Aero-engine blade flow field calculation platform based on automatic grid generation technology

Abstract

The invention discloses an aero-engine blade flow field calculation platform based on grid automatic generation technology, including S1) reading the aero-engine blade profile data and establishing a model; S2) using a hexahedral grid automatic generation method, that is, a transfinite interpolation method of a mapping function method to divide the model features, which includes encrypting one-dimensional contour line features; dividing the two-dimensional plane of the hexahedral grid; dividing a single hexahedron; generating regular hexahedral corner points; mapping the area between the two-dimensional planes, the surface mapping of the three-dimensional space, and the area mapping of the three-dimensional space, and generating hexahedral units one by one; S3) merging all hexahedral units to generate finite element units to complete the calculation of the aero-engine blade flow field. The invention adopts a line-based transfinite interpolation algorithm to achieve high-precision and high-quality generation of grids, and has the advantages of high division flexibility, fast generation speed, and good grid quality for a single large and complex model.

Description

Technical Field

[0001] The invention relates to the technical field of turbine machining, and in particular to an aviation engine blade flow field calculation platform based on automatic grid generation technology. Background Art

[0002] Since the 1960s, with the continuous development of computer technology, computational fluid dynamics (CFD) technology has also made continuous progress in aerospace, shipbuilding, water conservancy, chemical industry and other fields, and has achieved great achievements. With the rapid development of the current fluid field, CFD technology is increasingly demonstrating its great application value. The prerequisite for high-quality CFD calculations is high-quality meshing, and meshing technology is a bridge between theory and experiment. When a set of complex models needs to be processed, it is difficult for traditional finite volume analysis software to directly divide them. We often need to use other software to divide the model through various complex calculations, and then import it into the commonly used finite volume calculation software for processing. The biggest problem in meshing is the large amount of manual workload, which is also a bottleneck problem for CFD work efficiency.

[0003] When turbines are used in aviation and marine engines, their performance will directly affect the efficiency, noise, and CO2 emissions of aircraft and ships. At the same time, they are also widely used in gas turbine power generation, and have the characteristics of fast power generation response and low pollution. At the same time, gas turbine power generation can also be used as backup equipment for new energy power generation to compensate for power generation and solve the fluctuation problem of new energy power generation. Therefore, it is of great significance to national aviation, navigation, energy power generation, and the realization of carbon neutrality. There are complex viscous and unsteady three-dimensional flow fields in the turbine, accompanied by multiphase flow, heat transfer, combustion and other phenomena. The numerical calculation of the turbine flow field has undergone simplified methods such as S2 stream surface method, finite difference solution, S1 and S2 stream surface method of non-orthogonal curvilinear coordinate system. With the improvement of CFD algorithms and supercomputing capabilities, the widely used finite volume method for solving the full three-dimensional flow field of the NS equation can fully consider complex phenomena such as rotating flow, dynamic and static interference, transonic and supersonic. This application uses the characteristics of OpenFOAM to build a numerical platform for solving the fluid in turbine blades, in order to optimize the complex flow field of the turbine. Summary of the invention

[0004] The purpose of the present invention is to provide an aero-engine blade flow field calculation platform based on grid automatic generation technology, which adopts a line-based transfinite interpolation algorithm to achieve high-precision and high-quality grid generation, and has the advantages of high flexibility in dividing a single large and complex model, fast generation speed, and good grid quality.

[0005] To achieve the above object, the technical solution adopted by the present invention is: an aircraft engine blade flow field calculation platform based on grid automatic generation technology, comprising the following steps:

[0006] S1) reading the profile data of the aeroengine blades and establishing a model, the aeroengine blades including stator blades and rotor blades;

[0007] S2) using a hexahedral mesh automatic generation method, i.e., a transfinite interpolation method of a mapping function method, to divide the model features, which includes the following steps:

[0008] S21) encrypting the one-dimensional contour line feature;

[0009] S22) dividing the two-dimensional plane of the hexahedral grid;

[0010] S23) dividing a single hexahedron;

[0011] S24) generating corner points of a regular hexahedron;

[0012] S25) topological mapping includes region mapping between two-dimensional planes, surface mapping in three-dimensional space, and region mapping in three-dimensional space, and hexahedral units are generated one by one;

[0013] S3) Merge all hexahedral units to generate finite element units to complete the calculation of the flow field of the aero-engine blade.

[0014] As a further optimization, S2 also includes S26 to evaluate the quality of the hexahedral units generated by the mesh, and to judge the degree of deformity of the hexahedral units by comparing the differences in the micro-element volume magnification coefficients at each integration point through the unit determinant.

[0015] Compared with the prior art, the present invention has the following beneficial effects: the adopted transfinite interpolation algorithm is completely based on line meshing, and the algorithm solves the quality problem of surface mesh generation by itself, that is, the generated surface and the outer contour line must coincide, which avoids a large number of manual interactive parameter adjustment processes in commercial software to ensure the surface quality. In short, this program only needs to adjust the node distribution on the model lines to automatically generate the model mesh, and the high-quality surface of the module surface also makes the entire model mesh have a higher quality, thereby improving the accuracy of the finite volume method calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the modular grid partitioning method of the present invention.

[0017] Figure 2 It is a schematic diagram of area mapping in a two-dimensional plane.

[0018] Figure 3 Schematic diagram of surface mapping in three-dimensional space.

[0019] Figure 4 Schematic diagram of area mapping in three-dimensional space.

[0020] Figure 5 Schematic diagram of hexahedral region transformation.

[0021] Figure 6 Figure a is the wireframe diagram of the external flow field of the turbine stator blade.

[0022] Figure 6 Figure b is a wireframe diagram of the external flow field of the turbine rotor blade.

[0023] Figure 7 Figure a is a simple grid model diagram of the external flow field of the turbine stator blade.

[0024] Figure 7 Figure b is a simple grid model diagram of the external flow field of the turbine rotor blade.

[0025] Figure 8 This is the grid model diagram of the external flow field of the stator blade.

[0026] Fig. 9 This is the mesh model diagram of the external flow field of the rotor blade.

[0027] Fig.10 This is a bottom view of the groove flow field grid.

[0028] Fig.11 This is a histogram of the grid determinant detection of the external flow field of the rotor blade.

[0029] Fig.12 It is a histogram of the maximum-minimum internal angle detection of the rotor blade external flow field grid.

[0030] Fig.13 The histogram of the aspect ratio of the grid in the rotor blade outer flow field.

[0031] Fig.14 Streamline cloud diagram for LIC visualization of the flow field.

[0032] Fig.15 It is the pressure contour diagram of the flow field cross section. DETAILED DESCRIPTION

[0033] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0034] Currently, the intelligence level of meshing software is low, and most software requires manual and detailed adjustments after meshing. When the meshed model needs to be adjusted, all the work needs to be redone.

[0035] Taking Geomagic Studio meshing software as an example, its general division ideas are as follows: 1. Simplify the geometric model with 3D software; 2. Generate triangular faces; 3. Manually divide each 2D unit; 4. Generate 3D mesh units from 2D units; 5. Adjust the model, generate IGES file, and export the calculation.

[0036] The units divided by the above steps can generate a finite volume model for calculation. This method is very suitable for meshing of small fluid models. At present, the main meshing technologies are: automated meshing technology, overlapping meshing technology, Cartesian meshing technology, and adaptive mesh encryption technology. In these technical aspects, there are still many problems that have not been completely solved. For some large and complex model projects with millions of units, it is difficult to use such software for manual division. Before finding a truly universal meshing method, it is necessary to grasp the model characteristics through modeling calculations based on the particularity of this model and use other means to divide the mesh. The degree of refinement of the meshing can be adjusted arbitrarily according to the specific situation of the model, but the universality of different models is low, and some characteristic functions of the calculation algorithm need to be changed after some calculations before they can be used on different models. The modular meshing method is to finely classify various features of the model. The more features of the model are extracted, the more detailed the classification is, and when the model needs to be changed later, the details will be more convenient to change.

[0037] The present invention uses C++ platform for programming, selects hexahedral grid to divide the model, and writes the flowchart of the algorithm as shown in Figure 1 As shown. According to various characteristics of the model, the model is modularized, that is, divided into several hexahedral modules. The node distribution of the one-dimensional contour line of the hexahedral surface is finely controlled according to the characteristics of the entire model. For each hexahedral module, a unified automatic grid generation algorithm is used for grid division. The present invention uses the super-limit interpolation method of the mapping function method to achieve automatic grid generation.

[0038] The basic idea of ​​the transfinite interpolation method is to construct a mapping relationship between regions based on continuous boundary functions. For example, in the two-dimensional case, transform an area D on the physical plane (x, y) to a regular area on the computational plane (ξ, η) Then, it can be solved simply and regularly on the (ξ,η) plane and then mapped back to the area D to form the required grid. The process of this method is:

[0039] like Figure 2 As shown, the square area [0,1]×[0,1] in the ξη coordinate system is defined as the area The square area with curved edges in the xy coordinate system is defined as area D. Assume that there is an ideal coordinate mapping u:(ξ,η)→(x,y) between the two coordinate systems, where u is a coordinate mapping function that describes the correspondence between the coordinates (ξ,η) and (x,y), that is, u(ξ,η), and satisfies u: and Respectively, the regions and the boundary of D. The expression of the ideal mapping function u(ξ,η) is unknown, but the boundary node mapping relationship between the two regions is known, such as Figure 3 Each node on the boundary of u(0,η) in region D corresponds to the region In other words, u(0,η), u(1,η), u(ξ,0), and u(ξ,1) in region D can be understood as the continuous boundary functions mentioned above. According to the ideal mapping function u(ξ,η), a coordinate mapping F of a local region can be constructed: F is a description of two regions The correspondence between the coordinates (ξ,η) and (x,y) between and D is F(ξ,η). Here, let F(ξ,η) and u(ξ,η) be in the region and D satisfy the same mapping relationship on the boundary, that is,

[0040]

[0041] Therefore, F can be understood as an interpolation function of u, which has infinite points on the boundary that satisfy u, and is called a transfinite interpolation function. There are many forms of constructing transfinite interpolation functions. Here is a general construction process of a simple linear transfinite interpolation function:

[0042] (1) Construct a linear interpolation between the boundaries u(0,η) and u(1,η) in the direction of coordinate ξ:

[0043] L ξ =(1-ξ)u(0,η)+ξu(1,η),

[0044] (2) Construct a linear interpolation between the boundaries u(ξ,0) and u(ξ,1) in the direction of coordinate η:

[0045] L η =(1-η)u(ξ,0)+ηu(ξ,1),

[0046] (3) Construct bilinear interpolation based on the four corner points u(0,0), u(1,0), u(0,1), and u(1,1):

[0047]

[0048] (4) The linear transfinite interpolation function is:

[0049] F(ξ,η)=L ξ +L η -L ξη ,

[0050] In the formula, 0≤ξ≤1, 0≤η≤1, F(ξ,η) obviously meets the requirements.

[0051] The above is a description of transfinite interpolation for a general two-dimensional quadrilateral plane, while the transfinite interpolation for a three-dimensional hexahedron needs to be divided into two steps: 1. Transfinite interpolation of the three-dimensional surface of the hexahedron; 2. Transfinite interpolation inside the hexahedron.

[0052] like Figure 3 As shown in the figure, the square area [0,1]×[0,1] in the ξη plane in the ξη coordinate system is defined as the area The square area with curved edges in the xyz coordinate system is defined as area D. Assume that there is an ideal coordinate mapping u:(ξ,η,ζ)→(x,y,z) between the two coordinate systems, where u is the coordinate mapping function that describes the correspondence between the coordinates (ξ,η,ζ) and (x,y,z), i.e., u(ξ,η,z), and satisfies u: and Respectively, the regions and the boundary of D. The expression of the ideal mapping function u(ξ,η,ζ) is unknown, but the boundary node mapping relationship of the above two regions, that is, the continuous boundary function u(0,η,0), u(1,η,0), u(ξ,0,0), u(ξ,1,0) is known. According to the ideal mapping function u(ξ,η,ζ), a coordinate mapping F of a local region can be constructed: F is a description of two regions The correspondence between the coordinates (ξ,η,ζ) and (x,y,z) between and D is F(ξ,η,ζ). Here, let F(ξ,η,ζ) and u(ξ,η,ζ) be in the region and D satisfy the same mapping relationship on the boundary, that is,

[0053]

[0054] Therefore, F can be understood as an interpolation function of u, which has infinite points on the boundary that satisfy u, and is called a transfinite interpolation function. In the equation, ζ is a constant of 0, and F(ξ,η,ζ) can be written as F(ξ,η). Regarding the construction of transfinite interpolation function, a general construction process of simple linear transfinite interpolation function is given here:

[0055] (1) Construct a linear interpolation between the boundaries u(0,η,0) and u(1,η,0) in the direction of coordinate ξ:

[0056] L ξ =(1-ξ)u(0,η,0)+ξu(1,η,0),

[0057] (2) Construct a linear interpolation between the boundaries u(ξ,0,0) and u(ξ,1,0) in the direction of coordinate η:

[0058] L η =(1-η)u(ξ,0,0)+ηu(ξ,1,0),

[0059] (3) Construct bilinear interpolation based on the four corner points u(0,0,0), u(1,0,0), u(0,1,0), and u(1,1,0):

[0060]

[0061] (4) The linear transfinite interpolation function is:

[0062] F(ξ,η)=L ξ +L η -L ξη ,

[0063] In the formula, 0≤ξ≤1, 0≤η≤1, F(ξ,η) obviously meets the requirements.

[0064] like Figure 4 As shown, the cubic region of [0,1]×[0,1]×[0,1] in the ξηζ coordinate system is denoted as region The hexahedral region with curved surface in the xyz coordinate system is recorded as region D, and all mesh surfaces in the figure are discrete surfaces of the two regions. Similarly, it is assumed that there is an ideal coordinate mapping u:(ξ,η,ζ)→(x,y,z) between the two coordinate systems, that is, u(ξ,η,z), and satisfies u: and Respectively, the regions and the surface of D. The expression of the ideal mapping function u(ξ,η,ζ) is also unknown, but the surface interpolation function obtained by the transfinite interpolation of the three-dimensional surface of the hexahedron in step 1 can determine the node positions of all surface mesh divisions of the hexahedron, so the figures are all known boundary surface functions, that is, the surface node mapping relationship of the above two regions, that is, the required continuous boundary functions u(0,η,ζ), u(1,η,ζ), u(ξ,0,ζ), u(ξ,1,ζ), u(ξ,η,0), u(ξ,η,1) are all known. According to the ideal mapping function u(ξ,η,ζ), a coordinate mapping F of a local area can be constructed: F is a description of two regions The correspondence between the coordinates (ζ, η, ζ) and (x, y, z) between and D is F(ξ,η,ζ). Here, let F(ξ,η,ζ) and u(ξ,η,ζ) be in the region and D satisfy the same mapping relationship on the surface, that is,

[0065]

[0066] Since F(ξ,η,ζ) has infinite points on the surface of the hexahedron that satisfy the coordinate mapping relationship represented by u, it can be considered as the transfinite interpolation function of this hexahedron. The construction process of the linear interpolation function of the hexahedron is as follows:

[0067] (1) Construct a linear interpolation between the surfaces u(0,η,ζ) and u(1,η,ζ) in the direction of coordinate ξ:

[0068] L ξ =(1-ξ)u(0,η,ζ)+ξu(1,η,ζ),

[0069] (2) Construct a linear interpolation between the boundaries u(ξ,0,ζ) and u(ξ,1,ζ) in the direction of coordinate η:

[0070] L η =(1-η)u(ξ,0,ζ)+ηu(ξ,1,ζ),

[0071] (3) Construct a linear interpolation between the boundaries u(ξ,η,0) and u(ξ,η,1) in the direction of coordinate ζ:

[0072] L ζ =(1-ζ)u(ξ,η,0)+ζu(ξ,η,1),

[0073] (4) Construct a bilinear interpolation between the boundaries u(0,0,ζ), u(0,1,ζ), u(1,0,ζ), and u(1,1,ζ) in the directions of coordinates ξ and η:

[0074]

[0075] (5) Construct a bilinear interpolation between the boundaries u(ξ,0,0), u(ξ,0,1), u(ξ,1,0), and u(ξ,1,1) in the coordinates η and ζ:

[0076]

[0077] (6) Construct a bilinear interpolation between the boundaries u(0,η,0), u(0,η,1), u(1,η,0), and u(1,η,1) in the directions of coordinates ζ and ξ:

[0078]

[0079] (7) Construct trilinear interpolation based on the eight corner points of the cube u(0,0,0), u(0,0,1), u(0,1,0), u(0,1,1), u(1,0,0), u(1,0,1), u(1,1,0), u(1,1,1):

[0080] L ξηζ =(1-ξ)[(1-η)[(1-ζ)u(0,0,0)+ζu(0,0,1)]+η[(1-ζ)u(0,1,0)+ζu(0,1,1 )]+ξ[(1-η)[(1-ζ)u(1,0,0)+ζu(1,0,1)]+η[(1-ζ)u(1,1,0)+ζu(1,1,1)]]

[0081] (8) The transfinite interpolation function of the hexahedron is:

[0082]

[0083] In the formula, 0≤ξ≤1,0≤η≤1,0≤ζ≤1, and the linear construction of F(ξ,η,ζ) meets the requirements.

[0084] The purpose of meshing is to serve the calculation of the finite volume method, and the quality of the mesh, that is, the rationality of the mesh geometry, affects the accuracy of the calculation. There are many evaluation criteria for mesh quality, such as internal angle, skewness, aspect ratio, and warping. Although there are many parameters for evaluating mesh quality, the focus of mesh quality evaluation is different in different application scenarios. In the fluid computational simulation of the propeller external flow field involved in the present invention, the mesh quality is mainly evaluated based on the determinant and internal angle. In the hexahedral grid, the internal angle can be understood as the internal angle of the surface quadrilateral of the hexahedral unit, which is relatively easy to calculate, while the determinant characterizes the degree of deformity of the hexahedral unit, and the calculation is more complicated. The calculation method of the hexahedral unit determinant is as follows:

[0085] like Figure 5 As shown, to transform a set of independent variables (ξ,η,ζ) in the computational space into a new set of independent variables (x,y,z) in the physical space, the transformation is expressed by the following equation:

[0086]

[0087] It is known that the points (x1,y1,z1), (x2,y2,z2), (x3,y3,z3), (x4,y4,z4), (x5,y5,z5), (x6,y6,z6), (x7,y7,z7), and (x8,y8,z8) in the coordinate system xyz correspond to the points (-1,-1,-1), (1,-1,-1), (1,1,-1), (-1,-1,-1), (1,-1,1), (1,1,1), and (-1,1,1) in the coordinate system ξηζ, respectively.

[0088] At the same time, the hexahedron in the coordinate system ξηζ is constructed by connecting 8 known vertices with straight lines, while the coordinate system xyz is a standard cube, which needs to satisfy the mapping relationship between the two hexahedral regions. Therefore, the mapping function can be simply constructed using trilinear interpolation:

[0089]

[0090] In the formula,

[0091]

[0092]

[0093]

[0094]

[0095] For the transformation of three-dimensional space, the Jacobian determinant J can be expressed as:

[0096]

[0097] Therefore, the specific expression of the Jacobian determinant can be obtained. The value of the Jacobian determinant represents the magnification coefficient of the volume of the infinitesimal body after the transformation, that is, the ratio of the infinitesimal volume at a single point in the coordinate system ξηζ after the transformation. In the calculus substitution principle, the Jacobian determinant represents the ratio of the n-dimensional volume before and after the transformation. For example, the three-dimensional differential substitution is as follows:

[0098] dxdydz=Jdξdηdζ,

[0099] In fact, the determinant of a hexahedral unit is also called the relative determinant, which is a ratio of the minimum Jacobian determinant value to the maximum Jacobian determinant value at all integration points in the unit. Therefore, the unit determinant is used to judge the degree of deformity of the hexahedral unit by comparing the difference in the micro-element volume magnification coefficient at each integration point.

[0100] In order to reflect the universality of the algorithm, the present invention, based on the above-mentioned model modularization method and transfinite interpolation theory, takes turbine stator blades and rotor blades as examples to perform free mesh division of the blade external flow field. First, the external flow field model wireframe of the stator blades and rotor blades is obtained by manual division. Figure 6 As shown in the figure, the structure of the stator blade is relatively simple, and its external flow field is divided into three layers in the longitudinal direction; while the rotor blade is a complex structure with grooves on the top, so its external flow field is divided into five layers in the longitudinal direction. In fact, the wireframe of the blade external flow field has divided the entire model into several hexahedral modules. If the specific characteristics of the entire flow field are not considered, a simple uniform grid division can be performed for each module. Figure 7 As shown in the figure, a simple mesh of the flow field outside the blade is generated. Since the top of the rotor blade is grooved, Figure 7 The crescent-shaped area in (b) is also covered by the flow field grid.

[0101] In order to ensure the rationality and accuracy of fluid calculation, the division of flow field grids is very detailed, such as the division of fluid boundary layers, the control of grid size gradient, etc. Therefore, simple grid models obviously cannot meet the needs of fluid calculations. The modular grid division method proposed in this paper needs to determine the specific characteristics of the entire model in order to preset the node distribution of the one-dimensional contour line of each module, and then perform hexahedral grid division based on the transfinite interpolation method. Figure 8 This is the final meshing result of the flow field outside the stator blade. In order to meet the requirements of model characteristics and calculation accuracy, the overall mesh density has been greatly improved. The partially enlarged area in the figure is the flow field mesh containing the multi-module splicing at the front end of the blade and the blade boundary layer. It can be seen that this meshing takes into account the requirements of mesh size gradient and boundary layer mesh expansion. Similarly, according to the modular meshing method, Fig. 9 The final mesh generation result of the rotor blade external flow field is given. Since the rotor blade contains grooves, the model characteristics of the entire external flow field will be more complex and the module division will be more detailed. For example, in the partially enlarged mesh in the figure, that is, the tail of the groove, due to the groove boundary, more mesh expansion and gradient are required to meet the needs of fluid calculation. Fig.10 An overhead view of the flow field grid of the groove is given. The raised grid part in the figure completely corresponds to the area of ​​the blade groove, and the overall shape is crescent-shaped. At the same time, the figure partially enlarges the grid division details of the groove head and tail.

[0102] Generally speaking, to ensure the accuracy of fluid calculations, the division of the flow field grid needs to be very detailed, which also leads to the number of units divided by the entire model often reaching the million level. For example, the number of units in the external flow field grid of the above-mentioned rotor blades has reached more than 2 million. Faced with such a large number of units, the detection and verification of the grid quality is very important. In order to evaluate the quality of the flow field grid more conveniently and timely, the present invention further develops the function of grid quality verification based on the calculation framework of grid generation.

[0103] In order to ensure the accuracy of mesh quality verification, the present invention compares the results and improves the algorithm with the commercial software ICEM CFD. Fig. 9 The complex flow field grid presented, taking the determinant with relatively important grid quality parameters as an example, Fig.11 The comparison of the test results between the current calibration algorithm program and ICEM is given. In the figure, the arrows in the bar graph indicate that the data is much larger than the current vertical axis value range. It can be seen that the distribution comparison of the number of cells on the grid determinant on both sides is basically consistent. In fact, calculations on different platforms will produce certain precision errors, so there will be some errors in the specific distribution quantity, but the error ratio is very small and basically indistinguishable in the figure. The present invention continues with Fig.12 and Fig.13 The test results of the inner angle and aspect ratio in the mesh quality parameters are compared respectively. Similarly, the unit ratio distribution of the mesh parameters on both sides is basically the same. Regarding the mesh quality parameter aspect ratio, it refers to the ratio of the shortest side to the longest side of the mesh cell.

[0104] The present invention uses the method of super-finite interpolation to realize the modular meshing method of turbine blades. By demonstrating the effect of using eigenvalues ​​to control the fineness of model division, the method can effectively realize the finite volume meshing of large and complex models. Different from the algorithm of commercial software ICEM based on surface mesh generation, the present invention adopts the line-based super-finite interpolation algorithm to achieve high-precision and high-quality mesh generation, and has the advantages of high flexibility in the division of a single large and complex model, fast generation speed, and good mesh quality.

[0105] The present invention uses a three-dimensional steady-state Reynolds average k-ωSST turbulence model to simulate the flow of a secondary turbine. Figures 14 to 15 As shown in the figure, a mixed interface (mixingPlanes) is used at the interface between the mover and the stator to process information interaction, a periodic boundary (cyclicGgi) is used in the radial direction to simplify the model, and the steadyCompressibleMRFFoam solver of Foam-extend 3.1 is used to solve the flow field information.

[0106] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. The aero-engine blade flow field calculation platform based on automatic grid generation technology is characterized by: The following steps are included: S1) reading the profile data of the aero-engine blade and establishing a model; S2) using a hexahedral mesh automatic generation method to divide the model features, which includes the following steps: S21) encrypting the one-dimensional contour line feature; S22) dividing the two-dimensional plane of the hexahedral grid; S23) dividing a single hexahedron; S24) generating corner points of a regular hexahedron; S25) topological mapping, generating hexahedral units one by one; S3) merging all hexahedral units to generate finite element units to complete the calculation of the flow field of the aeroengine blade; Among them, the topological mapping in S2 includes the area mapping between two-dimensional planes, the surface mapping of three-dimensional space and the area mapping of three-dimensional space; The construction process of the linear transfinite interpolation function of the area mapping between the two-dimensional planes is as follows: Construct a linear interpolant between the boundaries u(0,η) and u(1,η) about the coordinate ξ: L ξ =(1-ξ)u(0,η)+ξu(1,η), Construct a linear interpolant between the boundaries u(ξ,0) and u(ξ,1) about the coordinate η: L η =(1-η)u(ξ,0)+ηu(ξ,1), Construct a bilinear interpolation based on the four corner points u(0,0), u(1,0), u(0,1), and u(1,1): The linear transfinite interpolation function is: F(ξ,η)=L ξ +L η -L ξη , Where, 0≤ξ≤1, 0≤η≤1, F(ξ,η) satisfies the following formula: The construction process of the linear transfinite interpolation function of the surface mapping of the three-dimensional space is as follows: Construct a linear interpolant between the boundaries u(0,η,0) and u(1,η,0) about the coordinate ξ: L ξ =(1-ξ)u(0,η,0)+ξu(1,η,0), Construct a linear interpolant between the boundaries u(ξ,0,0) and u(ξ,1,0) in the direction of coordinate η: L η =(1-η)u(ξ,0,0)+ηu(ξ,1,0), Construct a bilinear interpolation based on the four corner points u(0,0,0), u(1,0,0), u(0,1,0), and u(1,1,0): The linear transfinite interpolation function is: F(ξ,η)=L ξ +L η -L ξη , Where, 0≤ξ≤1, 0≤η≤1, F(ξ,η) satisfies the following formula: The construction process of the linear transfinite interpolation function of the area mapping of the three-dimensional space is as follows: Construct a linear interpolant between the faces u(0,η,ζ) and u(1,η,ζ) about the coordinate ξ: L ξ =(1-ξ)u(0,η,ζ)+ξu(1,η,ζ), Construct a linear interpolant between the boundaries u(ξ,0,ζ) and u(ξ,1,ζ) in the direction of coordinate η: L η =(1-η)u(ξ,0,ζ)+ηu(ξ,1,ζ), Construct a linear interpolant between the boundaries u(ξ,η,0) and u(ξ,η,1) in the direction of coordinate ζ: L ζ =(1-ζ)u(ξ,η,0)+ζu(ξ,η,1), Construct a bilinear interpolation between the boundaries u(0,0,ζ), u(0,1,ζ), u(1,0,ζ), and u(1,1,ζ) in the directions of coordinates ξ and η: Construct a bilinear interpolation between the boundaries u(ξ,0,0), u(ξ,0,1), u(ξ,1,0), and u(ξ,1,1) in the coordinates η and ζ directions: Construct a bilinear interpolation between the boundaries u(0,η,0), u(0,η,1), u(1,η,0), and u(1,η,1) in the directions of coordinates ζ and ξ: Construct a trilinear interpolation based on the eight corner points of the cube u(0,0,0), u(0,0,1), u(0,1,0), u(0,1,1), u(1,0,0), u(1,0,1), u(1,1,0), u(1,1,1): L ξηζ =(1-ξ)[(1-η)[(1-ζ)u(0,0,0)+ζu(0,0,1)] +η[(1-ζ)u(0,1,0)+ζu(0,1,1)] +ξ[(1-η)[(1-ζ)u(1,0,0)+ζu(1,0,1)] +η[(1-ζ)u(1,1,0)+ζu(1,1,1)]] (8) The transfinite interpolation function of the hexahedron is: Where, 0≤ξ≤1, 0≤η≤1, 0≤ζ≤1, and the linear construction of F(ξ,η,ζ) satisfies the following equation:

2. The aero-engine blade flow field calculation platform based on automatic grid generation technology according to claim 1 is characterized in that: The automatic grid generation method is a transfinite interpolation method of a mapping function method.

3. The aero-engine blade flow field calculation platform based on grid automatic generation technology according to claim 1 is characterized in that: S2 also includes S26, which evaluates the quality of the hexahedral units generated by the grid, and determines the degree of deformity of the hexahedral units by comparing the difference in the micro-element volume magnification coefficient at each integration point through the unit determinant.

4. The aero-engine blade flow field calculation platform based on grid automatic generation technology according to claim 3 is characterized in that: The construction process of the unit determinant of the hexahedron is: Construct a mapping function using trilinear interpolation: In the formula, For the transformation of three-dimensional space, the Jacobian determinant J is expressed as: The Jacobian determinant represents the ratio of the n-dimensional volume before and after the transformation, n = 3: dxdydz=Jdξdηdζ。 5. The aero-engine blade flow field calculation platform based on automatic grid generation technology according to claim 1 is characterized in that: The aircraft engine blades include stator blades and rotor blades.

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