Method, device, equipment and medium for long flexible blade wind turbine airfoil optimization

Abstract

The application relates to the technical field of wind turbine airfoil optimization, and particularly provides a wind turbine airfoil optimization method, device, equipment and medium for long and soft blades, aiming to solve the problem of lacking a systematic and high-robustness wind turbine airfoil optimization design method for long and soft blades. To this end, the method provided by the application comprises the following steps: obtaining basic parameters of a target airfoil, determining an initial airfoil according to the basic parameters, analyzing the initial airfoil into a plurality of geometric characteristic variables, constructing a plurality of optimization parameters representing the aerodynamic performance of the airfoil based on the plurality of geometric characteristic variables and the basic parameters, constructing an optimization objective function according to the plurality of optimization parameters, and optimizing and solving the plurality of geometric characteristic variables based on the optimization objective function. The optimization objective function is constructed by using the plurality of optimization parameters, the adaptability of the optimized airfoil to complex working conditions is improved, the optimal solution is directly obtained by using an optimization algorithm, the optimization efficiency is high, and the airfoil aerodynamic characteristics have good robustness.

Description

Technical Field

[0001] This application relates to the field of wind turbine airfoil optimization technology, specifically to a method, apparatus, equipment, and medium for optimizing wind turbine airfoils for long, flexible blades. Background Technology

[0002] In recent years, wind power equipment has rapidly developed towards larger and lighter sizes. The blades of ultra-large wind turbines have reached lengths exceeding 100 meters. Due to this increased length, the flexibility and deformation of these blades also increase, potentially leading to significant flexible deformation effects during operation. This can cause the actual operating angle of attack to deviate from the design angle of attack, and may even result in stall, leading to poor wind turbine performance and extreme loads. Currently, conventional wind turbine airfoils cannot meet the requirements of ultra-long, flexible blades, and there is a lack of systematic and robust wind turbine airfoil optimization design methods specifically for such long, flexible blades.

[0003] Accordingly, there is a need in the field for a new wind turbine airfoil optimization scheme for long and flexible blades to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned deficiencies, this application is made to solve or at least partially solve the technical problem that the prior art lacks a systematic and robust wind turbine airfoil optimization design method for long and flexible blades.

[0005] In a first aspect, a method for optimizing wind turbine airfoils for long, flexible blades is provided. The method includes: obtaining basic parameters of the target airfoil; determining an initial airfoil based on the basic parameters; analyzing the initial airfoil into multiple geometric feature variables; constructing multiple optimization parameters characterizing the aerodynamic performance of the airfoil based on the multiple geometric feature variables and the basic parameters; constructing an optimization objective function based on the multiple optimization parameters; and optimizing and solving the multiple geometric feature variables based on the optimization objective function.

[0006] In one technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the basic parameters include the design Reynolds number, and the various optimization parameters include the airfoil's aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters. The construction of various optimization parameters characterizing the airfoil's aerodynamic performance based on the multiple geometric feature variables and the basic parameters includes: calculating the airfoil's aerodynamic parameters according to the multiple geometric feature variables and the design Reynolds number; and using the aerodynamic parameters to construct the aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters characterizing the airfoil's aerodynamic performance.

[0007] In one technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the expressions for the aerodynamic efficiency parameter, stall safety parameter, mild stall characteristic parameter, and aerodynamic performance stability parameter are as follows:

[0008]

[0009] f2=α stall -α design

[0010]

[0011]

[0012] Where f1 represents the aerodynamic efficiency parameter, s CL C represents the normalization coefficient. L C represents the lift coefficient. D α represents the drag coefficient, and α represents the angle of attack. D Indicates the working angle of attack range. f2 represents the average value; f2 represents the stall safety parameter, α stall Indicates the stall angle of attack, α design f3 represents the operating angle of attack; f3 represents the mild stall characteristic parameter; C L stall represents the lift coefficient at stall angle of attack, max(·) represents finding the maximum value; f4 represents the aerodynamic performance stability parameter, grad(·) represents finding the gradient.

[0013] In one technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the step of constructing an optimization objective function based on the multiple optimization parameters includes: obtaining multiple weight coefficients, wherein each weight coefficient corresponds one-to-one with the optimization parameter; and using the sum of the products of each weight coefficient and each optimization parameter as the optimization objective function.

[0014] In one technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the method further includes: normalizing the order of magnitude of the various optimization parameters.

[0015] In one technical solution of the above-mentioned optimization method for wind turbine airfoils with long, flexible blades, the basic parameters of the target airfoil include the maximum relative thickness. The optimization solution based on the objective function for the plurality of geometric feature variables includes: obtaining the boundaries of the plurality of geometric feature variables; determining the constraints for optimization based on the boundaries of the plurality of geometric feature variables and the maximum relative thickness; and optimizing the plurality of geometric feature variables based on the constraints and the objective function.

[0016] In one technical solution of the above-mentioned method for optimizing the airfoil of a wind turbine with long and flexible blades, the step of determining the initial airfoil based on the basic parameters includes: selecting the initial airfoil from a preset airfoil library based on the maximum relative thickness.

[0017] In a second aspect, a wind turbine airfoil optimization device for long, flexible blades is provided. The device includes: a basic parameter acquisition module for acquiring basic parameters of the target airfoil; an initial airfoil determination module for determining an initial airfoil based on the basic parameters; a feature variable analysis module for analyzing the initial airfoil into multiple geometric feature variables; an optimization parameter construction module for constructing multiple optimization parameters characterizing the aerodynamic performance of the airfoil based on the multiple geometric feature variables and the basic parameters; an objective function construction module for constructing an optimization objective function based on the multiple optimization parameters; and an optimization solution module for optimizing and solving the multiple geometric feature variables based on the optimization objective function.

[0018] In a third aspect, a smart device is provided, the smart device including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the method described in the first aspect or any corresponding technical solution.

[0019] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and executed by a processor to perform the method described in the first aspect or any of the corresponding technical solutions.

[0020] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0021] In implementing the technical solution provided in this application, an initial airfoil is determined based on the basic parameters of the target airfoil. The initial airfoil is then analyzed into multiple geometric characteristic variables. Various optimization parameters characterizing the aerodynamic performance of the airfoil are then constructed based on these geometric characteristic variables and the basic parameters. An optimization objective function is constructed based on these optimization parameters. Finally, the multiple geometric characteristic variables are optimized and solved using this objective function to obtain the optimized airfoil. Because this application uses multiple optimization parameters characterizing the aerodynamic performance of the airfoil when constructing the optimization objective function, it fully considers the complex and variable operating conditions of ultra-long flexible blades, thereby improving the adaptability of the optimized airfoil to complex operating conditions. This provides a systematic and highly robust wind turbine airfoil optimization design method for long flexible blades.

[0022] In implementing the technical solution provided in this application, an optimization objective function is constructed by combining aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters. This achieves the goal of improving the airfoil's adaptability to complex operating conditions. By reducing performance fluctuations and decreasing the stall probability, the optimized airfoil is better matched with the ultra-long flexible blade.

[0023] In implementing the technical solution provided in this application, an optimization objective function is constructed based on the sum of the products of each weight coefficient and each optimization parameter, thereby achieving the goal of balancing the importance of multiple optimization parameters and dynamically adjusting them according to actual application needs to adapt to different optimization scenarios.

[0024] In implementing the technical solution provided in this application, by normalizing the order of magnitude of various optimization parameters, the numerical differences between various optimization objective functions are reduced, thereby improving the convergence speed and stability of the optimization algorithm.

[0025] In implementing the technical solution provided in this application, the constraints for optimization are determined based on the boundaries of multiple geometric feature variables and the maximum relative thickness, thereby ensuring that the optimization results meet actual needs, avoiding invalid solutions, reducing the search space of the optimization problem, and improving the solution efficiency. Attached Figure Description

[0026] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Wherein:

[0027] Figure 1 This is a schematic flowchart of the main steps of a wind turbine airfoil optimization method for long flexible blades according to an embodiment of this application;

[0028] Figure 2 This is a schematic diagram comparing the optimized airfoil shape before and after an embodiment of this application;

[0029] Figure 3 This is a schematic diagram comparing the lift-to-drag ratio curves of the airfoil before and after optimization according to an embodiment of this application;

[0030] Figure 4 This is a schematic diagram comparing the lift coefficient curves of the airfoil before and after optimization according to an embodiment of this application;

[0031] Figure 5 This is a schematic diagram comparing the drag coefficient curves of the airfoil before and after optimization according to an embodiment of this application;

[0032] Figure 6 This is a schematic diagram of the main structure of a wind turbine airfoil optimization device for long flexible blades according to an embodiment of this application;

[0033] Figure 7 This is a schematic diagram showing the connection relationship between the processor and memory of a smart device according to an embodiment of this application.

[0034] Figure label:

[0035] 11: Memory; 12: Processor. Detailed Implementation

[0036] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0037] In the description of this application, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection via an intermediate medium; a connection within two elements; a wireless connection or a wired connection.

[0038] Furthermore, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor can be implemented in software, in hardware, or a combination of both. Computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0039] Furthermore, if the term "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B," and can include only A, only B, or A and B. The singular forms of the terms "a" and "this" can also include plural forms.

[0040] Airfoils are the most basic element constituting wind turbine blades. The aerodynamic performance of the airfoil directly determines the blade's output performance and load characteristics, significantly impacting the overall performance and service life of the turbine. Therefore, optimized airfoil design is a fundamental method and necessary means to ensure the wind energy conversion efficiency and operational reliability of wind turbines. In recent years, wind power equipment has rapidly developed towards larger and lighter sizes, with ultra-large turbine blades exceeding 100 meters in length. These blades exhibit significant flexible deformation effects during operation, potentially leading to poor turbine performance and extreme loads. Currently, conventional wind turbine airfoils cannot meet the requirements of ultra-long, flexible blades. Therefore, there is an urgent need in this field for a new, highly robust wind turbine airfoil design method suitable for long, flexible blades to address these issues.

[0041] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a wind turbine airfoil optimization method for long, flexible blades according to an embodiment of this application. Figure 1 As shown, the method mainly includes the following steps S2 to S12:

[0042] Step S2: Obtain the basic parameters of the target airfoil.

[0043] In this embodiment, the basic parameters of the target airfoil are some parameters of the optimized airfoil that have been determined, such as the maximum relative thickness of the airfoil and its relative chord position, design Reynolds number, working angle of attack range, and blunt trailing edge thickness.

[0044] Step S4: Determine the initial airfoil based on the basic parameters.

[0045] In this embodiment, an initial airfoil is selected from a preset airfoil library based on the basic parameters in step S2. The preset airfoil library may be, for example, the NACA airfoil database or the DU airfoil database.

[0046] In one implementation, an initial airfoil can be selected from a preset airfoil library based on the maximum relative thickness.

[0047] Step S6: The initial airfoil is analyzed into multiple geometric feature variables.

[0048] In this embodiment, a parametric method is used to resolve the initial airfoil selected in step S6 into multiple geometric feature variables. The parametric method can optionally use the Class-Shape Transformation (CST) method to convert the initial airfoil into 8 to 12 geometric feature variables.

[0049] In one implementation, the mathematical expression for the CST method is:

[0050] y(x)=C(x)·S(x)+x·Δy TE

[0051] Where x represents the x-coordinate of the airfoil, y(x) represents the y-coordinate of the upper or lower surface of the airfoil, and C(x) is the category function used to represent the type of airfoil, usually C(x) = x N1 ·(1-x) N2 For a typical airfoil, N1 is set to 0.5 and N2 to 1; S(x) is the shape function, used to accurately describe the geometric shape of the airfoil, typically... Where A i S is an adjustable parameter. i For Bernstein functions; Δy TE This indicates the thickness of the blunt trailing edge.

[0052] In one implementation, a series of adjustable parameters A can also be used. i The coordinates of the airfoil are used to represent the airfoil.

[0053] In one implementation, the CST method described above can be used to resolve the initial airfoil into multiple geometric feature variables (such as five adjustable parameters on the upper surface and five adjustable parameters on the lower surface) for airfoil optimization design. By adjusting the shape function coefficients, airfoils with different geometric shapes can be generated.

[0054] Step S8: Construct various optimization parameters to characterize the aerodynamic performance of the airfoil based on multiple geometric feature variables and basic parameters.

[0055] In this embodiment, multiple optimized parameters characterizing the aerodynamic performance of the airfoil are constructed based on the multiple geometric feature variables obtained from step S6 and the basic parameters from step S2.

[0056] In one implementation, the various optimization parameters include, but are not limited to, aerodynamic efficiency parameters within the airfoil's operating angle of attack range, airfoil stall safety parameters, airfoil mild stall characteristic parameters, and airfoil aerodynamic performance stability parameters.

[0057] Step S10: Construct an optimization objective function based on various optimization parameters.

[0058] In this embodiment, the optimization objective function of the airfoil is constructed based on the various optimization parameters in step S8.

[0059] In one implementation, the objective function for airfoil optimization can be constructed by weighted summation of multiple optimization parameters.

[0060] Step S12: Optimize and solve multiple geometric feature variables based on the optimization objective function.

[0061] In this embodiment, optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, ant colony optimization algorithms, etc.) are used to optimize and solve multiple geometric feature variables according to the optimization objective function constructed in step S10.

[0062] In implementing the technical solution provided in this application, an initial airfoil is determined based on the basic parameters of the target airfoil. The initial airfoil is then analyzed into multiple geometric characteristic variables. Various optimization parameters characterizing the aerodynamic performance of the airfoil are then constructed based on these geometric characteristic variables and the basic parameters. An optimization objective function is constructed based on these optimization parameters. Finally, the multiple geometric characteristic variables are optimized and solved using this objective function to obtain the optimized airfoil. Because this application uses multiple optimization parameters characterizing the aerodynamic performance of the airfoil when constructing the optimization objective function, it fully considers the complex and variable operating conditions of ultra-long flexible blades, thereby improving the adaptability of the optimized airfoil to complex operating conditions. This provides a systematic and highly robust wind turbine airfoil optimization design method for long flexible blades.

[0063] The following provides further explanation of steps S8 to S12.

[0064] In one embodiment of this application, the basic parameters include the design Reynolds number, and various optimization parameters include the airfoil's aerodynamic efficiency parameter f1, stall safety parameter f2, mild stall characteristic parameter f3, and aerodynamic performance stability parameter f4. The above step S8 may further include the following steps S82 and S84:

[0065] Step S82: Calculate the aerodynamic parameters of the airfoil based on multiple geometric characteristic variables and the design Reynolds number.

[0066] In this embodiment, the aerodynamic parameters of the airfoil are calculated using numerical simulation based on the design Reynolds number and multiple geometric characteristic variables. The numerical simulation is such as the surface element method based on potential flow theory, and the aerodynamic parameters are such as the lift coefficient curve and the drag coefficient curve.

[0067] Step S84: Use aerodynamic parameters to construct aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters that characterize the aerodynamic performance of the airfoil.

[0068] In this embodiment, the aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters are calculated using the following formulas:

[0069]

[0070] f2=α stall -α design

[0071]

[0072]

[0073] Where f1 represents the aerodynamic efficiency parameter, s CL C represents the normalization coefficient. L C represents the lift coefficient. D α represents the drag coefficient, and α represents the angle of attack. D Indicates the working angle of attack range. This represents the average value of the lift coefficient. f1 represents the average lift-to-drag ratio; f2 represents the stall safety parameter, α stall Indicates the stall angle of attack, α design f3 represents the operating angle of attack; f3 represents the mild stall characteristic parameter; C L stall Indicates the lift coefficient at stall angle of attack. This means that in 0 < α - α stall <4 range The maximum value; f4 represents the aerodynamic performance stability parameter. express The average gradient.

[0074] In one embodiment of this application, the basic parameters include the design Reynolds number, and various optimization parameters include the airfoil's aerodynamic efficiency parameter f1, stall safety parameter f2, mild stall characteristic parameter f3, and aerodynamic performance stability parameter f4. The above step S8 may further include the following steps S82 and S84:

[0075] In one embodiment of this application, step S10 may further include steps S102 and S104:

[0076] Step S102: Obtain multiple weight coefficients, each of which corresponds to an optimization parameter.

[0077] In this embodiment, the weight coefficients corresponding to the optimization parameters can be determined based on the importance of each optimization parameter and the positive or negative direction of the target value. For example, among f1 to f4, in order to optimize the aerodynamic performance of the airfoil, f1 and f2 need to be maximized, while f3 and f4 need to be minimized. In this case, the target can be unified by adjusting the weight direction.

[0078] Step S104: The sum of the products of each weight coefficient and each optimization parameter is used as the optimization objective function.

[0079] In this embodiment, the weighted sum of multiple weight coefficients and multiple optimization parameters is used as the optimization objective function.

[0080] In implementing the technical solution provided in this application, an optimization objective function is constructed based on the sum of the products of each weight coefficient and each optimization parameter, thereby achieving the goal of balancing the importance of multiple optimization parameters and dynamically adjusting them according to actual application needs to adapt to different optimization scenarios.

[0081] In an optional implementation, before step S102 described above, the following step S101 is further included:

[0082] Step S101: Normalize the order of magnitude of various optimization parameters.

[0083] In this embodiment, since the dimensions and value ranges of different optimization parameters may vary greatly, direct addition would cause some optimization parameters to be ignored. Therefore, this embodiment performs order-of-magnitude normalization on multiple optimization parameters, allowing each optimization parameter to be compared on the same scale (order of magnitude), making the optimization results more fair and reasonable; by reducing the numerical differences between multiple optimization parameters, the convergence speed and stability of the optimization algorithm are further improved.

[0084] In one alternative implementation, the objective function F is optimized as follows:

[0085]

[0086] Among them, s i w represents the normalization coefficient of the i-th order of magnitude. i f represents the i-th weight coefficient. i Let f represent the i-th optimization parameter, and n represent the total number of optimization parameters. The optimization parameters include at least the airfoil's aerodynamic efficiency parameter f1, stall safety parameter f2, mild stall characteristic parameter f3, and aerodynamic performance stability parameter f4.

[0087] In one embodiment of this application, step S12 may further include steps S122 to S126:

[0088] Step S122: Obtain the boundaries of multiple geometric feature variables.

[0089] In this embodiment, the boundaries of the geometric feature variables can be determined based on the parameters of the initial airfoil, such as using an increase or decrease of 10% in the initial airfoil parameters as the upper and lower bounds of the geometric feature variables.

[0090] Step S124: Determine the constraints for optimization based on the boundaries of multiple geometric feature variables and the maximum relative thickness.

[0091] In this embodiment, the upper and lower bounds of the geometric feature variables and the maximum relative thickness determined in step S122 are used as constraints for optimization to avoid obtaining invalid solutions and ensure that the optimization results meet the actual requirements.

[0092] Step S126: Optimize and solve multiple geometric feature variables according to the constraints and the optimization objective function.

[0093] In this embodiment, an optimization algorithm is used to optimize and solve multiple geometric feature variables based on the above constraints and the optimization objective function. The optimization algorithm can be selected according to the optimization objective function, and this embodiment does not impose specific restrictions on it.

[0094] In one application scenario of this application, a method for designing a highly robust wind turbine airfoil suitable for long, flexible blades is provided, comprising the following steps:

[0095] Step 1: Determine the basic parameters of the target airfoil, including the maximum relative thickness of the airfoil and its relative chordal position, design Reynolds number, working angle of attack range, and blunt trailing edge thickness.

[0096] In this embodiment, the maximum relative thickness is 25%, the maximum relative thickness in the chord direction is 32%, and the design Reynolds number is 3 × 10⁻⁶. 6 The working angle of attack ranges from 4° to 7°, and the blunt trailing edge thickness is 0.42%.

[0097] Step 2: Select an optimized initial airfoil from the airfoil library based on the maximum relative thickness of the target airfoil.

[0098] In this embodiment, the initial airfoil is selected as DU 91-W2-250.

[0099] Step 3: Use the CST method to analyze the airfoil into multiple geometric characteristic variables.

[0100] Step 4: Based on the design Reynolds number and multiple geometric characteristic variables, the aerodynamic performance of the airfoil is parameterized, and the following optimization objective function F is constructed:

[0101]

[0102] Among them, f iThis includes aerodynamic efficiency parameter f1 within the airfoil's operating angle of attack range, airfoil stall safety parameter f2, airfoil mild stall characteristic parameter f3, and airfoil aerodynamic performance stability parameter f4. i The specific calculation formula can be found in the descriptions of other embodiments of this application, and will not be repeated here.

[0103] In this embodiment, f1 represents the maximum lift coefficient and lift-to-drag ratio within the operating angle of attack range. Maximizing f1 maintains good performance under varying angle of attack conditions, providing better adaptability to complex operating conditions. f2 represents stall safety; maximizing f2 expands the space between the operating angle of attack and the stall angle of attack, reducing stall risk and better matching ultra-long flexible blades. f3 represents mild stall characteristics; minimizing f3 reduces lift drop after airfoil stall, mitigating the impact of stall. f4 represents the airfoil aerodynamic performance stability parameter within the operating angle of attack range, i.e., the slope of the lift coefficient change. Minimizing f4 better reduces loads under varying angle of attack conditions, further matching ultra-long flexible blades. In summary, the method provided in this embodiment can adapt to the complex and variable operating conditions faced by ultra-long flexible blades, cope with frequent angle of attack changes, reduce stall risk, enhance the overall aerodynamic performance of the airfoil under varying angle of attack conditions, and reduce loads caused by angle of attack fluctuations.

[0104] Optional, order-of-magnitude normalization coefficient s i and weighting coefficient w i The values ​​are shown in Table 1. Set s CL =100.

[0105] Table 1. Coefficient Values

[0106] <![CDATA[f1]]> <![CDATA[f2]]> <![CDATA[f3]]> <![CDATA[f4]]> Order of magnitude normalization coefficient 1 20 2500 1500 Weighting coefficient 0.3 0.1 0.2 0.4

[0107] Step 5: Based on Steps 3 and 4, select an optimization algorithm to optimize the initial airfoil shape, obtaining an optimized, highly robust wind turbine airfoil suitable for long, flexible blades. Specifically, in the optimization algorithm, F is set as the objective function, and multiple geometric feature variables are set as optimization variables. Based on the airfoil structure and geometric compatibility requirements, upper and lower bounds of multiple geometric feature variables and the maximum relative thickness of the airfoil are set as constraints.

[0108] In one specific implementation, a genetic algorithm is selected for optimization calculation, and the upper and lower bounds of the airfoil geometric characteristic variables are set to the initial airfoil parameters increasing and decreasing by 10%.

[0109] In this embodiment, a comparison is made between the optimized high-robustness wind turbine airfoil (New Airfoil) and the initial airfoil (DU 91-W2-250). Figure 2 As shown, the lift-to-drag ratio curve, lift coefficient curve, and drag coefficient curve of the two are compared as follows: Figure 3 , Figure 4 and Figure 5 As shown. (Through) Figures 3 to 5 As can be seen, compared with the original airfoil, the optimized airfoil in this embodiment has a 0.4% higher lift-to-drag ratio and a 6.3% higher lift coefficient within the operating angle of attack range, exhibiting better aerodynamic performance. The distance between the operating angle of attack and the stall angle of attack is increased by 0.8°, and the stall characteristic coefficient is reduced by 81.3%, significantly reducing stall risk and the resulting power drop and load fluctuations. The slope of the lift coefficient change is reduced by 14.0%, effectively mitigating load changes caused by angle-of-attack variations. Therefore, the optimized airfoil obtained using the method in this embodiment has better aerodynamic performance and higher robustness, and can better cope with the complex operating characteristics of ultra-long flexible blades.

[0110] This embodiment employs an optimization algorithm to conveniently and simply obtain the wind turbine airfoil with optimal aerodynamic performance, directly yielding the overall optimal solution. This results in high design efficiency and avoids the complexity and uncertainty caused by repeated manual adjustments. Through a novel aerodynamic performance evaluation method, the designed airfoil exhibits excellent performance across its operating angle of attack range, including superior aerodynamic efficiency, minimal performance fluctuations, low stall probability, and smooth stall characteristics. This solves the problem of poor applicability of existing airfoil optimization methods to ultra-long flexible blades. Using this embodiment, the airfoil's operating range can be effectively broadened, blade performance improved, and turbine load fluctuations reduced. Compared to conventional design methods, the airfoil designed using the method in this embodiment performs better on ultra-long flexible blades, and its aerodynamic characteristics demonstrate good robustness.

[0111] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0112] Another aspect of this application provides a wind turbine airfoil optimization device for long, flexible blades, such as... Figure 6 As shown, it includes: a basic parameter acquisition module 2, used to acquire the basic parameters of the target airfoil; an initial airfoil determination module 4, used to determine the initial airfoil based on the basic parameters; a characteristic variable analysis module 6, used to analyze the initial airfoil into multiple geometric characteristic variables; an optimization parameter construction module 8, used to construct multiple optimization parameters characterizing the aerodynamic performance of the airfoil based on multiple geometric characteristic variables and basic parameters; an objective function construction module 10, used to construct an optimization objective function based on multiple optimization parameters; and an optimization solution module 12, used to optimize and solve multiple geometric characteristic variables based on the optimization objective function.

[0113] It should be noted that the aforementioned airfoil optimization device for long, flexible blades is used to perform... Figure 1 The wind turbine airfoil optimization method embodiment shown is similar in technical principle, technical problem solved and technical effect produced by both. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device and related descriptions can be referred to the content described in the embodiment of the method, and will not be repeated here.

[0114] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0115] Another aspect of this application provides a computer-readable storage medium.

[0116] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that executes the wind turbine airfoil optimization method for long flexible blades described in the above-described method embodiments. This program can be loaded and run by a processor to implement the aforementioned wind turbine airfoil optimization method for long flexible blades. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0117] Another aspect of this application provides a smart device.

[0118] In one embodiment of a smart device according to this application, the smart device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the methods described in any of the above embodiments. See Appendix Figure 7 , Figure 7 The image exemplarily illustrates a communication connection between memory 11 and processor 12 via a bus.

[0119] In some embodiments of this application, the smart device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in this application. Optionally, the smart device described in this application may be, but is not limited to, a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, in-vehicle device, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), augmented reality (AR) / virtual reality (VR) device, etc., and this application does not limit this.

[0120] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for optimizing wind turbine airfoils for long, flexible blades, characterized in that, The method includes: Obtain the basic parameters of the target airfoil; Determine the initial airfoil based on the aforementioned basic parameters; The initial airfoil is analyzed into multiple geometric feature variables; Based on the aforementioned multiple geometric feature variables and the aforementioned basic parameters, various optimization parameters characterizing the aerodynamic performance of the airfoil are constructed. Construct an optimization objective function based on the aforementioned various optimization parameters; The optimization objective function is used to optimize and solve the plurality of geometric feature variables; The basic parameters include the design Reynolds number, and the various optimization parameters include airfoil aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters. The various optimization parameters characterizing the airfoil's aerodynamic performance, constructed based on the multiple geometric characteristic variables and the basic parameters, include: The aerodynamic parameters of the airfoil are calculated based on the multiple geometric characteristic variables and the design Reynolds number. The aerodynamic parameters are used to construct the aerodynamic efficiency parameters, stall safety parameters, mild stall characteristic parameters, and aerodynamic performance stability parameters that characterize the aerodynamic performance of the airfoil. The expressions for the aerodynamic efficiency parameter, stall safety parameter, mild stall characteristic parameter, and aerodynamic performance stability parameter are as follows: f2＝α stall -α design Where f1 represents the aerodynamic efficiency parameter, s CL C represents the normalization coefficient. L C represents the lift coefficient. D α represents the drag coefficient, and α represents the angle of attack. D Indicates the working angle of attack range. f2 represents the average value; f2 represents the stall safety parameter, α stall Indicates the stall angle of attack, α design f3 represents the operating angle of attack; f3 represents the mild stall characteristic parameter; C L stall represents the lift coefficient at stall angle of attack, max(·) represents finding the maximum value; f4 represents the aerodynamic performance stability parameter, grad(·) represents finding the gradient.

2. The method according to claim 1, characterized in that, The step of constructing the optimization objective function based on the multiple optimization parameters includes: Obtain multiple weight coefficients, each of which corresponds one-to-one with the optimization parameters; The sum of the products of each weight coefficient and each optimization parameter is taken as the optimization objective function.

3. The method according to claim 2, characterized in that, The method further includes: The various optimization parameters are normalized to their order of magnitude.

4. The method according to claim 1, characterized in that, The basic parameters of the target airfoil include the maximum relative thickness, and the optimization solution of the multiple geometric feature variables based on the optimization objective function includes: Obtain the boundaries of the plurality of geometric feature variables; The constraints for the optimization solution are determined based on the boundaries of the multiple geometric feature variables and the maximum relative thickness. The plurality of geometric feature variables are optimized and solved according to the constraints and the optimization objective function.

5. The method according to claim 4, characterized in that, The process of determining the initial airfoil based on the basic parameters includes: Select an initial airfoil from the preset airfoil library based on the maximum relative thickness.

6. A wind turbine airfoil optimization device for long, flexible blades according to any one of claims 1 to 5, characterized in that, The device includes: The basic parameter acquisition module is used to acquire the basic parameters of the target airfoil. An initial airfoil determination module is used to determine the initial airfoil based on the basic parameters. The feature variable parsing module is used to resolve the initial airfoil into multiple geometric feature variables; An optimization parameter construction module is used to construct various optimization parameters characterizing the aerodynamic performance of the airfoil based on the multiple geometric feature variables and the basic parameters. The objective function construction module is used to construct an optimization objective function based on the various optimization parameters. The optimization solution module is used to optimize the solution of the plurality of geometric feature variables based on the optimization objective function.

7. A smart device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program that, when executed by the at least one processor, implements the method of any one of claims 1 to 5.

8. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the method of any one of claims 1 to 5.

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