Turbine blade optimization method for grid deformation based on radial basis function interpolation

Abstract

The invention provides a turbine blade optimization method for grid deformation based on radial basis function interpolation, and the method comprises the steps: carrying out the grid division of a to-be-optimized turbine blade, and obtaining the coordinate data of a grid node; according to the modeling optimization requirement of the turbine blade, the number and the positions of a plurality of control points arranged on the turbine blade are determined; selecting a plurality of target control points, changing the coordinate position of each target control point, and calculating the space position difference value of the control points; constructing a radial basis function interpolation equation set by using the spatial position difference, and solving a mapping relation between the position change of the control point and the position change of the grid node; and inputting the coordinate data of the grid nodes into the mapping relation, and reestablishing the three-dimensional model of the turbine blade by using the obtained deformed grid node information. According to the method, a grid deformation method based on radial basis function interpolation is introduced into turbine blade modeling design, the turbine blade optimization efficiency is improved, and the time cost is reduced.

Description

Technical Field

[0001] This application relates to the technical field of aero-engine turbine profile optimization, and particularly to a turbine blade optimization method based on radial basis function interpolation for grid deformation. Background Art

[0002] Currently, turbine blades are important components for energy conversion in turbines of aero-engines and gas turbines. In aero-engines and gas turbines, the profile design of turbine blades directly affects the flow of air between the blades, and the flow of air between the blades seriously affects the operating efficiency of the engine itself. Therefore, in order to improve the working efficiency of aero-engines and gas turbines, the aerodynamic performance of the blades can be improved by changing the shape of the turbine blade model. A reasonable blade profile can optimize the internal flow of the engine, and the air flow can pass through the turbine more efficiently, thereby improving the work efficiency of the turbine.

[0003] However, in practical applications, it is difficult to form the design of turbine blades in one go. It is often necessary to continuously adjust and optimize on the existing basic blade profile under the guidance of engineering practice and relevant flow control theories to design a series of blades. Then, by means of numerical simulation, the aerodynamic performance-related parameters of each designed blade are calculated. Then, the blade profile design with the best aerodynamic performance is selected from among the relevant aerodynamic performance parameters to achieve the purpose of optimizing the design of turbine blades.

[0004] In the related art, when adjusting and optimizing the profile design of turbine blades, the turbine blades are usually changed by manually operating 3D modeling software. However, this optimization method not only takes a lot of time, but also the smoothness of the model cannot be guaranteed during batch operations. Moreover, due to the minor changes in the local part of the blade, when performing numerical simulation calculations later, it is still necessary to divide the grid for the redesigned turbine blade, resulting in a great increase in the preprocessing time cost.

[0005] Therefore, how to optimize the design of turbine blades with high efficiency and high smoothness has become an urgent problem to be solved at present. Summary of the Invention

[0006] This application aims to solve at least one of the technical problems in the related art to some extent.

[0007] To this end, the first object of this application is to propose a turbine blade optimization method based on radial basis function interpolation for grid deformation. This method introduces the grid deformation method based on radial basis function interpolation into the turbine blade profile design, improves the efficiency of turbine blade optimization, reduces the time cost, and solves the problems such as high time cost of the turbine blade optimization adjustment method, poor smoothness of the turbine blade, and the need for re-meshing.

[0008] The second object of the present application is to propose a turbine blade optimization system based on radial basis function interpolation for grid deformation.

[0009] The third object of the present application is to propose a non-transitory computer-readable storage medium.

[0010] To achieve the above object, the first aspect of the present application is to propose a turbine blade optimization method based on radial basis function interpolation for grid deformation, including the following steps:

[0011] Perform grid division processing on the turbine blade to be optimized, and obtain the coordinate data of the grid nodes of the turbine blade;

[0012] According to the shape optimization requirements of the turbine blade, determine the number and positions of a plurality of control points arranged on the turbine blade;

[0013] Select a plurality of target control points for blade adjustment from the plurality of control points, change the coordinate positions of each of the target control points, and calculate the spatial position difference of each of the target control points;

[0014] Use the spatial position differences to construct a radial basis function interpolation equation set, and solve the radial basis function interpolation equation set to obtain the mapping relationship between the change in the control point position and the change in the grid node position;

[0015] Input the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information, and use the deformed grid node information to re-establish the three-dimensional model of the turbine blade.

[0016] Optionally, in an embodiment of the present application, the obtaining the coordinate data of the grid nodes of the turbine blade includes: when the number of grid nodes is greater than a number threshold, calling an open-source library in a computer programming language for 3D data visualization and analysis processing; converting the turbine blade model file after grid division processing into a file type recognizable by the open-source library, and reading the coordinate data of the grid nodes in the converted turbine blade model file through the grid node information reading function in the open-source library.

[0017] Optionally, in an embodiment of the present application, determining the number and positions of a plurality of control points arranged on the turbine blade according to the styling optimization requirements of the turbine blade includes: determining the range to be adjusted of the turbine blade, and when adjusting the overall shape of the turbine blade, arranging a plurality of control points outside the turbine blade so as to enclose the turbine blade within a polyhedron formed by the plurality of control points; when adjusting a local position of the turbine blade, arranging a plurality of control points at the boundary vertices of the local position, wherein the number of control points arranged when adjusting the local position is greater than the number of control points arranged when adjusting the overall shape.

[0018] Optionally, in an embodiment of the present application, changing the coordinate positions of each of the target control points and calculating the spatial position difference of each of the target control points includes: determining a reasonable change range of the overall shape and the local position; within the corresponding reasonable change range, changing the coordinate positions of each of the target control points and calculating the difference between the spatial position coordinates after the position change of each of the target control points and the initial spatial position coordinates.

[0019] Optionally, in an embodiment of the present application, constructing a radial basis function interpolation equation system using the spatial position differences includes: taking each of the target control points as an interpolation point and taking the spatial position difference of each of the target control points as an interpolation function in a basic form of radial basis function interpolation; based on the interpolation result of each interpolation point being equal to the corresponding spatial position difference, constructing the to-be-solved radial basis function interpolation equation system, wherein the radial basis function interpolation equation system includes weight coefficients corresponding to each of the to-be-solved target control points.

[0020] Optionally, in an embodiment of the present application, solving the radial basis function interpolation equation system includes: selecting a radial basis kernel function and an action radius of the radial basis kernel function; using the radial basis kernel function and the action radius to solve the radial basis function interpolation equation system and calculating the weight coefficients corresponding to each of the target control points; establishing a mapping relationship between the change in the control point position and the change in the grid node position according to the weight coefficients corresponding to each of the target control points.

[0021] Optionally, in an embodiment of the present application, the step of inputting the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information includes: converting the coordinate data of the grid nodes read by the open-source library into an array form; batch-inputting the numerical coordinate data into the mapping relationship to obtain the three-dimensional coordinate change values of each grid node; and combining the original coordinate data of each grid node and the three-dimensional coordinate change values to obtain the deformed grid node information of each grid node.

[0022] To achieve the above object, a second aspect of the present application further provides a turbine blade optimization system for grid deformation based on radial basis function interpolation, including the following modules:

[0023] An acquisition module, configured to perform grid division processing on a turbine blade to be optimized and acquire the coordinate data of the grid nodes of the turbine blade;

[0024] A determination module, configured to determine the number and positions of a plurality of control points arranged on the turbine blade according to the shaping optimization requirements of the turbine blade;

[0025] A change module, configured to select a plurality of target control points for blade adjustment from the plurality of control points, change the coordinate positions of each target control point, and calculate the spatial position difference of each target control point;

[0026] A solution module, configured to construct a radial basis function interpolation equation set by using the spatial position differences and solve the radial basis function interpolation equation set to obtain the mapping relationship between the change in the control point position and the change in the grid node position;

[0027] A reconstruction module, configured to input the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information, and use the deformed grid node information to re-establish the three-dimensional model of the turbine blade.

[0028] To implement the above embodiments, a third aspect of the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for optimizing a turbine blade for grid deformation based on radial basis function interpolation in the first aspect above.

[0029] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects: The present application introduces a grid deformation method based on radial basis function interpolation into the turbine blade modeling design. By establishing the mapping relationship between the control points and the grid nodes of the turbine blade, it is possible to realize the control of the grid nodes of the blade by the control points, so that the change of the turbine blade can be realized by setting the control points and adjusting the control points. The present application can not only realize the overall adjustment of the blade, but also realize the fine adjustment of the local part of the blade, and only change the position information of the grid nodes during the whole blade deformation process, without changing the topological connection relationship between the network nodes. Therefore, the optimization process of the present application has the advantages of strong deformation ability, avoiding re-meshing, and smooth transition. Thus, the present application can realize the rapid optimization and adjustment of the turbine blade modeling design, simplify the optimization process, reduce the time cost required for the optimization design, improve the efficiency of the optimization design, and ensure the smoothness of the turbine blade.

[0030] Additional aspects and advantages of the present invention 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 invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above 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 accompanying drawings, where:

[0032] Figure 1 is a flowchart of a turbine blade optimization method based on radial basis function interpolation for grid deformation proposed by an embodiment of the present application;

[0033] Figure 2 is a schematic diagram of some grid nodes of a turbine blade before deformation proposed by an embodiment of the present application;

[0034] Figure 3 is a schematic diagram of some grid nodes of a turbine blade after deformation proposed by an embodiment of the present application;

[0035] Figure 4 is a schematic diagram of a turbine blade model before grid deformation proposed by an embodiment of the present application;

[0036] Figure 5 is a schematic diagram of a turbine blade model after grid deformation proposed by an embodiment of the present application;

[0037] Figure 6 is a schematic diagram of the structure of a turbine blade optimization system based on radial basis function interpolation for grid deformation proposed by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0039] A turbine blade optimization method and system based on radial basis function interpolation for grid deformation proposed in an embodiment of the present application will be described below with reference to the accompanying drawings.

[0040] Figure 1 The flowchart of a turbine blade optimization method based on radial basis function interpolation for grid deformation proposed in an embodiment of the present application is shown in Figure 1 As shown, the method includes the following steps:

[0041] Step S101, perform grid division processing on the turbine blade to be optimized, and obtain the coordinate data of the grid nodes of the turbine blade.

[0042] Specifically, for a given turbine blade to be optimized, first perform simple grid division processing on it. As a possible implementation, an existing grid division application software can be directly used to perform grid division on the turbine blade. A structured grid division method or an unstructured grid division method can be adopted, and the specific grid division method can be determined according to actual needs.

[0043] Furthermore, obtain the coordinate data of the grid nodes generated after the turbine blade is subjected to grid division. As a possible implementation, when the number of grid nodes is small, the grid division software can be used to directly obtain the grid node information, while when the number of grid nodes is large, the grid node information of the turbine blade can be obtained in batches with the help of a computer programming language.

[0044] In an embodiment of the present application, obtaining the coordinate data of the grid nodes of the turbine blade includes: when the number of grid nodes is greater than the number threshold, calling an open-source library for 3D data visualization and analysis processing in a computer programming language; converting the turbine blade model file after grid division processing into a file type recognizable by the open-source library, and reading the coordinate data of the grid nodes in the converted turbine blade model file through the grid node information reading function in the open-source library.

[0045] Specifically, in this embodiment, the quantity threshold is used to distinguish whether the number of grid nodes meets the requirement for batch obtaining of grid node information. When the number of grid nodes is greater than the quantity threshold, it indicates that the number of grid nodes is relatively large. In this embodiment, the open-source library PyVista in the computer programming language Python, which is used for 3D data visualization and analysis processing, is adopted to batch obtain the coordinate data of the grid nodes of the turbine blade. Specifically, when obtaining, first convert the turbine blade model file with the grid divided into a file type recognizable by the PyVista library, such as file formats like STL and OBJ, and then use the function in the PyVista library to read the grid node information (for example, pyvista.read) to read the coordinate data in the grid nodes and save it.

[0046] Step S102: Determine the number and positions of multiple control points arranged on the turbine blade according to the shaping optimization requirements of the turbine blade.

[0047] Specifically, according to the actual shaping design optimization requirements, arrange an appropriate number of control points at corresponding positions. Among them, the shaping optimization requirements can be the areas to be optimized of the blade determined according to the actual turbine blade design requirements, etc.

[0048] In an embodiment of the present application, determining the number and positions of multiple control points arranged on the turbine blade according to the shaping optimization requirements of the turbine blade includes: determining the range to be adjusted of the turbine blade. When adjusting the overall shape of the turbine blade, arrange multiple control points outside the turbine blade so that the turbine blade is included in the polyhedron formed by the multiple control points; when adjusting the local position of the turbine blade, arrange multiple control points at the boundary vertices of the local position. Among them, the number of control points arranged when adjusting the local position is greater than the number of control points arranged when adjusting the overall shape.

[0049] Specifically, in this embodiment, first determine the range to be optimized and adjusted for the turbine blade according to actual requirements. This range can be the area of the entire turbine blade or a certain local position in the turbine blade. If it is necessary to adjust the shape of the turbine blade as a whole, the number of control points can be set relatively small to reduce the computing resources required for optimization, and the positions of each control point can be arranged outside the turbine blade. If it is necessary to adjust the local position of the turbine blade, the number of control points needs to be as large as possible to ensure the optimization accuracy and the accuracy of the optimization result, and the positions of each control point can be arranged at the boundary vertices of the turbine blade contour. Therefore, the number of control points arranged when adjusting the local position is greater than the number of control points arranged when adjusting the overall shape.

[0050] For example, if it is necessary to change the shape of the turbine blade as a whole, then as Figure 2As shown, the number of selected control points can be 16, and the polyhedron formed by these 16 control points contains the turbine blade within the polyhedron. It should be noted that Figure 2 Only some of the grid nodes are shown. For ease of distinction, Figure 2 different colors are used to represent the control points and the grid nodes.

[0051] Step S103: Select multiple target control points for blade adjustment from the multiple control points, change the coordinate positions of each target control point, and calculate the spatial position difference of each target control point.

[0052] Specifically, change the coordinate positions of the selected target control points and calculate the spatial position differences of each target control point.

[0053] In an embodiment of the present application, changing the coordinate positions of each target control point and calculating the spatial position difference of each target control point includes: determining a reasonable change range for the overall shape and local position; within the corresponding reasonable change range, change the coordinate positions of each target control point, and calculate the difference between the spatial position coordinates after the position change of each target control point and the initial spatial position coordinates.

[0054] Specifically, in this embodiment, the reasonable change ranges for optimizing the overall shape and local position of the blade can be determined respectively by combining actual engineering experience and flow control theory. Then, according to the adjustment method determined according to the actual modeling optimization requirements in step S102, within the reasonable change range corresponding to adjusting the overall shape or local position, change the positions of all or part of the control points (i.e., the coordinate positions of the target control points). Then calculate the difference between the spatial position coordinates after the position change of each target control point and the initial spatial position coordinates before the change.

[0055] Continuing to refer to Figure 2 the example shown, in this example, when adjusting the overall shape of the blade, 2 target control points are selected from 16 control points. Again, as Figure 3 shown, indirectly change the overall shape of the turbine blade by changing the positions of these 2 control points. The difference in spatial position can be calculated by subtracting the initial spatial position coordinates of each target control point from the spatial position coordinates after the position change of each target control point in the three-dimensional spatial position coordinates in the coordinate system shown in Figure 3 .

[0056] Step S104: Use the spatial position differences to construct a radial basis function interpolation equation system and solve the radial basis function interpolation equation system to obtain the mapping relationship between the change in the control point position and the change in the grid node position.

[0057] Specifically, by using radial basis function interpolation, a mapping relationship between the change in the position of the control points and the change in the position of the grid nodes is established.

[0058] It should be noted that for N control points in this application, the basic form of radial basis function interpolation is shown in the following formula:

[0059]

[0060] where F(r) is the interpolation function, φ(||r - r i ||) is the general form of the radial basis function, ||r - r i || is the Euclidean distance between two control points, r i is the position of the i-th control point, and N is the number of control points.

[0061] In an embodiment of this application, a radial basis function interpolation equation system is constructed by using spatial position differences, including: taking each target control point as an interpolation point, and taking the spatial position difference of each target control point as the interpolation function in the basic form of radial basis function interpolation; based on the interpolation result of each interpolation point being equal to the corresponding spatial position difference, a radial basis function interpolation equation system to be solved is constructed, where the radial basis function interpolation equation system includes the weight coefficients corresponding to each target control point to be solved.

[0062] Specifically, when solving the radial basis function interpolation in this embodiment, taking the control points in this application as the interpolation points of the radial basis function interpolation, then the difference function F(r) in the above basic form of radial basis function interpolation is the change amount of the control position of the control points in this application, that is, the spatial position difference calculated above. And a radial basis function φ(||r - r i ||) can be given, and according to the positions of the control points arranged above, r i is known, then the only unknown in the above basic form of radial basis function interpolation is the coefficient w i corresponding to each interpolation point (control point).

[0063] Furthermore, the solution calculation can be carried out by using the condition that the interpolation result of each interpolation point must be the same as its known displacement amount, and thus the following radial basis function interpolation equation system shown in the formula can be obtained:

[0064] ΔX m = ΦW x

[0065] ΔY m = ΦW y

[0066] ΔZ m = ΦW z

[0067] Among them, m represents the selected target control point, and the components of the displacements of the N control points in the three directions of x, y, and z can be expressed by the following formulas:

[0068] ΔX m =[Δx (m,1) ,Δx (m,2) ,…,Δx (m,N) T

[0069] ΔY m =[Δy (m,1) ,Δy (m,2) ,…,Δy (m,N) T

[0070] ΔZ m =[Δz (m,1) ,Δz (m,2) ,…,Δz (m,N) T

[0071] Among them, the weight coefficients corresponding to each target control point to be solved in the radial basis function interpolation equation system can be expressed by the following formulas:

[0072] W x =[w (m,1) ,w (m,2) ,…,w (m,N) T

[0073] W y =[w (m,1) ,w (m,2) ,…,w (m,N) T

[0074] W z =[w (m,1) ,w (m,2) ,…,w (m,N) T

[0075] The elements in the matrix Φ of the radial basis function interpolation equation system are the radial basis function values with the distance between any two control points as a parameter, and the specific form of this matrix can be expressed by the following formula:

[0076]

[0077] ​​​​​​Further, in this embodiment, solving the radial basis function interpolation equations includes: selecting a radial basis kernel function and the action radius of the radial basis kernel function; using the radial basis kernel function and the action radius to solve the radial basis function interpolation equations, and calculating the weight coefficients corresponding to each target control point; according to the weight coefficients corresponding to each target control point, establishing a mapping relationship between the change in the control point position and the change in the grid node position.

[0078] Specifically, in this embodiment, a suitable radial basis kernel function is first selected. Among them, there are various kernel functions, such as Gaussian function, linear function, and thin plate spline function, etc. For specific problems, the interpolation effects of different kernel functions are different. Therefore, in this embodiment, a suitable kernel function is selected in combination with the actual optimization requirements. Then, the action radius of the selected kernel function is chosen. For specific problems, different action radii of the kernel function will also affect the interpolation effect. Therefore, it is necessary to consider selecting a suitable action radius of the kernel function. Then, using the determined radial basis kernel function and the action radius to solve the radial basis function interpolation equations, the coefficient matrix of the mapping relationship is obtained.

[0079] For example, the Gaussian function is selected as the radial basis kernel function, and its expression is:

[0080]

[0081] In this example, the action radius of the Gaussian function is set to 1.0, so as to ensure the smooth transition between grid nodes. Furthermore, by solving the equations, the weight coefficients can be obtained as follows:

[0082] W x = Φ -1 ΔX m

[0083] W y = Φ -1 ΔY m

[0084] W z = Φ -1 ΔZ m

[0085] where Φ -1 is the invertible matrix of Φ.

[0086] Furthermore, since the weight coefficients have been solved, the mapping relationship between the control point position change and the grid node is established.

[0087] Step S105, input the coordinate data of the grid nodes into the mapping relationship, obtain the deformed grid node information, and use the deformed grid node information to re - establish the three - dimensional model of the turbine blade.

[0088] Specifically, first input the grid node coordinates obtained in step S101 into the mapping relationship established in step S104 to obtain the deformed grid node information. For example, with the aid of a computer programming language, the original grid node coordinates can be batch-input into the established mapping relationship, and then batch-output to obtain the deformed grid node information. Then, using the deformed grid node information, re-establish the three-dimensional model of the turbine blade to optimize the original model of the turbine blade to be optimized into the newly established three-dimensional model.

[0089] In an embodiment of the present application, inputting the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information includes: converting the coordinate data of the grid nodes read by the open-source library into an array form; batch-inputting the numerical coordinate data into the mapping relationship to obtain the three-dimensional coordinate change values of each grid node; and combining the original coordinate data and the three-dimensional coordinate change values of each grid node to obtain the deformed grid node information of each grid node.

[0090] Specifically, in this embodiment, use the reading function of the PyVista library in the embodiment of step S101 to first read the original grid node coordinate information, then convert the coordinate information in the form of (x, y, z) into an array form, and batch-input it into the radial basis function interpolation equation system established in S104 to calculate the x, y, and z coordinate change values of each grid node. Combining the original coordinate data, the deformed grid node coordinates can be obtained. For example, add the original coordinate data and the coordinate change values to obtain the deformed grid node coordinates.

[0091] Furthermore, when re-establishing the three-dimensional model of the turbine blade using the deformed grid node information. As a possible implementation, the writing function (pyvista.save) in Python in the computer programming language can be used to write and save the deformed grid node information as a 3D model file in STL format. And use the reading function to read the saved deformed turbine blade model file, and use the display function (mesh.plot) to display the deformed turbine blade.

[0092] As an example, for Figure 4 the shown turbine blade model, after optimization according to the turbine blade optimization method based on radial basis function interpolation of the present application, the adjusted turbine blade model is obtained as shown in Figure 5 shown.

[0093] Thus, the present application can realize the optimized design of the turbine blade by combining programming means throughout the deformation process, avoiding problems such as high time cost, poor smoothness of the turbine blade, and the need to re-divide the grid in the traditional turbine blade optimization and adjustment methods.

[0094] In summary, the turbine blade optimization method based on radial basis function interpolation for grid deformation in the embodiments of the present application introduces the grid deformation method based on radial basis function interpolation into the turbine blade modeling design. By establishing the mapping relationship between the control points and the grid nodes of the turbine blade, it can realize the control of the grid nodes of the blade by the control points, so that the change of the turbine blade can be realized by setting the control points and adjusting the control points. This method can not only realize the overall adjustment of the blade, but also realize the fine adjustment of the local part of the blade, and only change the position information of the grid nodes during the whole blade deformation process, without changing the topological connection relationship between the network nodes. Therefore, the optimization process of this method has the advantages of strong deformation ability, avoiding re-meshing and smooth transition. Thus, this method can realize the rapid optimization adjustment of the turbine blade modeling design, simplify the optimization process, reduce the time cost required for the optimization design, improve the efficiency of the optimization design, and ensure the smoothness of the turbine blade.

[0095] To implement the above embodiments, the present application also proposes a turbine blade optimization system based on radial basis function interpolation for grid deformation. Figure 6 As shown in the structural schematic diagram of a turbine blade optimization system based on radial basis function interpolation for grid deformation proposed in the embodiments of the present application, Figure 6 as shown, the system includes:

[0096] An acquisition module 100, configured to perform grid division processing on the turbine blade to be optimized and acquire the coordinate data of the grid nodes of the turbine blade.

[0097] A determination module 200, configured to determine the number and positions of a plurality of control points arranged on the turbine blade according to the shape optimization requirements of the turbine blade.

[0098] A change module 300, configured to select a plurality of target control points for blade adjustment from the plurality of control points, change the coordinate positions of each target control point, and calculate the spatial position difference of each target control point.

[0099] A solution module 400, configured to construct a radial basis function interpolation equation system by using the spatial position difference and solve the radial basis function interpolation equation system to obtain the mapping relationship between the change of the control point position and the change of the grid node position.

[0100] A reconstruction module 500, configured to input the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information, and use the deformed grid node information to re-establish the three-dimensional model of the turbine blade.

[0101] Optionally, in an embodiment of the present application, the obtaining module 100 is specifically configured to: when the number of grid nodes is greater than the number threshold, call an open-source library for 3D data visualization and analysis processing in a computer programming language; convert the turbine blade model file after mesh division processing into a file type recognizable by the open-source library, and read the coordinate data of the grid nodes in the converted turbine blade model file through the grid node information reading function in the open-source library.

[0102] Optionally, in an embodiment of the present application, the determining module 200 is specifically configured to: determine the range to be adjusted for the turbine blade. When adjusting the overall shape of the turbine blade, arrange a plurality of control points outside the turbine blade to enclose the turbine blade in a polyhedron formed by the plurality of control points; when adjusting the local position of the turbine blade, arrange a plurality of control points at the boundary vertices of the local position, where the number of control points arranged when adjusting the local position is greater than the number of control points arranged when adjusting the overall shape.

[0103] Optionally, in an embodiment of the present application, the changing module 300 is specifically configured to: determine the reasonable change range of the overall shape and the local position; within the corresponding reasonable change range, change the coordinate positions of each target control point, and calculate the difference between the spatial position coordinates after the position change of each target control point and the initial spatial position coordinates.

[0104] Optionally, in an embodiment of the present application, the solving module 400 is specifically configured to: use each target control point as an interpolation point, and use the spatial position difference of each target control point as the interpolation function in the basic form of radial basis function interpolation; based on the interpolation result of each interpolation point being equal to the corresponding spatial position difference, construct the radial basis function interpolation equation system to be solved, where the radial basis function interpolation equation system includes the weight coefficients corresponding to each target control point to be solved.

[0105] Optionally, in an embodiment of the present application, the solving module 400 is specifically configured to: select a radial basis kernel function and the action radius of the radial basis kernel function; solve the radial basis function interpolation equation system using the radial basis kernel function and the action radius, and calculate the weight coefficients corresponding to each target control point; establish a mapping relationship between the change in the position of the control points and the change in the position of the grid nodes according to the weight coefficients corresponding to each target control point.

[0106] Optionally, in an embodiment of the present application, the reconstructing module 500 is specifically configured to: convert the coordinate data of the grid nodes read by the open-source library into an array form; batch input the numerical coordinate data into the mapping relationship to obtain the three-dimensional coordinate change values of each grid node; combine the original coordinate data of each grid node and the three-dimensional coordinate change values to obtain the deformed grid node information of each grid node.

[0107] It should be noted that the foregoing explanation of the embodiments of the turbine blade optimization method based on radial basis function interpolation for grid deformation also applies to the system of this embodiment, and will not be repeated here.

[0108] In summary, the turbine blade optimization system based on radial basis function interpolation for grid deformation in the embodiments of the present application introduces the grid deformation method based on radial basis function interpolation into the turbine blade styling design, can achieve rapid optimization and adjustment of the turbine blade styling design, simplifies the optimization process, reduces the time cost required for the optimization design, improves the efficiency of the optimization design, and ensures the smoothness of the turbine blade.

[0109] To implement the above embodiments, the present application also proposes a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the turbine blade optimization method based on radial basis function interpolation for grid deformation as described in any one of the embodiments in the first aspect above.

[0110] 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 representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0111] 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 and clearly defined.

[0112] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations, where functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0113] Logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. 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 connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (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 medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0114] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0115] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and when the program is executed, it includes one or a combination of the steps of the method embodiment.

[0116] In addition, in each of the embodiments of the present application, the functional units can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0117] The above-mentioned storage medium can be a read-only memory, a magnetic disk, 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 turbine blade optimization method based on radial basis function interpolation for mesh deformation, characterized in that: The following steps are involved: Performing meshing processing on the turbine blade to be optimized, and obtaining coordinate data of the mesh nodes of the turbine blade; Determining the number and positions of a plurality of control points arranged on the turbine blade according to a shape optimization requirement of the turbine blade; Selecting a plurality of target control points for blade adjustment from the plurality of control points, changing the coordinate position of each of the target control points, and calculating the spatial position difference of each of the target control points; Constructing a radial basis function interpolation equation group using the spatial position difference, and solving the radial basis function interpolation equation group to obtain a mapping relationship between the control point position change and the grid node position change; The coordinate data of the grid nodes are input into the mapping relationship to obtain the deformed grid node information, and the three-dimensional model of the turbine blade is re-established using the deformed grid node information.

2. The method according to claim 1, characterized in that The obtaining of coordinate data of the grid nodes of the turbine blades comprises: When the number of grid nodes is greater than a number threshold, an open source library for 3D data visualization and analysis processing in a computer programming language is called; The turbine blade model file after meshing is converted into a file type recognizable by the open source library, and the coordinate data of the mesh nodes in the converted turbine blade model file are read through the mesh node information reading function in the open source library.

3. The method according to claim 1, characterized in that Determining the number and positions of a plurality of control points arranged on the turbine blade according to the shape optimization requirements of the turbine blade includes: Determining a range of the turbine blade to be adjusted, and arranging a plurality of control points outside the turbine blade while adjusting the overall shape of the turbine blade so as to include the turbine blade in a polyhedron formed by the plurality of control points; When adjusting the local position of the turbine blade, a plurality of control points are arranged on boundary vertices of the local position, wherein the number of control points arranged when adjusting the local position is greater than the number of control points arranged when adjusting the overall shape.

4. The method according to claim 3, characterized in that The step of changing the coordinate position of each target control point and calculating the spatial position difference of each target control point includes: Determining a reasonable range of variation of the overall shape and the local position; Within the corresponding reasonable variation range, the coordinate position of each target control point is changed, and the difference between the spatial position coordinates of each target control point after the position change and the initial spatial position coordinates is calculated.

5. The method according to claim 1, characterized in that The method of constructing a radial basis function interpolation equation group by using the spatial position difference comprises: Taking each of the target control points as an interpolation point, and taking the spatial position difference of each of the target control points as an interpolation function in a basic form of radial basis function interpolation; Based on the interpolation result of each interpolation point being equal to the corresponding spatial position difference, the radial basis function interpolation equation group to be solved is constructed, wherein the radial basis function interpolation equation group includes weight coefficients corresponding to each target control point to be solved.

6. The method according to claim 5, characterized in that The step of solving the radial basis function interpolation equation group comprises: Selecting a radial basis kernel function and an action radius of the radial basis kernel function; Solving the radial basis function interpolation equation group using the radial basis kernel function and the action radius to calculate the weight coefficient corresponding to each target control point; According to the weight coefficients corresponding to the target control points, a mapping relationship between the position changes of the control points and the position changes of the grid nodes is established.

7. The method according to claim 2, characterized in that The step of inputting the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information includes: Convert the coordinate data of the grid nodes read by the open source library into an array form; Batch inputting coordinate data in numerical form into the mapping relationship to obtain a three-dimensional coordinate change value of each grid node; The deformed grid node information of each grid node is obtained by combining the original coordinate data of each grid node and the three-dimensional coordinate change value.

8. A turbine blade optimization system for mesh deformation based on radial basis function interpolation, characterized in that: Includes the following modules: An acquisition module, used for performing meshing processing on the turbine blade to be optimized and acquiring coordinate data of the mesh nodes of the turbine blade; A determination module, used to determine the number and positions of a plurality of control points arranged on the turbine blade according to the shape optimization requirements of the turbine blade; A change module, used for selecting a plurality of target control points for blade adjustment from the plurality of control points, changing the coordinate position of each of the target control points, and calculating the spatial position difference of each of the target control points; A solution module, used to construct a radial basis function interpolation equation group using the spatial position difference, and solve the radial basis function interpolation equation group to obtain a mapping relationship between the control point position change and the grid node position change; The reconstruction module is used to input the coordinate data of the grid nodes into the mapping relationship to obtain the deformed grid node information, and use the deformed grid node information to re-establish the three-dimensional model of the turbine blade.

9. The system according to claim 8, characterized in that The acquisition module is specifically used for: When the number of grid nodes is greater than a number threshold, an open source library for 3D data visualization and analysis processing in a computer programming language is called; The turbine blade model file after meshing is converted into a file type recognizable by the open source library, and the coordinate data of the mesh nodes in the converted turbine blade model file are read through the mesh node information reading function in the open source library.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the turbine blade optimization method for mesh deformation based on radial basis function interpolation as described in any one of claims 1 to 7 is implemented.

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