Method and device for automatically generating large-scale unstructured grids of commercial aircraft

Abstract

The invention discloses an automatic generation method and device for a large-scale unstructured grid of a commercial aircraft. The method comprises the following steps: carrying out geometric segmentation on a computational domain enclosed by a near-field bounding box and a far-field bounding box through a constructed inscribed geometric surface to obtain far-field regions; generating a first surface grid on the surfaces of the aircraft body and the aircraft parts in the near-field region, generating a second surface grid on the inscribed geometric surface of the near-field region and the far-field region, and generating a third surface grid on the surface of the far-field bounding box; generating a non-structural body grid based on the second surface grid and the third surface grid; automatically generating a basic unstructured mixture grid in the near-field region based on the first surface grid; according to the commercial aircraft large-scale non-structural hybrid grid generation method and device, the calculation domains are divided into the calculation sub-domains, automatic generation of the grids of the calculation sub-domains is achieved, then automatic combination of the grids is achieved, the technical bottlenecks that super-large-scale non-structural grids are difficult to generate, the automation degree is low and the like are effectively solved, and the commercial aircraft large-scale non-structural hybrid grid generation method and device are suitable for large-scale aircraft large-scale non-structural hybrid grids. And the simulation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of computational fluid dynamics simulation technology, 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., and obtains aerodynamic performance by solving the Navier-Stokes equations. With the rise of artificial intelligence and digital twin technology, achieving high-fidelity fluid dynamics simulation of the whole 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 whole aircraft with engines; for this purpose, continuous efforts are needed in the fields of high-performance computing, grid generation and adaptation, numerical algorithms, etc.

[0003] It is generally believed that grid generation accounts for 70% or even more of the entire work to achieve a complete fluid dynamics simulation. How to generate large-scale computational grids required to support high-fidelity fluid dynamics simulation of complex whole-aircraft configurations is one of the challenging problems that CFD technology must face currently and in the future. For the generation of large-scale unstructured grids required for high-fidelity fluid dynamics simulation at the whole-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 grid generation on the order of hundreds of millions of grids, which is very time-consuming and laborious. In addition, cross-media storage and transmission of data also bring the problem of disconnection between grid 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 of 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 sub-domain with a uniform density of unstructured grids; and automatically merging the unstructured grids in each computational sub-domain based on an arbitrary sub-region 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 sub-domain 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 a 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.

[0006] Optionally, after using the geometric modeling tool to create an inscribed geometric surface within the computational domain without the aircraft fuselage according to a preset position and size to divide the computational domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple computational sub-domains, 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 sub-domain.

[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 far-field bounding box surface 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 the near-field bounding box surface 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; restoring 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 in 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 in 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 method uses an automatic boundary layer grid generation algorithm 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 advancing vectors of each array point on different surface grids; weighting and averaging the advancing 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 advancing 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 advancing 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 advancing 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 method processes the far-field region unstructured grid and the basic unstructured hybrid grid with boundary layer grids based on a local grid omnidirectional dissection algorithm to obtain each computational subdomain with a uniform unstructured grid density, including: obtaining all edges of each grid in the far-field region unstructured grid and the basic unstructured hybrid grid with boundary layer grids; 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 grid after octree dissection; obtaining each computational subdomain with a desired unstructured grid density based on each volume grid after octree dissection.

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

[0014] To achieve the above object, the second aspect of the present application further provides a method and apparatus 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, where 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 having 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 embodiment of the present application provides 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 fuselage and components, constructing the inscribed geometric surface, near-field bounding box, and far-field bounding box, and using the inscribed geometric surface to geometrically divide the computational domain enclosed by the near-field bounding box and 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 the first surface grid on the surfaces of the aircraft fuselage and each component in the approach region using the similarity rule, generating the second surface grid on the inscribed geometric surface between the near-field region and the far-field region, and generating the third surface grid on the surface of the far-field bounding box; using the improved Delaunay algorithm to generate the unstructured grids of each far-field region based on the second surface grid and the third surface grid; using the boundary layer grid automatic generation algorithm to automatically generate the basic unstructured hybrid grid with boundary layer grids in the near-field region based on the first surface grid; processing the unstructured grids of 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 subdomain 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 subdomain to obtain the large-scale unstructured grids of the 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 subdomain. The present application can not only meet the large-scale unstructured hybrid grid generation requirements for high-fidelity fluid dynamics simulation of the entire commercial aircraft, but also, as a common technology, meet the large-scale computational grid generation requirements 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 the computational domain division to each computational subdomain, 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. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings 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 is the implementation flowchart of the method for automatically generating large-scale unstructured hybrid grids for high-fidelity fluid dynamics simulation of commercial aircraft in the embodiment of the present application; Figure 2 is the schematic diagram of the automatic division of the computational domain for high-fidelity fluid dynamics simulation of commercial aircraft in the embodiment of the present application; Figure 3 is a typical spatial surface triangle triangulation generated by the surface mesh generation algorithm in the embodiments of the present application; Figure 4 is a flow chart of the volume mesh generation algorithm in the embodiments of the present application; Figure 5 is a flow chart of the boundary layer mesh generation algorithm in the embodiments of the present application; Figure 6 is a schematic diagram of the automatic remeshing of tetrahedral meshes for secondary encryption in the embodiments of the present application; Figure 7 is a simple mesh merging example in the embodiments of the present application; Figure 8 is the mesh merging result of a simple test case in the embodiments of the present application; Figure 9 is a schematic diagram of the symmetric plane mesh of the NASA-CRM wing-body model before merging in the embodiments of the present application; Figure 10 is a schematic diagram of the mesh of the NASA-CRM wing-body model generated after merging in the embodiments of the present application. Detailed implementation manners

[0018] 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 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 belong to the scope of protection of the present application.

[0019] This specification provides method operation steps such as in the embodiments or flow charts, 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 in the order shown in the embodiments or the drawings, or executed in parallel (for example, in an environment of parallel processors or multi-threaded processing).

[0020] 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 the demand of high-fidelity fluid dynamics simulations at the whole-aircraft level of commercial aircraft. For the generation of large-scale unstructured meshes 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 the problem of the disconnection between mesh generation and fluid dynamics simulation calculations.

[0021] To solve the above problems, the present application comprehensively utilizes technologies such as geometric management, automatic mesh generation, adaptive adjustment of mesh density, and efficient automatic merging of multi-region 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 simulation at the whole-aircraft level of commercial aircraft.

[0022] Refer to Figure 1 , Figure 1 FIG. is a flowchart of a method for automatically generating large-scale unstructured meshes for commercial aircraft provided by the present application. This method can be executed by a processor of a terminal or a server. The method may include: S10. Determine a computational domain based on the geometric models of the aircraft fuselage 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.

[0023] Specifically, step S10 may include the following execution process: S101. Import the geometric digital model of the aircraft fuselage 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.

[0024] S102. Use a geometric modeling tool to create an inscribed geometric surface within the computational domain without the aircraft fuselage 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.

[0025] In an embodiment of the present application, after step S102, the method may further include the following execution process: Uniformly name the near-field rectangular bounding box and the inscribed geometric surface, and save the geometric data of each computational sub-domain.

[0026] In the specific execution 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 the far-field region, near-field region, and inscribed geometric surface through a computer terminal or a server terminal. The present application proposes an automatic positioning method for automatically dividing the computational domain. The method may be the following steps: First, the processor may obtain the geometric models of the aircraft fuselage and components. In this embodiment, an STL file is used as the geometric description model, and its typical definition form is as follows:

[0027] Then, based on the read geometric data, the processor calculates the center point of the geometric model and the maximum and minimum values in the X, Y, and Z directions by cyclically scanning the geometric patches and nodes:

[0028]

[0029] 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 calculation domain scaling parameter : (3) (4) Similarly, the corner coordinates of the far-field bounding box of the aircraft body and components can be determined by introducing the far-field calculation domain scaling parameter :

[0030]

[0031] Then, using the above corner coordinates, the processor can automatically divide the entire calculation domain into 1 near-field calculation sub-domain and 8 far-field calculation sub-domains, as shown in Figure 2 shown.

[0032] Finally, the processor can perform secondary division of the calculation sub-domains by artificially adding inscribed geometric surfaces to the above calculation sub-domains, so as to form a new combination of different calculation sub-domains for the entire calculation domain. This combination can be expressed in an abstract form as:

[0033] Here, represents the calculation domain, represents the calculation sub-domain, is the number of calculation sub-domains.

[0034] S20. Generate the first surface mesh on the surfaces 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 surfaces of the near-field area and the far-field area, and generate the third surface mesh on the surface of the far-field bounding box.

[0035] It should be noted that: the generation of the surface mesh can 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 inscribed geometric surfaces, etc.

[0036] In an embodiment of the present application, step S20 may include the following execution process: S201. On the inscribed geometric surface and on the surface of the near-field bounding box, the line elements and surface elements adopt the same grid point distribution, and the second surface grid and the third surface grid are obtained respectively.

[0037] Among them, the similarity rule means that the same grid point distribution is adopted for the line elements and surface elements on the inscribed geometric surface to ensure strict docking rules for the inscribed geometric surface. Among them, the inscribed geometric surface mainly includes the near-field bounding box of the near-field calculation subdomain ( Figure 2 the area labeled "9" in it), and the common inscribed geometric surface between the far-field calculation subdomains ( Figure 2 the areas labeled from "1" to "8" in it). On the inscribed geometric surface and on the surface of the near-field bounding box, the line elements and surface elements adopt the same grid point distribution, and the second surface grid and the third surface grid are obtained respectively. This can ensure that the finally generated computational grid is a strictly point-to-point docking grid, thereby ensuring that the above-mentioned inscribed geometric surface should be the same surface grid in different calculation subdomains.

[0038] It should be noted that before step S20, the method may further include the following execution process: For the non-watertight areas in the geometric digital model of the aircraft body, watertight treatment is carried out by means of geometric simplification and adding geometric surfaces to obtain an aircraft body with good watertightness.

[0039] The watertight treatment of the aircraft body and aircraft components is a prerequisite for automatically generating surface grids. It should be noted that in this application, whether it is the far-field area or the inscribed geometric surfaces of different calculation subdomains, they are all defined as regular rectangular areas with clear analytical expressions. Therefore, there are no problems of geometric repair and watertight treatment. However, for the areas of the aircraft body and aircraft components, geometric repair and watertight treatment are required under necessary circumstances.

[0040] The relevant methods for watertight treatment may 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 grid 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 automatic surface grid generation.

[0041] 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 body and each aircraft component to obtain the first surface grid.

[0042] Specifically, step S202 may include the following execution process: S2021. Construct an original grid plane covering the entire computational domain.

[0043] S2022. Obtain the boundary points of the aircraft fuselage and each component of the aircraft, and insert the boundary points into the original mesh using the Bowyer-Watson algorithm.

[0044] S2023. Restore the boundaries of the aircraft fuselage and each component of the aircraft based on the boundary points, and delete the elements outside the boundaries to obtain the initial mesh surface.

[0045] S2024. Calculate the mesh distribution function values of each boundary point, and determine the mesh metric matrix based on the mesh distribution function values.

[0046] S2025. Generate each point on the spatial surface of the aircraft fuselage and each component of the aircraft based on the mesh metric matrix, and generate the first surface mesh based on the initial mesh surface using the Bowyer-Watson algorithm.

[0047] Currently, the Delaunay triangulation method for planar regions is very mature, but the triangulation method for spatial surfaces has been in development. The triangulation of spatial surfaces can be carried out on the spatial surface, or through the parametric expression of the surface, triangulation is carried out on the parametric plane and then transformed to the spatial surface to form the triangulation of the spatial surface. This application adopts the latter method.

[0048] Take triangulation in the computational subdomain as an example. The basic steps are as follows: (1) Establish a coarse original mesh covering the entire computational domain . (2) According to the boundary points determined by the input aircraft fuselage and aircraft components, insert the boundary points into the coarse original mesh using the Bowyer-Watson algorithm. (3) Check and restore the boundary edges determined by the aircraft fuselage and aircraft components, and delete the elements outside the domain in the coarse original mesh to form an initial mesh consisting only of boundary points. (4) Calculate the mesh distribution function values of the boundary points, and define the metric matrix, whose expression is:

[0049] In the formula, , is the mesh distribution function, , represents the tangent plane of any point on the aircraft fuselage or aircraft component. (5) Generate the in-domain points of the aircraft fuselage 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 of a typical spatial surface generated by this method is given.

[0050] In the specific implementation process, the above algorithm needs to be recycled to perform triangular meshing on the boundary surfaces of the aircraft fuselage and aircraft components in categories, including: 1) First, generate surface meshes for the aircraft fuselage and each component. The relevant work includes setting the grid point distribution and size of the line elements and surface elements of the aircraft fuselage and aircraft components, and using the above surface mesh triangular meshing algorithm to automatically generate the surface meshes of the aircraft fuselage and each aircraft component. 2) Subsequently, generate surface meshes for 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 surface meshes for each regular inscribed geometric surface. 3) Again, generate surface meshes for 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 surface meshes for 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 fuselage, 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 fuselage 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.

[0051] 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.

[0052] In an embodiment of the present application, step S30 may include the following execution process: 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.

[0053] 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.

[0054] Figure 4The 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) recovering 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.

[0055] After classifying and generating the volume grid through the above improved Delaunay algorithm 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 for the near-field computational subdomain and the automatic generation of the volume grid for the far-field computational subdomain.

[0056] Among them, the automatic generation of the volume grid for the far-field computational subdomain is achieved by circularly assembling the inscribed geometric surface and the surface grid of 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 scale of the surface grid of 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).

[0057] The automatic generation of the volume grid for the near-field computational subdomain is achieved by assembling the surface grids of the aircraft body and components and the near-field bounding box to establish a computational 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 scale of the surface grid of the body.

[0058] The volume grid data saving names the grids of each computational 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 computational subdomain described above, a total of 1 near-field subdomain volume grid and 8 far-field subdomain volume grids will be generated.

[0059] 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.

[0060] For the computational sub-region containing the body surface of the aircraft, the present application provides an automatic boundary layer mesh generation algorithm below to automatically generate a basic unstructured hybrid volume mesh with boundary layer meshes.

[0061] S40: Use the automatic boundary layer mesh generation algorithm to automatically generate a basic unstructured hybrid volume mesh with boundary layer meshes in the near-field region based on the first surface mesh.

[0062] Specifically, step S40 may include the following execution process: 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.

[0063] S402: Weightedly average the advancement vectors of each array point on different meshes.

[0064] S403: Based on each array point, advance the first layer along the advancement vector in a direction away from the surface of the aircraft body or each component of the aircraft.

[0065] 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 along the advancement vector in a direction away from the surface of the aircraft body or each component of the aircraft 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.

[0066] S405: Use the 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.

[0067] Specifically, the automatic boundary layer mesh generation algorithm includes setting the wall distance of the expected first layer of mesh, setting the number of advancement layers of 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 advancement 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:

[0068] 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, return to step 2) and enter the next round of the advancement process. It is not difficult to see that for the generation of the boundary layer grid, it is necessary to set the grid spacing of the first layer and the number of layers for advancement, which are the key parameters of the above method.

[0069] 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-scale to facilitate automatic generation.

[0070] 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.

[0071] In an embodiment of the present application, step S50 may include the following execution process: 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.

[0072] S502. Use the octal 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 octal dissection.

[0073] S503. Based on each volume grid after octal dissection, obtain each computational subdomain with a uniform density of the unstructured body grid.

[0074] In the specific execution process, the processor uses the local grid omnidirectional dissection algorithm, specifically the octal 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 patterns, quarter patterns, octal patterns, etc. In this application, for the omnidirectional encryption of tetrahedrons, the octal pattern 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.

[0075] 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 using a unified name or a unified generalized boundary condition BCGeneral, etc. for special output marking of the near-field envelope surface and the inscribed geometric surfaces within each computational subdomain, aiming to facilitate the subsequent search and positioning of the common interface.

[0076] In this example, the inscribed geometric surfaces are uniformly named in the form of "innerface + label number". For example, if there are 2 inner geometric surfaces in a computational subdomain, they can be combined into one side boundary surface in the CGNS file and named "innerface"; they can also be stored independently as two side boundary surfaces and named "innerface1" and "innerface2" in sequence. The subsequent operations can locate the inscribed side boundary surfaces by searching for the side 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 side boundary surfaces by judging the boundary condition types.

[0077] S60. Based on the automatic merging algorithm of unstructured hybrid meshes for arbitrary sub-regions, automatically merge the unstructured meshes in each computational subdomain to obtain a large-scale unstructured mesh of a commercial aircraft.

[0078] In an embodiment of this application, step S60 may include the following execution process: S601. Based on a preset search algorithm, obtain each inscribed surface of each computational subdomain.

[0079] 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.

[0080] S603. Determine the true matching relationship between 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.

[0081] S604. Based on a predefined global array, clear the redundant points in each computational subdomain and automatically merge the redundant unstructured meshes in each computational subdomain to obtain a large-scale unstructured mesh of a commercial aircraft.

[0082] Specifically, the automatic merging of any multi-zone unstructured hybrid meshes includes locating and searching for the inscribed surfaces based on unified names or special markings such as unified boundary types, automatically calculating the overlapping relationships between surfaces and between points in the inscribed surface data, automatically backtracking the overlapping relationships between points, and automatically clearing redundant points and redundant elements after mesh merging, etc.

[0083] Furthermore, the algorithm for locating and searching for the inscribed surfaces based on unified names or special markings such as unified boundary types can be represented by the following pseudo-code:

[0084] Here, the relevant preprocessing operations refer to the preprocessing operations for the inscribed surfaces carried out in advance to facilitate the implementation of inscribed surface matching, including: (1) calculating the centroid of each inscribed surface element face, and the calculation method is:

[0085] Here, is the defined number of nodes of the face element. For triangular face elements, its value is 3, and for quadrilateral face elements, its value is 4. is the node coordinate; (2) calculating the area of each face element. For triangular face elements, according to Heron's formula for calculating the area of any triangle, the calculation method is:

[0086] Here, are the side lengths of the 3 sides; for quadrilateral face elements, the diagonal sides of the quadrilateral element can be used to transform it into the sum of the areas of 2 triangular face elements for calculation, and the calculation method is:

[0087] Unify and output the inscribed surface 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 inscribed surface information through this file, which is convenient for subsequent matching of elements and nodes based on this database. The pseudo-code for the database building process is as follows:

[0088] Furthermore, the automatic calculation of the overlapping relationships between surfaces and between points in the inscribed surface data, that is, by retrieving the inscribed surface information library common_surf.x to obtain all inscribed surfaces, and then establishing the surface matching relationship and the point matching relationship. The specific matching can adopt the following backward scanning algorithm:

[0089] In the above pseudocode, represents the total number of inscribed faces to be retrieved, represents the desired deviation threshold. It is worth noting that the last step of the above operation is to update the information in the inscribed face information library and append the matching information of faces and nodes, with the aim of facilitating subsequent operations such as backtracking of point matching relationships and removal of redundant points.

[0090] Furthermore, for the automatic backtracking of the point-to-point lapping relationship, 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 matching relationship found in the following 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 merging, the points of computational subdomain 2 are automatically cleared after the mesh merging, and the matching points of computational subdomain 3 cannot match the real points. Based on the aforementioned matching relationship, the real matching relationship is established through backtracking: .

[0091] Furthermore, the automatic removal of inscribed redundant points and redundant elements includes the following steps: 1) Introduce a global array to record the global numbers of the mesh points after merging, where is the number of computational subdomains, is the maximum number of mesh points in a computational subdomain; 2) Perform the calculation of the global node numbers and the removal of redundant points. Loop through the mesh points of the computational subdomains for scanning, 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: 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:

[0092] Here, is the local mesh point number in the computational subdomain.

[0093] For the second computational subdomain ( ), perform sequential scanning in a point-by-point loop. Except for the points that have already appeared in the first computational subdomain (i.e., the points on the common interface between the second computational subdomain and the first computational subdomain), the remaining grid points are normally counted. For such counted grid points, their global numbers are as follows:

[0094] where is the cumulative sequence number of the grid point when accumulating to this grid point; for the points that have already appeared in the first computational subdomain, according to the node matching relationship on the interface, use the existing node numbers for assignment.

[0095] More generally, for the th computational subdomain, perform sequential scanning in a point-by-point loop. Except for the points that have already appeared in the th computational subdomain (i.e., the points on the common interface between this computational subdomain and other computational subdomains), the remaining grid points are normally counted, and the calculation method of their global numbers is the same as that in Equation (14); for the points on the interface, according to the node matching relationship on the interface, use the existing node numbers for assignment.

[0096] Perform volume mesh element merging and node number update. Sequentially loop according to the computational subdomains. For each volume mesh element, replace the element node number (local number ) with the global number .

[0097] Perform boundary face element merging, node number update, and redundant face removal. Adopt the same method as the node number update of the volume mesh elements. Sequentially loop according to the computational subdomains. For each boundary face mesh element, replace the element node number (local number ) with the global number . At the same time, judge whether this face mesh element is a redundant boundary face (internal interface). If so, directly remove this mesh element from the boundary face mesh element list.

[0098] Through the above operations, the merging of the meshes of different computational subdomains is substantially completed. The number of grid points and the number of volume mesh elements after merging are respectively:

[0099] Here, represents the number of grid points of the computational subdomain, represents the number of grid points of the internal interface (redundant grid points), represents the number of volume mesh elements of the computational subdomain.

[0100] It should be noted that the above algorithm proposed in this application has universality and can handle the free merging of any number of computational subdomains. Taking a Figures 7 to 8 measured case as an example, such asFigure 7 As shown, first, for the original cube computational domain, through artificial division, 4 different computational sub-domains are formed. Among them, each computational sub-domain is generated using a different generation strategy, and it is ensured that the interface grids between the computational sub-domains maintain the same surface grid distribution, that is, there are computational sub-domains with various grid types such as hexahedrons, pyramids, prisms, and tetrahedrons. Select 3 of these computational sub-domains for free merging. At this time, in the grid merging, there will be a situation where there are redundant non-matching interface surfaces, and it is difficult for general algorithms to handle. Figure 8 The final grid merging result given by the algorithm in this application is shown. Among them, on the left side of the figure is the situation without redundant point removal, and there are obviously unreasonable interface surfaces; on the right side of the figure is the correct merging result after redundant point removal, which verifies the effectiveness of the algorithm.

[0101] Starting from the free merging algorithm of any of the above-mentioned computational sub-domains, if each computational sub-domain reaches the scale of tens of millions of grids, then the scale of the finally generated computational grid is very easy to reach over hundreds of millions, or even over billions of grids. By reasonably controlling the grid distribution of the computational sub-domains, it is relatively easy to meet the computational grid resolution requirements for high-fidelity simulation of the whole machine.

[0102] Output the grid data file in the form of a standard CGNS data file, including outputting the merged coordinate points, the connection relationships of volume grids, the connection relationships of surface elements, and the outer boundaries and boundary conditions after removing redundant internal interfaces, etc.

[0103] Specifically, the underlying CGNS library can use versions above CGNS 3.2 to support parallel CGNS reading, and it is required to support the output of large files above 2GB (2000M bytes).

[0104] Furthermore, in the standard CGNS data file output, the output content mainly includes the coordinate points of the merged grid, the definitions of volume grid elements of different types (tetrahedrons, prisms, pyramids, etc.) and the corresponding connection information, the definitions of boundary elements of different types (triangles or quadrilaterals) and the corresponding connection information, the outer boundaries and boundary conditions after removing redundant internal interfaces, and other related information.

[0105] Furthermore, in the standard CGNS data file output, the boundary surface is defined according to the face center (FaceCenter) and the face patch serial number list (PointList).

[0106] Furthermore, during the output process of the merged CGNS file, the memory overhead can be saved by scanning the computational sub-domain grids on the side, performing real grid merging on the side, releasing the occupied memory on the side, and the output method, so as to better support the real-time processing of large-throughput grid data.

[0107] As an example, this application provides a specific instance of generating a high-fidelity computational grid for the wing-body combination model of the NASA Common Research Model (abbreviation: NASA-CRM) using the above complete algorithm process. The specific operation steps follow steps S1-S6 of this application. Figure 9 The schematic diagrams of the meshes on the symmetry planes of different computational sub-domains are given. Figure 10 After independently generating the meshes of the computational sub-domains using the above strategy, the merged large-scale mesh is further given. For the same configuration, the measured results show that for meshes in the order of tens of millions, the time consumption in the mesh merging process is in the order of minutes; for meshes in the order of hundreds of millions, the time consumption in the mesh merging process is within the order of hours, and the mesh merging generation efficiency is within an acceptable range.

[0108] Based on the above method embodiments, this application further provides a device for automatically generating large-scale unstructured meshes for commercial aircraft, including a computational domain segmentation module, a surface mesh generation module, a volume mesh generation module, a hybrid volume mesh generation module, a density determination module, and a mesh merging module. Among them, the computational domain segmentation module is used to determine the computational domain based on the geometric models of the aircraft body and components, construct the inscribed geometric surface, the near-field bounding box, and the far-field bounding box, and use the inscribed geometric surface to geometrically segment the computational domain surrounded 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; the surface mesh generation module is used to generate the first surface mesh on the surfaces of the aircraft body 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; the volume mesh generation module is used to generate the unstructured volume meshes of each far-field region based on the second surface mesh and the third surface mesh using the improved Delaunay algorithm; the hybrid volume mesh generation module is used to automatically generate the basic unstructured hybrid volume mesh with boundary layer meshes in the near-field region based on the first surface mesh using the boundary layer mesh automatic generation algorithm; the density determination module is used to process the unstructured volume meshes of the far-field region and the basic unstructured hybrid volume mesh with boundary layer meshes based on the local mesh omnidirectional dissection algorithm to obtain each computational sub-domain with unstructured volume meshes of the desired density; the mesh merging module is used to automatically merge the unstructured volume meshes in each computational sub-domain based on the arbitrary sub-region unstructured hybrid mesh automatic merging algorithm to obtain the large-scale unstructured meshes for commercial aircraft.

[0109] 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 terms "comprising", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0110] 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, and the differences between each embodiment and other embodiments are emphasized. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content.

[0111] 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: include: Based on the geometric model of the aircraft body and its components, the computational domain is determined, and the inscribed geometric surface, the near-field bounding box and the far-field bounding box are constructed. The inscribed geometric surface is used to geometrically segment the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain various far-field regions, wherein the computational domain within the near-field bounding box is the near-field region. Using similar rules, a first surface mesh is generated on the surface of the aircraft body and each aircraft component in the approach area, a second surface mesh is generated on the inscribed geometric surface of the near field area and the far field area, and a third surface mesh is generated on the surface of the far field bounding box; The improved Delaunay algorithm is used to generate the unstructured mesh of each far field area based on the second surface mesh and the third surface mesh; The boundary layer mesh automatic generation algorithm is used to automatically generate the basic unstructured hybrid mesh with the boundary layer mesh in the near field area based on the first surface mesh; Based on the local grid omnidirectional subdivision algorithm, the unstructured grid in the far field area and the basic unstructured hybrid grid with boundary layer grid are processed to obtain each computational subdomain with the desired density of unstructured grid; Based on the automatic merging algorithm of unstructured hybrid grids in arbitrary sub-regions, the unstructured grids in each computational subdomain are automatically merged to obtain a large-scale unstructured grid for commercial aircraft.

2. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The calculation domain is determined based on the geometric model of the aircraft body and its components, an inscribed geometric surface and a far-field bounding box are constructed, and the calculation domain enclosed by the far-field bounding box is geometrically segmented using the inscribed geometric surface to obtain various calculation subdomains, including: Use geometric modeling tools to import the geometric digital model of the aircraft body, and create far-field rectangular bounding boxes and near-field rectangular bounding boxes according to the preset calculation domain size; By using geometric modeling tools, an inscribed geometric surface is created in the calculation domain without the aircraft body according to preset positions and sizes, so as to divide the calculation domain between the far-field rectangular bounding box and the near-field rectangular bounding box into multiple calculation subdomains.

3. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: Before generating the first surface mesh on the surfaces of the aircraft body and aircraft components in the approach area by using similarity rules, the method further includes: The non-watertight areas in the geometric digital model of the aircraft body are watertightened by geometric simplification and adding geometric surfaces to obtain an aircraft body with good watertightness.

4. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The method of using similar rules to generate a first surface mesh on the surface of the aircraft body and each aircraft component in the approach area, generating a second surface mesh on the inscribed geometric surface of the near field area and the far field area, and generating a third surface mesh on the surface of the far field bounding box includes: The line elements and surface elements on the inscribed geometric surface and on the surface of the near-field bounding box adopt the same grid point distribution, and the second surface grid and the third surface grid are obtained respectively; Triangulation is performed on the parameter plane to obtain the triangulation of the plane, and the triangulation of the plane is transformed onto the curved surface of the space of the aircraft body and each aircraft component to obtain the first surface mesh.

5. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 4, characterized in that: The triangulation is performed on the parameter plane to obtain the triangulation of the plane, and the triangulation of the plane is transformed onto the curved surface of the space of the aircraft body and each component of the aircraft to obtain the parameterized plane, including: Construct the original grid plane covering the entire computational domain; Obtain the boundary points of the aircraft body and aircraft components, and insert the boundary points into the original grid using the Bowyer-Watson algorithm; The boundaries of the aircraft body and its components are restored based on the boundary points, and the elements outside the boundaries are deleted to obtain the initial mesh surface; Calculate the grid distribution function value of each boundary point, and determine the grid metric matrix based on the grid distribution function value; Points on the curved surface of the aircraft body and each component of the aircraft are generated based on the grid metric matrix, and a first surface grid is generated based on the initial grid surface based on the Bowyer-Watson algorithm.

6. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The improved Delaunay algorithm is used to generate non-structural meshes in 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 the second surface mesh and the third surface mesh as boundary meshes; The improved Delaunay-based volume mesh generation algorithm is used to automatically generate non-structured volume meshes for each far-field region containing boundary condition information based on the second surface mesh and the third surface mesh.

7. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The method of using the boundary layer mesh automatic generation algorithm to automatically generate a basic unstructured hybrid mesh with a boundary layer mesh in the near field region based on the first surface mesh includes: Obtain each grid point of the first surface grid, use each grid point as an array surface point, and calculate the advancement vector of each array surface point on different surface grids; Weighted average of the advancement vectors of each array point on different grids; Based on each array surface point, the first layer is advanced along the advancement vector in a direction away from the aircraft body or the surface of each aircraft component; Connecting each grid point on the first layer to obtain a new array point, and based on the new array point, continue to advance a new layer along the propulsion vector in a direction away from the aircraft body or the surface of each aircraft component until a preset number of layers is reached to obtain a boundary layer grid; wherein the layer height of each layer and the total number of propulsion layers are determined based on preset values; The improved Delaunay algorithm is used to generate a basic unstructured hybrid mesh with a boundary layer mesh between the boundary layer mesh and the near-field bounding box.

8. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The method of processing the non-structured body grid in the far field region and the basic non-structured hybrid body grid with the boundary layer grid based on the local grid omnidirectional subdivision algorithm to obtain each calculation subdomain with the desired non-structured body grid density includes: Get all the edges of each grid in the unstructured grid in the far field region and the basic unstructured hybrid grid with boundary layer grid; Each edge is encrypted and marked by using the eight-division method, and each edge is divided into two parts based on the encrypted mark to obtain each volume mesh after eight-division; Based on the meshes of each volume after octant, each computational subdomain with the desired non-structural mesh density is obtained.

9. The method for automatically generating large-scale unstructured grids for commercial aircraft according to claim 1, characterized in that: The algorithm for automatically merging unstructured hybrid grids in any sub-region is used to automatically merge unstructured grids in each computational sub-domain to obtain a large-scale unstructured grid for a commercial aircraft, including: Based on a preset search algorithm, each inscribed surface of each calculation subdomain is obtained; Determine the matching relationship between the cells of the inscribed surfaces of adjacent computational subdomains and the matching relationship between the points based on a backward scanning algorithm; Determine the real matching relationship between points on the internal surface according to the matching relationship between units of the internal surface of adjacent calculation subdomains and the matching relationship between points; Based on the predefined global array, the redundant points in each computational subdomain are cleared and the redundant unstructured meshes in each computational subdomain are automatically merged to obtain a large-scale unstructured mesh for commercial aircraft.

10. A device for automatically generating large-scale unstructured grids for commercial aircraft, characterized in that: include: The computational domain segmentation module is used to determine the computational domain based on the geometric model of the aircraft body and its components, construct the inscribed geometric surface, the near-field bounding box and the far-field bounding box, and use the inscribed geometric surface to geometrically segment the computational domain enclosed by the near-field bounding box and the far-field bounding box to obtain various far-field regions, wherein the computational domain within the near-field bounding box is the near-field region; A surface mesh generation module is used to generate a first surface mesh on the surface of the aircraft body and each aircraft component in the approach area by using a similar rule, generate a second surface mesh on the inscribed geometric surface of the near field area and the far field area, and generate a third surface mesh on the surface of the far field bounding box; The volume mesh generation module is used to generate the unstructured volume mesh of each far field region based on the second surface mesh and the third surface mesh using the improved Delaunay algorithm; A hybrid body mesh generation module is used to automatically generate a basic unstructured hybrid body mesh with a boundary layer mesh in a near-field region based on the first surface mesh by using a boundary layer mesh automatic generation algorithm; The density determination module is used to process the unstructured body grid in the far field region and the basic unstructured hybrid body grid with the boundary layer grid based on the local grid omnidirectional subdivision algorithm to obtain each calculation subdomain with the unstructured body grid of the desired density; The grid merging module is used to automatically merge the unstructured grids in each calculation subdomain based on the automatic merging algorithm of unstructured hybrid grids in any subdomain to obtain a large-scale unstructured grid for commercial aircraft.

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