as shown in Figure 8 First, for the original cube computational domain, through artificial division, 4 different computational subdomains are formed. Among them, different generation strategies are adopted for each computational subdomain, and it is ensured that the interface grids between the computational subdomains have the same face grid distribution, that is, there are computational subdomains with various grid types such as hexahedrons, pyramids, prisms, and tetrahedrons. Select 3 of the computational subdomains for free merger. At this time, there will be a situation of redundant non-matching interfaces in the grid merger, which is generally difficult to handle by general algorithms. The final grid merger result given by the algorithm in this application is shown. Among them, the left figure shows the situation without redundant point removal, and there are obviously unreasonable interfaces; the right figure shows the correct merger result after redundant point removal, which verifies the effectiveness of the algorithm.

[0142] Starting from the free merger algorithm of the above-mentioned arbitrary number of computational subdomains, if each computational subdomain reaches the scale of tens of millions of grids, then the scale of the final generated computational grid is very easy to reach hundreds of millions, or even billions of grids. By reasonably controlling the grid distribution of the computational subdomains, it is relatively easy to meet the computational grid resolution requirements for high-fidelity simulation of the whole machine.

[0143] Output the grid data file in the form of a standard CGNS data file, including the output of the merged coordinate points, volume grid connection relationships, face element connection relationships, and the outer boundary and boundary conditions after removing the redundant internal interfaces, etc.

[0144] Specifically, the underlying CGNS library can use versions above CGNS3.2 to support parallel CGNS reading and requires support for large file output of more than 2GB (2000M bytes).

[0145] Furthermore, in the output standard CGNS data file, the output content mainly includes the coordinate points of the merged grid, the definitions of volume grid cells of different types (tetrahedrons, prisms, pyramids, etc.) and the corresponding connection information, the definitions of boundary cells of different types (triangles or quadrilaterals) and the corresponding connection information, the outer boundary and boundary conditions after removing the redundant internal interfaces, and other relevant information.

[0146] Furthermore, in the output standard CGNS data file, the boundary surface is defined according to the face center (FaceCenter) and the face patch serial number list (PointList).

[0147] Furthermore, during the output process of the merged CGNS file, the memory overhead can be saved and the real-time processing of large-throughput grid data can be better supported by scanning the computational subdomain grids, merging the real grids, releasing the occupied memory, and outputting in a certain way.

[0148] As an example, this application gives a specific example of generating a high-fidelity computational grid for the wing-body combination model of the NASA Common Research Model commercial aircraft (abbreviation: NASA-CRM) using the above complete algorithm process. The specific operation steps follow steps S1 - S6 of this application. Figure 9 Schematic diagrams of the symmetric plane grids of different computational subdomains are given. Figure 10 After independently generating the computational subdomain grids using the above strategy, large grids further merged are given. For the same configuration, the measured results show that for grids in the order of tens of millions, the time consumed in the grid merging link is in the order of minutes; for grids in the order of hundreds of millions, the time consumed in the grid merging link is within the order of hours, and the grid merging generation efficiency is within an acceptable range.

[0149] Based on the above method embodiments, this application also provides a device for automatically generating large-scale unstructured grids for commercial aircraft, including a computational domain segmentation module, a surface grid generation module, a volume grid generation module, a hybrid volume grid generation module, a density determination module, and a grid merging module. Among them, the computational domain segmentation module is used to determine the computational domain based on the geometric model of the aircraft body and components, construct the inscribed geometric surface, the near-field bounding box, and the far-field bounding box, and geometrically segment the computational domain surrounded by the near-field bounding box and the far-field bounding box using the inscribed geometric surface to obtain each far-field region, where the computational domain within the near-field bounding box is the near-field region; the surface grid generation module is used to generate the first surface grid on the surfaces of the aircraft body and each component in the approach region using the similarity rule, generate the second surface grid on the inscribed geometric surface between the near-field region and the far-field region, and generate the third surface grid on the surface of the far-field bounding box; the volume grid generation module is used to generate unstructured grids for each far-field region based on the second surface grid and the third surface grid using the improved Delaunay algorithm; the hybrid volume grid generation module is used to automatically generate a basic unstructured hybrid volume grid with an attached boundary layer grid in the near-field region based on the first surface grid using the attached boundary layer grid automatic generation algorithm; the density determination module is used to process the unstructured grids in the far-field region and the basic unstructured hybrid volume grid with an attached boundary layer grid based on the local grid omnidirectional dissection algorithm to obtain each computational subdomain with unstructured grids of the desired density; the grid merging module is used to automatically merge the unstructured grids in each computational subdomain based on the arbitrary sub-region unstructured hybrid grid automatic merging algorithm to obtain large-scale unstructured grids for commercial aircraft.

[0150] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0151] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the relevant part of the method embodiment for the relevant content.

[0152] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A method for automatically generating large-scale unstructured grids for commercial aircraft, characterized in that, Including: Determine the computational domain based on the geometric models of the aircraft airframe and components, construct the inscribed geometric surface, near-field bounding box, and far-field bounding box, and use the inscribed geometric surface to geometrically divide the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain each far-field region. Among them, the computational domain within the near-field bounding box is the near-field region; Generate the first surface mesh on the surfaces of the aircraft airframe and each component in the approach region using the similarity rule, generate the second surface mesh on the inscribed geometric surface between the near-field region and the far-field region, and generate the third surface mesh on the surface of the far-field bounding box; Adopt the improved Delaunay algorithm to generate the unstructured grids of each far-field region based on the second surface mesh and the third surface mesh, including: Read the second surface mesh and the third surface mesh and use them as boundary grids; Adopt the volume mesh generation algorithm based on the improved Delaunay to generate the unstructured grids of each far-field region that automatically contain boundary condition information based on the second surface mesh and the third surface mesh; Adopt the boundary layer mesh automatic generation algorithm to automatically generate the basic unstructured hybrid mesh with boundary layer meshes in the near-field region based on the first surface mesh, including: Obtain each grid point of the first surface mesh, use each grid point as an array point, and calculate the advancement vectors of each array point on different surface meshes; Weight-average the advancement vectors of each array point on different grids; Advance the first layer based on each array point along the advancement vector in the direction away from the surface of the aircraft airframe or each component; Connect each grid point on the first layer to obtain new array points, and continue to advance new layers based on the new array points along the advancement vector in the direction away from the surface of the aircraft airframe or each component until the preset number of layers is reached to obtain the boundary layer mesh; among them, the height of each layer and the total number of advancement layers are determined based on the preset values; Adopt the aforementioned improved Delaunay algorithm to generate the basic unstructured hybrid mesh with boundary layer meshes between the boundary layer mesh and the near-field bounding box; Process the unstructured grids of the far-field region and the basic unstructured hybrid mesh with boundary layer meshes based on the local grid omnidirectional dissection algorithm to obtain each computational subdomain with the desired density of unstructured grids, including: Obtain all the edges of each grid in the unstructured grids of the far-field region and the basic unstructured hybrid mesh with boundary layer meshes; Use the octree dissection method to encrypt and mark each edge, and dissect each edge into two parts based on the encryption mark to obtain each volume mesh after octree dissection; Based on each volume mesh after octree dissection, obtain each computational subdomain with the desired density of unstructured grids; Based on the arbitrary sub-region unstructured hybrid mesh automatic merging algorithm, automatically merge the unstructured grids in each computational subdomain to obtain the large-scale unstructured mesh of the commercial aircraft.

2. The method for automatically generating a large-scale unstructured grid for a commercial aircraft according to claim 1, wherein The determination of the computational domain based on the geometric models of the aircraft airframe and components, the construction of the inscribed geometric surface and the far-field bounding box, and the geometric division of the computational domain enclosed by the far-field bounding box using the inscribed geometric surface to obtain each computational subdomain includes: Import the geometric digital model of the aircraft airframe using a geometric modeling tool, and create a far-field rectangular bounding box and a near-field rectangular bounding box according to the preset computational domain size; Using a geometric modeling tool, create an inscribed geometric surface within the computational domain without the aircraft airframe according to the preset position and size, so as to divide the computational domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple computational sub-domains.

3. A method for automatically generating a large-scale unstructured grid of a commercial aircraft according to claim 1, characterized in that, Before generating the first surface mesh on the surfaces of the aircraft airframe and each component of the aircraft in the approach area using the similarity rule, the method further includes: For the non-watertight areas in the geometric digital model of the aircraft airframe, perform watertight treatment by means of geometric simplification and adding geometric surfaces to obtain an aircraft airframe with good watertightness.

4. A method for automatically generating a large-scale unstructured grid for a commercial aircraft according to claim 1, characterized in that The method of generating the first surface mesh on the surfaces of the aircraft airframe and each component of the aircraft in the approach area using the similarity rule, generating the second surface mesh on the inscribed geometric surfaces in the near-field area and the far-field area, and generating the third surface mesh on the surface of the far-field bounding box, includes: Adopt the same grid point distribution for the line elements and surface elements on the inscribed geometric surfaces and on the surface of the near-field bounding box, and respectively obtain the second surface mesh and the third surface mesh; Perform triangulation on the parameter plane to obtain a triangulation of the plane, and transform the triangulation of the plane to the curved surfaces in the space of the aircraft airframe and each component of the aircraft to obtain the first surface mesh.

5. The method for automatically generating a large-scale unstructured grid for a commercial aircraft according to claim 4, wherein The step of performing triangulation on the parameter plane to obtain a triangulation of the plane, and transforming the triangulation of the plane to the curved surfaces in the space of the aircraft airframe and each component of the aircraft to obtain a parameterized plane includes: Construct an original grid plane covering the entire computational domain; Obtain the boundary points of the aircraft airframe and each component of the aircraft, and insert the boundary points into the original grid using the Bowyer-Watson algorithm; Restore the boundaries of the aircraft airframe and each component of the aircraft based on the boundary points, and delete the elements outside the boundaries to obtain an initial grid surface; Calculate the grid distribution function values of each boundary point, and determine the grid metric matrix based on the grid distribution function values; Generate points on the curved surfaces in the space of the aircraft airframe and each component of the aircraft based on the grid metric matrix, and generate the first surface mesh based on the initial grid surface using the Bowyer-Watson algorithm.

6. A method for automatically generating a large-scale unstructured grid for a commercial aircraft according to claim 1, characterized in that The step of automatically generating an attached boundary layer grid-based unstructured hybrid grid in the near-field area based on the first surface mesh using an attached boundary layer grid automatic generation algorithm includes: Obtain each grid point of the first surface mesh, use each grid point as an array point, and calculate the advancement vectors of each array point on different surface meshes; Perform weighted averaging on the advancement vectors of each array point on different grids; Advance the first layer based on each array point along the advancement vector in the direction away from the surface of the aircraft airframe or each component of the aircraft; Connect the grid points on the first layer to obtain new array points, and continue to advance a new layer based on the new array points along the advancement vector in the direction away from the surface of the aircraft airframe or each component of the aircraft until the preset number of layers is reached to obtain the attached boundary layer grid; wherein, the height of each layer and the total number of advancement layers are determined based on preset values; Adopt the aforementioned improved Delaunay algorithm to generate an attached boundary layer grid-based unstructured hybrid grid between the attached boundary layer grid and the near-field bounding box.

7. An automatic generation device for large-scale unstructured grids of commercial aircraft, characterized in that, including: A computational domain division module, configured to determine a computational domain based on the geometric models of the aircraft airframe and components, construct an inscribed geometric surface, a near-field bounding box, and a far-field bounding box, and geometrically divide the computational domain enclosed by the near-field bounding box and the far-field bounding box using the inscribed geometric surface to obtain each far-field region, where the computational domain within the near-field bounding box is the near-field region; A surface mesh generation module, configured to generate a first surface mesh on the surfaces of the aircraft airframe and each component in the approach region, a second surface mesh on the inscribed geometric surface between the near-field region and the far-field region, and a third surface mesh on the surface of the far-field bounding box using a similarity rule; A volume mesh generation module, configured to generate unstructured meshes for each far-field region based on the second surface mesh and the third surface mesh using an improved Delaunay algorithm, including: Reading the second surface mesh and the third surface mesh and using them as boundary meshes; Using a volume mesh generation algorithm based on the improved Delaunay to generate unstructured meshes for each far-field region that automatically include boundary condition information based on the second surface mesh and the third surface mesh; A hybrid volume mesh generation module, configured to automatically generate a basic unstructured hybrid volume mesh with a boundary layer mesh in the near-field region based on the first surface mesh using a boundary layer mesh automatic generation algorithm, including: Obtaining each grid point of the first surface mesh, using each grid point as an array point, and calculating the advancement vectors of each array point on different surface meshes; Weighted averaging the advancement vectors of each array point on different meshes; Advancing the first layer in a direction away from the surface of the aircraft airframe or each component along the advancement vector for each array point; Connecting each grid point on the first layer to obtain new array points, and continuing to advance a new layer in a direction away from the surface of the aircraft airframe or each component along the advancement vector based on the new array points until a preset number of layers is reached to obtain a boundary layer mesh; where the layer height of each layer and the total number of advancing layers are determined based on preset values; Using the aforementioned improved Delaunay algorithm to generate a basic unstructured hybrid volume mesh with a boundary layer mesh between the boundary layer mesh and the near-field bounding box; A density determination module, configured to process the unstructured meshes of the far-field regions and the basic unstructured hybrid volume mesh with a boundary layer mesh based on a local grid omnidirectional dissection algorithm to obtain each computational subdomain with an unstructured mesh of the desired density, including: Obtaining all the edges of each mesh in the unstructured meshes of the far-field regions and the basic unstructured hybrid volume mesh with a boundary layer mesh; Using an octree dissection method to encrypt and mark each edge, and dissecting each edge into two parts based on the encryption mark to obtain each volume mesh after octree dissection; Based on each volume mesh after octree dissection, obtaining each computational subdomain with the desired unstructured mesh density; A mesh merging module, configured to automatically merge the unstructured meshes in each computational subdomain based on an arbitrary sub-region unstructured hybrid mesh automatic merging algorithm to obtain a large-scale unstructured mesh for a commercial aircraft.

Images