Method of applying mesh deformation of inverse distance weighting function interpolation to turbine modelling
Through the mesh deformation method of inverse distance weighting function interpolation, the problems of high time cost and poor control in turbine blade design are solved, and rapid optimization and efficient turbine blade geometric shape adjustment are achieved, reducing shock loss.
Patent Information
- Application Number
- CN202510321141.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-18
- Publication Date
- 2025-07-08
AI Technical Summary
In the aerodynamic optimization design of turbine blades of aero engines, traditional methods are time-consuming and inefficient, and the controllability of turbine geometry changes is poor, resulting in shock loss and waste of computing resources.
The grid deformation method using inverse distance weighting function interpolation is adopted to establish the mapping relationship between control points and grid nodes, and the geometry of turbine blades is quickly optimized and adjusted, reducing the grid reconstruction time and improving design efficiency.
The rapid optimization of turbine blade geometry is achieved, which reduces time cost, improves design efficiency, ensures the smoothness of the geometric model and reduces shock loss.
Smart Images
Figure CN120277802A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of aero-engine turbine profile optimization design, and particularly to a method and device for applying mesh deformation by inverse distance weighted function interpolation to turbine profiling. Background Art
[0002] In the aerodynamic optimization design and numerical simulation of aero-engine turbine blades, it is necessary to continuously adjust and optimize the given geometric shape of the turbine blade to achieve better aerodynamic performance. As the geometric shape of the turbine changes, the computational grid corresponding to the geometric model also needs to be regenerated. And in the process of batch adjustment and optimization, regenerating the computational grid is very time-consuming. In fact, most of the current aerodynamic optimizations of turbine blades are local adjustments on the original geometric shape, and the original basic shape of the turbine blade does not change significantly. Compared with the original grid, the regenerated computational grid only has differences in local areas. Such batch regeneration of grids also causes serious waste of computing resources. At the same time, for the traditional manual operation of 3D modeling software to change the geometric shape of the turbine blade, the randomness of shape change is relatively large, the control of the size and degree of deformation is poor, and the fine degree of local details of the shape is poor. And the smoothness of the blade geometric model cannot be well guaranteed. To a certain extent, this will cause shock waves and shock losses in the transonic flow of the turbine blade, seriously affecting the efficiency of the aero-engine. Summary of the Invention
[0003] This application aims to solve at least one of the technical problems in the related art to some extent.
[0004] To this end, the first object of this application is to propose a method for applying mesh deformation by inverse distance weighted function interpolation to turbine profiling, which solves the problems of high time cost, poor control of turbine geometric shape change, and poor fine degree of local details of geometric shape in the traditional turbine blade optimization design method. By applying the mesh deformation method based on inverse distance weighted function interpolation to the turbine blade profiling, it realizes the rapid optimization adjustment of the turbine blade geometric shape, saves time cost, and improves the efficiency of turbine profiling optimization design.
[0005] The second object of this application is to propose a computer device.
[0006] The third object of this application is to propose a non-transitory computer-readable storage medium.
[0007] To achieve the above object, an embodiment of the first aspect of the present application proposes a method of applying grid deformation by inverse distance weighted function interpolation to turbine modeling, including: for a turbine blade to be optimized, obtaining three-dimensional coordinate data of grid nodes of its geometric model; arranging deformation control points according to the grid node coordinates of the turbine blade; changing the positions of the control points according to the geometric deformation requirements of the turbine blade, and calculating the position change amount of the control points; using inverse distance weighted function interpolation to establish a mapping relationship between the spatial position change of the control points and the spatial position change of the grid nodes of the turbine blade; inputting the three-dimensional coordinate data of the grid nodes into the established mapping relationship, and combining the position change amount of the control points to obtain the position change of the grid nodes; calculating the coordinate positions of the grid nodes after the change according to the position change of the grid nodes, and reconstructing the geometric model of the deformed turbine blade.
[0008] To achieve the above object, an embodiment of the second aspect of the present invention proposes a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method of applying grid deformation by inverse distance weighted function interpolation to turbine modeling as described above.
[0009] To achieve the above object, an embodiment of the third aspect of the present invention proposes a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by a processor, they can execute the method of applying grid deformation by inverse distance weighted function interpolation to turbine modeling.
[0010] The method and device for applying grid deformation by inverse distance weighted function interpolation to turbine modeling according to the embodiments of the present application introduce the grid deformation method based on inverse distance weighted function interpolation into the turbine blade modeling design. By using the grid deformation method based on inverse distance weighted function interpolation to establish a mapping relationship between the position change of the control points and the position change of the grid nodes or geometric vertices of the turbine blade, it is possible to realize the control of the grid nodes or geometric vertices of the blade by the control points and ensure the smoothness of the geometric model. Thus, the shape of the turbine blade can be changed by setting control points and adjusting their positions. This embodiment can not only achieve the overall deformation of the blade, but also achieve fine adjustment of the local part of the blade, only by arranging an appropriate number and position of control points according to actual needs. This embodiment does not require the connection relationship between grid nodes, is also independent of the grid type, has simple and efficient calculations without solving complex equations, and only changes the position coordinates of grid nodes during the entire blade deformation process without changing the topological connection relationship between nodes. Therefore, it can greatly reduce the time consumed for regenerating the grid and can also cooperate with numerical simulation calculation software to realize the automatic optimization design of turbine blades.
[0011] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings
[0012] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:
[0013] Figure 1 is a schematic flowchart of a method for applying grid deformation by inverse distance weighted function interpolation to turbine modeling provided in Embodiment 1 of the present application;
[0014] Figure 2 is a comparison diagram before and after deformation of the grid nodes on the turbine blade surface processed by the grid deformation method based on inverse distance weighted function interpolation in Embodiment of the present application;
[0015] Figure 3 is a comparison diagram before and after of the turbine blade model processed by the grid deformation method based on inverse distance weighted function interpolation in Embodiment of the present application. Detailed Description of the Embodiments
[0016] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.
[0017] A method and apparatus for applying grid deformation by inverse distance weighted function interpolation to turbine modeling according to embodiments of the present application will be described below with reference to the drawings.
[0018] Figure 1 is a schematic flowchart of a method for applying grid deformation by inverse distance weighted function interpolation to turbine modeling provided in Embodiment 1 of the present application.
[0019] As Figure 1 shown, the method for applying grid deformation by inverse distance weighted function interpolation to turbine modeling includes the following steps:
[0020] Step 101, for the turbine blade to be optimized, obtain the three-dimensional coordinate data of the grid nodes of its geometric model;
[0021] Specifically, in this embodiment, for the turbine blade to be optimized, its surface grid node coordinate data is used for its geometric deformation processing. Therefore, when optimizing the turbine blade, it is necessary to first obtain the geometric vertex coordinate data of the turbine blade.
[0022] When obtaining the geometric vertex coordinate data of the turbine blade, the blade model can be directly converted into the STL file format using 3D modeling software. The STL format file describes the surface geometry of a three-dimensional object in the form of a triangular mesh, with a simple data structure that is convenient for processing and conversion. Then, the 3D data visualization and analysis processing Python extension library - PyVista is used to process the file, and the coordinate data of the turbine blade mesh nodes is obtained through the function (pyvista.read) in the extension library to read the mesh node information.
[0023] Step 102: Arrange deformation control points according to the mesh node coordinates of the turbine blade.
[0024] Specifically, in this embodiment, for the geometric shape optimization and adjustment requirements of the turbine blade, the specific position or approximate range of the geometric adjustment of the turbine blade is determined. If the shape of the turbine blade is to be adjusted as a whole, the number of control points can be less, and the control points can be connected together by line segments to form a cuboid to enclose the blade geometry therein; if detailed adjustment is to be made to a local position of the blade, the number of control points can be more, and the control point positions can be arranged near the geometric boundary positions. For example, assuming that the geometric shape of the turbine blade is to be adjusted and optimized as a whole, in the arrangement of the control point positions, a cuboid formed by connecting 12 evenly distributed control points is used to enclose the blade geometry therein.
[0025] Step 103: Change the positions of the control points according to the geometric deformation requirements of the turbine blade, and calculate the position change amount of the control points.
[0026] Specifically, in this embodiment, for the overall or local deformation area of the turbine blade, the reasonable range of the change in the control point positions needs to be determined. If the change in the control point positions is too small, the change in the turbine shape is subtle and it is difficult to achieve the purpose of the change; if the change in the control point positions is too large, the turbine shape deviates from the original basic geometric model and loses its physical meaning. The trailing edge radius of the blade can be used as the basic dimension for reference of the control point position change amount.
[0027] Specifically, in this embodiment, the change amount of the control point positions is calculated, and the formula is:
[0028]
[0029] where (x n , y n , z n ) are the coordinates of the control point after movement, and (x0, y0, z0) are the original coordinates of the control point.
[0030] Step 104: Use the inverse distance weighted function interpolation to establish a mapping relationship between the spatial position change of the control points and the spatial position change of the turbine blade mesh nodes.
[0031] Specifically, in this embodiment, the establishment process of the mapping relationship includes:
[0032] According to the Euclidean distance between the control points and the grid nodes, calculate the weight of the influence of the position change of each control point on the position change of the grid nodes. The weight is proportional to the inverse distance, and the influence of the distance on the weight is mainly adjusted by the power parameter of the inverse distance. When the power parameter is at a relatively high value, the influence of the control point closest to the grid node on it can be emphasized. When the power parameter is at a relatively low value, the influence of the control points farther from the grid node on the grid node becomes larger. The selection of the power parameter seriously affects the deformation effect and smoothness of the blade. Generally, a better deformation effect can be obtained within the range of 0.5 to 3. In the example, the power parameter adopts the default value of 2;
[0033] For a specific grid node, the weight formula between it and n control points is:
[0034]
[0035] where (x, y, z) are the coordinates of the grid node, the subscript i represents the i-th control point, (x i , y i , z i ) are the coordinates of the i-th control point, L i is the distance between the grid node and the i-th control point, and p is the inverse distance power parameter;
[0036] Using the weights corresponding to each deformed grid node and the control points, perform weighted averaging on the position changes of its control points to obtain the interpolation result of the position changes of the grid nodes;
[0037] For a specific grid node, its coordinate position change formula is:
[0038]
[0039] For m grid nodes and n control points arranged, the entire mapping relationship expression is:
[0040]
[0041] Step 105, input the three-dimensional coordinate data of the grid nodes into the established mapping relationship, and combine the position change amount of the control points to obtain the position change of the grid nodes;
[0042] Specifically, in this embodiment, convert the original grid node coordinates into a multi-dimensional array, input them into the established mapping relationship, and through operations combining the original coordinate positions and coordinate position changes of the control points, the coordinate changes of each grid node can be obtained.
[0043] Step 106: Calculate the coordinate positions of the grid nodes after the change based on the change in the grid node positions, and reconstruct the geometric model of the deformed turbine blade.
[0044] Specifically, in this embodiment, according to the change in the coordinate positions of the grid nodes, the coordinate data of the deformed grid nodes is calculated. The formula is as follows:
[0045]
[0046] where (x ′ m , y ′ m , z ′ m ) are the coordinate positions of the deformed grid nodes;
[0047] When reconstructing the deformed geometric model, use the save function of the Pyvista library in Python to save the deformed grid node data as a new 3D model STL format file.
[0048] The method and device for applying the grid deformation by inverse distance weighted function interpolation to turbine modeling in the embodiments of the present application introduce the grid deformation method based on inverse distance weighted function interpolation into the turbine blade modeling design. Through the grid deformation method based on inverse distance weighted function interpolation, a mapping relationship between the change in the control point position and the change in the grid node or geometric vertex position of the turbine blade is established, which can realize the control of the grid nodes or geometric vertices of the blade by the control points and ensure the smoothness of the geometric model. Therefore, the shape of the turbine blade can be changed by setting the control points and adjusting the positions of the control points. This embodiment can not only realize the overall deformation of the blade, but also realize the fine adjustment of the local part of the blade, and only need to arrange an appropriate number and position of control points according to actual needs. This embodiment does not require the connection relationship between grid nodes, is also independent of the grid type, has simple and efficient calculations and does not require solving complex equations. During the entire blade deformation process, only the position coordinates of the grid nodes are changed, and the topological connection relationship between the nodes is not changed. Therefore, it can greatly reduce the time consumed for regenerating the grid and can also cooperate with numerical simulation calculation software to realize the automatic optimization design of the turbine blade.
[0049] Figure 2 This is a comparison diagram before and after the deformation of the grid nodes on the turbine blade surface processed by the grid deformation method based on inverse distance weighted function interpolation in this embodiment. Figure 2 On the left side is before deformation, and on the right side is after deformation.
[0050] Figure 3 This is a comparison diagram before and after the turbine blade model processed by the grid deformation method based on inverse distance weighted function interpolation in this embodiment. Figure 3On the left side is before deformation, and on the right side is after deformation.
[0051] To implement the above embodiments, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the methods described in the above embodiments are implemented.
[0052] To implement the above embodiments, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the methods described in the above embodiments are implemented.
[0053] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.
[0054] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.
[0055] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present application.
[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.
[0057] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.
[0058] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.
[0059] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically separately for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0060] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.
Claims
1. A method for applying grid deformation by inverse distance weighted function interpolation to turbine modeling, characterized in that, Including: For the turbine blade to be optimized, obtain the three-dimensional coordinate data of the grid nodes of its geometric model; Arrange deformation control points according to the grid node coordinates of the turbine blade; Change the positions of the control points according to the geometric deformation requirements of the turbine blade, and calculate the position change amount of the control points; Use the inverse distance weighted function interpolation to establish the mapping relationship between the spatial position change of the control points and the spatial position change of the grid nodes of the turbine blade; Input the three-dimensional coordinate data of the grid nodes into the established mapping relationship, and combine with the position change amount of the control points to obtain the position change of the grid nodes; Calculate the coordinate positions of the grid nodes after the change according to the position change of the grid nodes, and reconstruct the geometric model of the deformed turbine blade.
2. The method according to claim 1, wherein Obtain the three-dimensional coordinate data of the grid nodes of the geometric model of the turbine blade to be optimized, including: Use a 3D model to convert the blade geometric model into STL format data, and the STL format data describes the surface geometry of a three-dimensional object in the form of triangular meshes; Use mesh generation software to perform mesh generation, read the grid node information, and obtain the three-dimensional coordinate data of the turbine blade grid nodes.
3. The method according to claim 1, characterized in that Before changing the positions of the control points according to the geometric deformation requirements of the turbine blade, it also includes: Set the range of the change in the position of the control points; Take the trailing edge radius of the blade as the basic dimension for reference of the position change amount of the control points; Calculate the position change amount of the control points, expressed as: Among them, (x n , y n , z n ) are the coordinates after the position of the control point is moved, and (x0, y0, z0) are the original coordinates of the control point.
4. The method according to claim 1, wherein Use the inverse distance weighted function interpolation to establish the mapping relationship between the spatial position change of the control points and the spatial position change of the grid nodes of the turbine blade, including: According to the Euclidean distance between the control points and the grid nodes, calculate the weight of the influence of the position change of each control point on the position change of the grid nodes. When calculating, set the weight to be inversely proportional to the inverse distance, and adjust the influence of the distance on the weight through the power parameter of the inverse distance; For each grid node, use the weights corresponding to each control point to perform weighted averaging on the position changes of each control point to obtain the interpolation result of the position change of the grid node; Establish a mapping relationship based on the distance between the grid nodes and the control points, the weights corresponding to the distances, the position changes of the control points, and the position changes of the grid nodes.
5. The method according to claim 4, characterized in that, For a specific grid node, the weight formula between it and n control points is: Among them, (x, y, z) are the coordinates of the grid nodes, and the subscript i represents the i-th control point. (x i , y i , z i ) are the coordinates of the i-th control point, L i is the distance between the grid node and the i-th control point, and p is the inverse distance power parameter; For a specific grid node, its coordinate position change formula is: where, Δh i is the position change of the i-th control point; For m grid nodes, arrange n control points, and the entire mapping relationship expression is:
6. The method according to claim 1, characterized in that, Input the three-dimensional coordinate data of the grid nodes into the established mapping relationship, and combine with the position change amount of the control points to obtain the position change of the grid nodes, including: Convert the three-dimensional coordinates of the grid nodes into a multi-dimensional array, input it into the established mapping relationship, combine with the position change amount of the control points, and obtain the coordinate changes of each grid node through operations.
7. The method according to claim 1, wherein Calculate the coordinate positions of the grid nodes after the change according to the position change of the grid nodes, expressed as: Among them, (x ′ m , y ′ m , z ′ m ) is the coordinate position of the deformed grid node; Reconstruct the geometric model of the deformed turbine blade, including: Save the changed grid node coordinate data as STL format data of a 3D model.
8. A computer device, characterized in that, Comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, when the processor executes the computer program, the method described in any one of claims 1-7 is implemented.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the method described in any one of claims 1-7 is implemented.