Modeling method and device for turbine blade based on blade profile scatter points and electronic equipment

Abstract

The invention provides a turbine blade modeling method and device based on blade profile scatter points and electronic equipment, and the method comprises the steps that a first candidate scatter point set corresponding to a turbine blade is obtained, a candidate modeling curve corresponding to the turbine blade is obtained according to the first candidate scatter point set, and all first candidate scatter points in the first candidate scatter point set are distributed based on blade profiles; obtaining a corresponding curve modeling point deviation based on the first candidate scatter point set and the candidate modeling curve; and obtaining a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation. According to the method, the modeling accuracy and the modeling precision of the target blade curve model of the turbine blade are improved, the modeling efficiency of the target blade curve model is improved, the accuracy and the efficiency of turbine blade design of a gas turbine are further improved, and the method for obtaining the target blade curve model of the turbine blade is optimized.

Description

Technical Field

[0001] The present application relates to the technical field of gas turbines, and in particular to a modeling method, device and electronic equipment for turbine blades based on blade profile scatter points. Background Art

[0002] During the operation of a gas turbine, the aerodynamic performance of the turbine blades directly affects the efficiency and reliability of the entire machine, and the blade shape of the turbine blades is the core factor that determines the aerodynamic characteristics.

[0003] In actual engineering, turbine blade profile design or detection can be achieved through discrete point sets. In this scenario, how to accurately model the turbine blades is very important. Summary of the Invention

[0004] The purpose of this application is to solve one of the technical problems in the above technology at least to a certain extent.

[0005] A first aspect of the present application provides a modeling method for a turbine blade based on blade profile scatter points, comprising: obtaining a first candidate scatter point set corresponding to the turbine blade, and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scatter point set, wherein each first candidate scatter point in the first candidate scatter point set is distributed based on the blade profile; obtaining a corresponding curve modeling point deviation based on the first candidate scatter point set and the candidate modeling curve; and obtaining a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

[0006] A second aspect of the present application provides a modeling device for a turbine blade based on blade profile scatter points, comprising: a curve fitting module for acquiring a first candidate scatter point set corresponding to the turbine blade, and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scatter point set, wherein each first candidate scatter point in the first candidate scatter point set is based on a blade profile distribution; a first acquisition module for obtaining a corresponding curve modeling point deviation based on the first candidate scatter point set and the candidate modeling curve; and a second acquisition module for obtaining a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

[0007] An embodiment of the third aspect of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the turbine blade modeling method based on blade profile scatter points provided in the first aspect of the present application.

[0008] The fourth embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the turbine blade modeling method based on blade profile scatter points provided in the first aspect of the present application.

[0009] The fifth embodiment of the present application provides a computer program product. When the instruction processor in the computer program product is executed, the turbine blade modeling method based on blade profile scatter points provided in the first aspect of the present application is executed.

[0010] The present application provides a turbine blade modeling method and device based on blade profile scatter points. The candidate modeling curve of the turbine blade is obtained according to a first candidate scatter point set, and then a target blade curve model of the turbine blade is obtained based on the curve modeling point deviation between the first candidate scatter point set and the candidate modeling curve. This improves the modeling accuracy and modeling precision of the target blade curve model of the turbine blade, as well as the modeling efficiency of the target blade curve model, thereby improving the accuracy and efficiency of the turbine blade design of the gas turbine and optimizing the method for obtaining the target blade curve model of the turbine blade.

[0011] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0013] Figure 1 Schematic diagram of a flow chart of a turbine blade modeling method based on blade profile scattered points according to an embodiment of the present application;

[0014] Figure 2 A schematic flow chart of a turbine blade modeling method based on blade profile scatter points according to another embodiment of the present application;

[0015] Figure 3 A schematic diagram of a turbine blade curve type according to an embodiment of the present application;

[0016] Figure 4 Schematic diagram of a flow chart of a turbine blade modeling method based on blade profile scatter points according to another embodiment of the present application;

[0017] Figure 5 Schematic diagram of the structure of a turbine blade modeling device based on blade profile scattered points according to an embodiment of the present application;

[0018] Figure 6 It is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0020] The following describes the turbine blade modeling method, device and electronic device based on blade profile scatter points according to an embodiment of the present application with reference to the accompanying drawings.

[0021] Figure 1 FIG. 1 is a flow chart of a turbine blade modeling method based on blade profile scattered points according to an embodiment of the present application. Figure 1 As shown, the method includes:

[0022] S101 , obtaining a first candidate scattered point set corresponding to a turbine blade, and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scattered point set, wherein each first candidate scattered point in the first candidate scattered point set is based on a blade profile distribution.

[0023] In an embodiment of the present application, turbine blade modeling can be achieved by performing curve fitting on relevant discrete scattered points, wherein the discrete scattered points can be based on the blade profile distribution so that the curve obtained after fitting is a blade profile curve.

[0024] In this scenario, the scattered points based on the blade profile distribution used in curve fitting of the turbine blade can be determined as the first candidate scattered points, thereby obtaining a first candidate scattered point set consisting of the first candidate scattered points.

[0025] Optionally, each first candidate scatter point in the first candidate scatter point set can be algorithmically processed based on a curve fitting algorithm in related technology, and then a curve obtained by curve fitting based on each first candidate scatter point is obtained according to the result of the algorithm processing as a candidate modeling curve corresponding to the turbine blade.

[0026] The candidate modeling curves of the turbine blades may be Bezier curves or other types of curves, which are not specifically limited here.

[0027] It should be noted that the curve fitting algorithm proposed in the embodiments of the present application may be the least squares method or other types of curve fitting algorithms, which are not specifically limited here.

[0028] S102: Obtain corresponding curve modeling point deviations based on the first candidate scattered point set and the candidate modeling curve.

[0029] In an embodiment of the present application, when curve fitting is performed on the first candidate scatter point set, there may be a certain degree of deviation between some of the first candidate scatter points in the first candidate scatter point set and the candidate modeling curve obtained by fitting. The deviation can be determined as the curve modeling point deviation of each of the first candidate scatter points.

[0030] In this scenario, for any first candidate scatter point, the first candidate scatter point and the candidate modeling curve can be algorithmically processed according to a preset deviation acquisition algorithm, and the deviation value of the first candidate scatter point based on the candidate modeling curve is obtained according to the result of the algorithm processing, which is used as the curve modeling point deviation corresponding to the first candidate scatter point.

[0031] S103: Obtain a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

[0032] In the embodiment of the present application, whether each first candidate scatter point meets the modeling requirements of the turbine blade based on the blade profile scatter point can be identified based on the curve modeling point deviation of each first candidate scatter point.

[0033] In a scenario where each first candidate scatter point is identified according to the curve modeling point deviation to meet the modeling requirements of the turbine blade based on the blade profile scatter point, a blade curve model corresponding to the turbine blade can be generated according to the candidate modeling curve currently fitted as the target blade curve model of the turbine blade.

[0034] In a scenario where it is identified based on the curve modeling point deviation that some of the first candidate scatter points do not meet the modeling requirements of the turbine blade based on the blade profile scatter points, the relevant first candidate scatter points can be adjusted and optimized based on the obtained curve modeling point deviation until the adjusted and optimized first candidate scatter points meet the modeling requirements of the turbine blade based on the blade profile scatter points. The target blade curve model of the turbine blade can be generated based on the candidate modeling curve obtained from the adjusted and optimized first candidate scatter points.

[0035] Among them, according to the generation algorithm of the blade curve model in the related art, the candidate modeling curves can be algorithmically processed to obtain the target blade curve model.

[0036] The turbine blade modeling method based on blade profile scatter points proposed in this application obtains a candidate modeling curve of the turbine blade according to a first candidate scatter point set based on the blade profile distribution, and then obtains a target blade curve model of the turbine blade based on the curve modeling point deviation between the first candidate scatter point set and the candidate modeling curve, thereby improving the modeling accuracy and modeling precision of the target blade curve model of the turbine blade, and improving the modeling efficiency of the target blade curve model, thereby improving the accuracy and efficiency of the turbine blade design of the gas turbine, and optimizing the method for obtaining the target blade curve model of the turbine blade.

[0037] In the above embodiment, the modeling method of the turbine blade based on the blade profile scattered points can be combined with Figure 2 Further understanding, Figure 2 FIG. 1 is a flow chart of a turbine blade modeling method based on blade profile scatter points according to another embodiment of the present invention. Figure 2 As shown, the method includes:

[0038] S201 : Obtain a curve fitting order of each curve type in a curve type set of a turbine blade to obtain a curve fitting strategy for the turbine blade.

[0039] In the embodiment of the present application, the curve of the turbine blade may include multiple curve types, such as Figure 3 As shown, Figure 3 The turbine blades shown are made of Figure 3 The turbine blades are composed of 1-suction surface profile, 2-leading edge profile, 3-pressure surface profile and 4-trailing edge profile. In this example, the turbine blades may include Figure 3 There are 4 types of curves shown.

[0040] In this scenario, the set of curve types included in the curve of the turbine blade can be determined as the curve type set of the turbine blade.

[0041] Furthermore, for any curve type, a fitting order when fitting a curve corresponding to the curve type is obtained as the curve fitting order of the curve type.

[0042] For example, for Figure 3 The curve type corresponding to the 1-suction surface profile shown is set to use an 8th-order Bezier curve when fitting a curve of this curve type. In this example, the 8th order is the curve fitting order corresponding to this curve type.

[0043] Optionally, a strategy for performing curve fitting on turbine blades is constructed according to the curve fitting order under each curve type, as the curve fitting strategy.

[0044] S202: Obtain a set of modeling control points corresponding to the turbine blades.

[0045] In an embodiment of the present application, the control points used for curve fitting of the projection blades can be obtained based on a preset control point algorithm formula as modeling control points, thereby obtaining a modeling control point set composed of the modeling control points.

[0046] As an example, the algorithm formula for modeling control points can be as follows:

[0047]

[0048] In the above formula, L represents the sum of squares of the differences between the scatter points on the candidate modeling curve after fitting and the first candidate scatter points, (PXj , P Yj ) represents the coordinates of the modeling control point, j takes values of 0, 1, 2, ..., m, where m represents the curve fitting order, X represents the horizontal coordinate of the first candidate scattered point, Y represents the vertical coordinate of the first candidate scattered point, t i represents a scattered point, where t i ∈[0,1].

[0049] In this example, the independent variables of the above two formulas are the horizontal coordinate and vertical coordinate of the modeling control point, respectively. The process of obtaining the horizontal coordinate of the modeling control point can be understood in conjunction with the following content on solving the horizontal coordinate of the modeling coordinate point:

[0050] Alternatively, the following formula can be derived to equal 0:

[0051]

[0052] This results in the following matrix equations: ij ,pass The matrix equations are as follows:

[0053]

[0054] in, represents the partial derivative, and λ represents the constant term in the system of equations.

[0055] The m+1 sets of equations shown above have a total of m+1 unknowns, and the horizontal coordinates of the modeling control points are obtained by solving them.

[0056] It should be noted that the process of solving the ordinate of the modeling control point can be understood in conjunction with the process of solving the abscissa, and is not specifically limited here.

[0057] Optionally, in the process of curve fitting, the first and last points of the fitting curve are the first and last points of each first candidate scattered point. Thus, the first and last points of each first candidate scattered point can be determined as the first and last control points of the Bezier curve. In this scenario, the above-mentioned solution matrix can be adjusted to the m-1 order, and when the first and last slopes of the discrete points are known, the above-mentioned solution equation group can also be adjusted accordingly, which will not be repeated here.

[0058] S203 , performing curve fitting on the modeling control point set and the first candidate scattered point set based on a curve fitting strategy to obtain a fitted candidate modeling curve.

[0059] Optionally, a curve fitting algorithm in related art may be used to perform curve fitting based on the modeling control point set and each point in the first candidate scattered point set, thereby obtaining a fitted candidate modeling curve.

[0060] As an example, the fitting of the candidate modeling curve can be achieved based on the algorithm shown in the following formula:

[0061]

[0062] In the above formula, B (t) Represents the candidate modeling curve, m represents the curve fitting order, t represents the relative position of the first candidate scatter point, j represents the identifier of the modeling control point, and C represents the number of combinations.

[0063] Among them, the initial relative position t of the first candidate scattered point i It can be obtained according to the following algorithm formula:

[0064]

[0065] In the above formula, d i represents the distance between the two first candidate scattered points, S represents all d i The sum of n represents the number of first candidate scattered points, where t i ∈[0,1], i takes values of 1, 2, …, n.

[0066] S204 : For any first candidate scatter point in the first candidate scatter point set, obtain a second candidate scatter point corresponding to the first candidate scatter point on the candidate modeling curve.

[0067] Optionally, the candidate point distances between the first candidate scatter point and each third candidate scatter point on the candidate modeling curve are obtained, and the target point distance that meets the preset distance condition is obtained from the candidate point distances, and the third candidate scatter point corresponding to the target point distance is determined as the second candidate scatter point corresponding to the first candidate scatter point.

[0068] In an embodiment of the present application, each scatter point on the fitted candidate modeling curve can be determined as a third candidate scatter point on the candidate modeling curve. In this scenario, for any first candidate scatter point, the distance between the first candidate scatter point and each third candidate scatter point can be obtained as the candidate point distance according to the point-to-point distance acquisition algorithm in the relevant technology.

[0069] Optionally, each candidate point distance may be compared with a preset distance condition, and a candidate point distance matching the distance condition may be selected from the candidate distances. The candidate point distance is the target point distance.

[0070] The distance condition may be a minimum distance or a distance greater than a set value, which is not specifically limited here.

[0071] In this scenario, a third candidate scatter point corresponding to the target point distance on the candidate modeling curve can be obtained, and the third candidate scatter point can be used as the second candidate scatter point of the first candidate scatter point on the candidate modeling curve.

[0072] S205 : Obtain curve modeling point deviations according to the first modeling parameters of the first candidate scatter point and the second modeling parameters of the second candidate scatter point.

[0073] In an embodiment of the present application, for any first candidate scatter point, the relative position of the first candidate scatter point when participating in the candidate modeling curve fitting can be determined as the first modeling parameter of the first candidate scatter point, and the relative position of the second candidate scatter point corresponding to the first candidate scatter point on the candidate modeling curve can be determined as the second modeling parameter of the second candidate scatter point.

[0074] Optionally, based on the deviation acquisition algorithm in the relevant technology, the first modeling parameter of the first candidate scatter point and the second modeling parameter of the second candidate scatter point can be algorithmically processed, and then the deviation between the two is obtained according to the result of the algorithm processing, which is used as the curve modeling point deviation corresponding to the first candidate scatter point.

[0075] Among them, the corresponding deviation acquisition algorithm can be obtained based on the square loss in the relevant technology, or the corresponding deviation algorithm can be obtained based on other types of loss functions. No specific limitation is made here. Among them, the deviation acquisition algorithm based on the square loss as the loss function can be understood as the least squares method.

[0076] As an example, the curve modeling point deviation corresponding to the first candidate scattered point can be obtained by the following formula:

[0077]

[0078] In the above formula, error represents the deviation of the curve modeling point, t i_new The second modeling parameter representing the second candidate scatter point, t i_ori The first modeling parameter representing the first candidate scatter point.

[0079] S206 , obtaining a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

[0080] Optionally, it is identified whether there is a fourth candidate scatter point whose deviation from the curve modeling point of each first candidate scatter point is greater than or equal to a preset deviation threshold.

[0081] In the embodiment of the present application, a preset deviation threshold can be obtained, and the curve modeling point deviation of each first candidate scatter point can be compared with the deviation threshold. Specifically, for any first candidate scatter point, when the curve modeling point deviation corresponding to the first candidate scatter point is greater than or equal to the deviation threshold, the first candidate scatter point can be determined as the fourth candidate scatter point among the first candidate scatter points.

[0082] In a scenario where there is no fourth candidate scatter point among the first candidate scatter points, in response to identifying that there is no fourth candidate scatter point among the first candidate scatter points, a target blade curve model of the turbine blade is generated according to the candidate modeling curve.

[0083] In a scenario where there is no fourth candidate scatter point among the first candidate scatter points, it can be determined that the candidate modeling curve currently fitted meets the modeling requirements of the turbine blade for the curve model. In this scenario, a curve model corresponding to the turbine blade can be generated based on the candidate modeling curve as the target blade curve model.

[0084] Among them, the candidate modeling curve can be subjected to model building processing according to the curve model building method in the related art, so as to obtain the target blade curve model of the turbine blade.

[0085] In a scenario where at least one fourth candidate scatter point exists among each first candidate scatter point, in response to identifying that at least one fourth candidate scatter point exists among each first candidate scatter point, a fifth candidate scatter point corresponding to each of the at least one fourth candidate scatter point on the candidate modeling curve is obtained, and the first candidate scatter point set is updated according to each fifth candidate scatter point to obtain a new first candidate scatter point set.

[0086] In an embodiment of the present application, when it is identified that there is at least one fourth candidate scatter point among the first candidate scatter points, it can be determined that the current candidate modeling curve does not meet the modeling requirements of the turbine blade for the curve model. In this scenario, it is possible to return to re-fit the modeling curve.

[0087] Optionally, the part of the fourth candidate scatter points can be integrated into the first candidate scatter point set, wherein, for any fourth candidate scatter point, the first candidate scatter point corresponding to the fourth candidate scatter point in the first candidate scatter point set can be obtained, and the information of the fourth candidate scatter point is overwritten on the first candidate scatter point, thereby realizing the integration of the fourth candidate scatter point into the first candidate scatter point set.

[0088] Furthermore, all fourth candidate scattered points are integrated into the first candidate scattered point set to obtain an integrated new first candidate scattered point set.

[0089] Optionally, return to generating a new candidate modeling curve based on the new first candidate scatter point set until there is no fourth candidate scatter point in the curve modeling point deviation of each new first candidate scatter point in the new candidate modeling curve, and generate the target blade curve model based on the new candidate modeling curve.

[0090] In an embodiment of the present application, after obtaining the integrated new first candidate scatter point set, a new round of curve fitting can be performed based on the new first candidate scatter point set to obtain a new candidate modeling curve, and it can be identified whether there is a fourth candidate scatter point on the new candidate modeling curve.

[0091] Optionally, when it is identified that the fourth candidate scattered point does not exist on the new candidate modeling curve, a blade curve model, that is, a target blade curve model of the turbine blade, may be generated based on the new candidate modeling curve.

[0092] The turbine blade modeling method based on blade profile scatter points proposed in this application obtains a candidate modeling curve of the turbine blade according to a first candidate scatter point set based on the blade profile distribution, and then obtains a target blade curve model of the turbine blade based on the curve modeling point deviation between the first candidate scatter point set and the candidate modeling curve, thereby improving the modeling accuracy and modeling precision of the target blade curve model of the turbine blade, and improving the modeling efficiency of the target blade curve model, thereby improving the accuracy and efficiency of the turbine blade design of the gas turbine, and optimizing the method for obtaining the target blade curve model of the turbine blade.

[0093] To better understand the above embodiments, Figure 4 understand, Figure 4 Schematic diagram of a flow chart of a turbine blade modeling method based on blade profile scatter points according to another embodiment of the present application.

[0094] like Figure 4 As shown, in the process of modeling the blade curve model of the turbine blade, n blade profile discrete points can be obtained as first candidate scattered points, and the initial relative position of each first candidate scattered point can be obtained. Furthermore, the Bezier curve is fitted based on each first candidate scattered point by the least squares method to obtain the fitted candidate modeling curve.

[0095] Optionally, the minimum distance between each first candidate scatter point and the candidate modeling curve is obtained to obtain a point on the candidate modeling curve with the minimum distance as the second candidate scatter point corresponding to each first candidate scatter point on the candidate modeling curve.

[0096] Furthermore, based on any first candidate scattered point and the second candidate scattered point corresponding to the first candidate scattered point, the corresponding curve modeling point deviation is obtained, such as Figure 4As shown, when the deviations of each curve modeling point are less than the preset deviation threshold, the target blade curve model of the turbine blade is obtained based on the candidate modeling curve. When there is a deviation greater than or equal to the deviation threshold in the deviations of each curve modeling point, return to perform a new round of curve fitting, obtain a new first candidate scatter point set to obtain a new candidate modeling curve, until the deviations between the corresponding points of the new first candidate scatter point set on the new candidate modeling curve are all less than the above-mentioned deviation threshold, and the target blade curve model is obtained according to the new candidate modeling curve.

[0097] The turbine blade modeling method based on blade profile scatter points proposed in this application obtains a candidate modeling curve of the turbine blade according to a first candidate scatter point set based on the blade profile distribution, and then obtains a target blade curve model of the turbine blade based on the curve modeling point deviation between the first candidate scatter point set and the candidate modeling curve, thereby improving the modeling accuracy and modeling precision of the target blade curve model of the turbine blade, and improving the modeling efficiency of the target blade curve model, thereby improving the accuracy and efficiency of the turbine blade design of the gas turbine, and optimizing the method for obtaining the target blade curve model of the turbine blade.

[0098] Corresponding to the turbine blade modeling method based on blade profile scatter points proposed in the above-mentioned embodiments, an embodiment of the present application also proposes a turbine blade modeling device based on blade profile scatter points. Since the turbine blade modeling device based on blade profile scatter points proposed in the embodiment of the present application corresponds to the turbine blade modeling method based on blade profile scatter points proposed in the above-mentioned embodiments, the implementation method of the above-mentioned turbine blade modeling method based on blade profile scatter points is also applicable to the turbine blade modeling device based on blade profile scatter points proposed in the embodiment of the present application, and will not be described in detail in the following embodiments.

[0099] Figure 5 FIG. 1 is a schematic structural diagram of a turbine blade modeling device based on blade profile scattered points according to an embodiment of the present application. Figure 5 As shown, the turbine blade modeling device 500 based on blade profile scatter points includes a curve fitting module 51, a first acquisition module 52 and a second acquisition module 53, wherein:

[0100] a curve fitting module 51 for obtaining a first candidate scattered point set corresponding to the turbine blade, and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scattered point set, wherein each first candidate scattered point in the first candidate scattered point set is based on a blade profile distribution;

[0101] A first acquisition module 52 is configured to obtain corresponding curve modeling point deviations based on the first candidate scattered point set and the candidate modeling curve;

[0102] The second acquisition module 53 is configured to obtain a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

[0103] In an embodiment of the present application, the curve fitting module 51 is also used to: obtain the curve fitting order of each curve type in the curve type set of the turbine blade to obtain the curve fitting strategy of the turbine blade; obtain the modeling control point set corresponding to the turbine blade; and perform curve fitting on the modeling control point set and the first candidate scattered point set based on the curve fitting strategy to obtain the fitted candidate modeling curve.

[0104] In the embodiment of the present application, the first acquisition module 52 is further configured to: for any first candidate scatter point in the first candidate scatter point set, obtain a second candidate scatter point corresponding to the first candidate scatter point on the candidate modeling curve; and obtain a curve modeling point deviation based on a first modeling parameter of the first candidate scatter point and a second modeling parameter of the second candidate scatter point.

[0105] In the embodiment of the present application, the first acquisition module 52 is further configured to: obtain a candidate point distance between the first candidate scatter point and each third candidate scatter point on the candidate modeling curve; obtain a target point distance that satisfies a preset distance condition from each candidate point distance, and determine the third candidate scatter point corresponding to the target point distance as the second candidate scatter point corresponding to the first candidate scatter point.

[0106] In the embodiment of the present application, the second acquisition module 53 is further configured to: identify whether there is a fourth candidate scatter point whose deviation from the curve modeling point of each first candidate scatter point is greater than or equal to a preset deviation threshold; and in response to identifying that the fourth candidate scatter point does not exist among the first candidate scatter points, generate a target blade curve model of the turbine blade based on the candidate modeling curve.

[0107] In the embodiment of the present application, the second acquisition module 53 is further configured to: in response to identifying that at least one fourth candidate scatter point exists among each first candidate scatter point, obtain a fifth candidate scatter point corresponding to each of the at least one fourth candidate scatter point on the candidate modeling curve, and update the first candidate scatter point set according to each fifth candidate scatter point to obtain a new first candidate scatter point set; return to generating a new candidate modeling curve based on the new first candidate scatter point set until the fourth candidate scatter point no longer exists in the curve modeling point deviations of each new first candidate scatter point in the new candidate modeling curve, and generate the target blade curve model according to the new candidate modeling curve.

[0108] The turbine blade modeling device based on blade profile scatter points proposed in this application obtains a candidate modeling curve of the turbine blade according to a first candidate scatter point set based on the blade profile distribution, and then obtains a target blade curve model of the turbine blade based on the curve modeling point deviation between the first candidate scatter point set and the candidate modeling curve, thereby improving the modeling accuracy and modeling precision of the target blade curve model of the turbine blade, and improving the modeling efficiency of the target blade curve model, thereby improving the accuracy and efficiency of the turbine blade design of the gas turbine, and optimizing the method for obtaining the target blade curve model of the turbine blade.

[0109] To achieve the above embodiments, the present application also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0110] Figure 6 This is a block diagram of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, the device 600 includes a memory 61, a processor 62, and a computer program stored in the memory 61 and executable on the processor 62. When the processor 62 executes the program instructions, the execution is realized. Figures 1 to 4 A method for modeling a turbine blade based on blade profile scatter points of an embodiment.

[0111] In order to implement the above embodiment, the present application also provides a non-transitory computer-readable storage medium storing computer instructions, which is used to enable the computer to execute Figures 1 to 4 A method for modeling a turbine blade based on blade profile scatter points of an embodiment.

[0112] In order to implement the above embodiment, the present application also provides a computer program product, when the instruction processor in the computer program product executes Figures 1 to 4 A method for modeling a turbine blade based on blade profile scatter points of an embodiment.

[0113] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0115] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0116] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For 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 conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0117] 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 of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0118] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related 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 embodiment.

[0119] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If 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.

[0120] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A turbine blade modeling method based on blade profile scattered points, characterized in that: The method comprises: Acquiring a first candidate scattered point set corresponding to a turbine blade, and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scattered point set, wherein each first candidate scattered point in the first candidate scattered point set is based on a blade profile distribution; Based on the first candidate scattered point set and the candidate modeling curve, obtaining corresponding curve modeling point deviations; Based on the curve modeling point deviation, a target blade curve model corresponding to the turbine blade is obtained.

2. The method according to claim 1, characterized in that The step of obtaining a first candidate scattered point set corresponding to the turbine blade and obtaining a candidate modeling curve corresponding to the turbine blade based on the first candidate scattered point set, wherein each first candidate scattered point in the first candidate scattered point set is based on a blade profile distribution, includes: Obtaining a curve fitting order of each curve type in the curve type set of the turbine blade to obtain a curve fitting strategy for the turbine blade; Obtaining a modeling control point set corresponding to the turbine blade; Curve fitting is performed on the modeling control point set and the first candidate scattered point set based on the curve fitting strategy to obtain the fitted candidate modeling curve.

3. The method according to claim 2, characterized in that The obtaining corresponding curve modeling point deviations based on the first candidate scattered point set and the candidate modeling curve includes: For any first candidate scattered point in the first candidate scattered point set, obtaining a second candidate scattered point corresponding to the first candidate scattered point on the candidate modeling curve; The curve modeling point deviation is obtained according to a first modeling parameter of the first candidate scatter point and a second modeling parameter of the second candidate scatter point.

4. The method according to claim 3, characterized in that The step of obtaining, for any first candidate scatter point in the first candidate scatter point set, a second candidate scatter point corresponding to the first candidate scatter point on the candidate modeling curve includes: Obtaining candidate point distances between the first candidate scattered point and each third candidate scattered point on the candidate modeling curve; A target point distance that meets a preset distance condition is obtained from the distances of each candidate point, and a third candidate scattered point corresponding to the target point distance is determined as the second candidate scattered point corresponding to the first candidate scattered point.

5. The method according to claim 3, characterized in that The step of obtaining a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation includes: Identify whether there is a fourth candidate scattered point whose deviation from the curve modeling point of each first candidate scattered point is greater than or equal to a preset deviation threshold; In response to identifying that a fourth candidate scattered point does not exist among the first candidate scattered points, the target blade curve model of the turbine blade is generated according to the candidate modeling curve.

6. The method according to claim 5, characterized in that The method further comprises: In response to identifying that at least one fourth candidate scatter point exists among each of the first candidate scatter points, obtaining a fifth candidate scatter point corresponding to each of the at least one fourth candidate scatter point on the candidate modeling curve, and updating the first candidate scatter point set according to each fifth candidate scatter point to obtain a new first candidate scatter point set; Return to generating a new candidate modeling curve based on the new first candidate scattered point set until the fourth candidate scattered point does not exist in the curve modeling point deviations of each new first candidate scattered point in the new candidate modeling curve, and generate the target blade curve model based on the new candidate modeling curve.

7. A turbine blade modeling device based on blade profile scattered points, characterized in that: The device comprises: a curve fitting module, configured to obtain a first candidate scattered point set corresponding to a turbine blade, and obtain a candidate modeling curve corresponding to the turbine blade based on the first candidate scattered point set, wherein each first candidate scattered point in the first candidate scattered point set is based on a blade profile distribution; A first acquisition module, configured to obtain corresponding curve modeling point deviations based on the first candidate scattered point set and the candidate modeling curve; The second acquisition module is used to obtain a target blade curve model corresponding to the turbine blade based on the curve modeling point deviation.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When a processor executes the instructions in the computer program product, the method according to any one of claims 1 to 6 is performed.

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