Wind turbine airfoil shape optimization method, device and equipment for long flexible blade and medium

Abstract

The invention relates to the technical field of wind turbine airfoil optimization, particularly provides a wind turbine airfoil optimization method, device and equipment for a long flexible blade and a medium, and aims to solve the problem that a forming system and high-robustness wind turbine airfoil optimization design method for the long flexible blade is lacked. In order to achieve the purpose, the method comprises the steps that basic parameters of a target airfoil profile are obtained, an initial airfoil profile is determined according to the basic parameters, the initial airfoil profile is analyzed into multiple geometric characteristic variables, and multiple optimization parameters representing the aerodynamic performance of the airfoil profile are constructed based on the multiple geometric characteristic variables and the basic parameters. And constructing an optimization objective function according to the plurality of optimization parameters, and performing optimization solution on the plurality of geometric feature variables based on the optimization objective function. According to the method, the optimization objective function is constructed by using multiple optimization parameters, the adaptability of the optimized airfoil to complex working conditions is improved, the optimal solution is directly obtained by using the optimization algorithm, the optimization efficiency is high, and the aerodynamic characteristics of the airfoil have good robustness.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine airfoil optimization, and particularly to a wind turbine airfoil optimization method, device, equipment and medium for long flexible blades. Background Art

[0002] In recent years, wind power equipment has been developing rapidly towards large-scale and lightweight. The blade sizes of ultra-large units have reached over one hundred meters. Due to the increase in length, the flexibility and deformation degree of such blades also increase. During operation, a huge flexible deformation effect may occur, causing the actual working angle of attack of the airfoil to deviate from the designed angle of attack, and may even enter the stall state, resulting in problems such as poor performance of the wind turbine and extreme loads. Currently, conventional wind turbine airfoils cannot meet the requirements of ultra-long flexible blades, and there is a lack of a systematic and highly robust wind turbine airfoil optimization design method for the above-mentioned long flexible blades.

[0003] Correspondingly, there is a need in the art for a new wind turbine airfoil optimization solution for long flexible blades to solve the above problems. Summary of the Invention

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

[0005] In a first aspect, a wind turbine airfoil optimization method for long flexible blades is provided. The method includes: obtaining the basic parameters of a target airfoil; determining an initial airfoil according to the basic parameters; parsing 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 according to the multiple optimization parameters; and performing an optimization solution on the multiple geometric feature variables based on the optimization objective function.

[0006] In a technical solution of the above wind turbine airfoil optimization method for long flexible blades, the basic parameters include the design Reynolds number, and the multiple optimization parameters include the aerodynamic efficiency parameter, stall safety parameter, mild stall characteristic parameter, and aerodynamic performance stability parameter of the airfoil. Constructing multiple optimization parameters characterizing the aerodynamic performance of the airfoil based on the multiple geometric feature variables and the basic parameters includes: calculating the aerodynamic parameters of the airfoil according to the multiple geometric feature variables and the design Reynolds number; and constructing the aerodynamic efficiency parameter, stall safety parameter, mild stall characteristic parameter, and aerodynamic performance stability parameter characterizing the aerodynamic performance of the airfoil by using the aerodynamic parameters.

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

[0008]

[0009] f2 = α stall -α design

[0010]

[0011]

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

[0013] In a technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, constructing the optimization objective function according to the multiple optimization parameters includes: obtaining a plurality of weight coefficients, where the weight coefficients correspond one-to-one to the optimization parameters; taking the sum of the products of each weight coefficient and each optimization parameter as the optimization objective function.

[0014] In a technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the method further includes: performing magnitude normalization processing on the multiple optimization parameters.

[0015] In a technical solution of the above-mentioned wind turbine airfoil optimization method for long and flexible blades, the basic parameters of the target airfoil include the maximum relative thickness. Optimizing and solving the multiple geometric feature variables based on the optimization objective function includes: obtaining the boundaries of the multiple geometric feature variables; determining the constraint conditions for the optimization solution according to the boundaries of the multiple geometric feature variables and the maximum relative thickness; and performing optimization and solution on the multiple geometric feature variables according to the constraint conditions and the optimization objective function.

[0016] In one technical solution of the above wind turbine airfoil optimization method for long flexible blades, determining the initial airfoil according to the basic parameters includes: selecting an initial airfoil from a preset airfoil library according to the maximum relative thickness.

[0017] In a second aspect, there is provided a wind turbine airfoil optimization device for long flexible blades, the device including: a basic parameter acquisition module for acquiring basic parameters of a target airfoil; an initial airfoil determination module for determining an initial airfoil according to the basic parameters; a characteristic variable analysis module for analyzing the initial airfoil into a plurality of geometric characteristic variables; an optimization parameter construction module for constructing various optimization parameters characterizing the aerodynamic performance of the airfoil based on the plurality of geometric characteristic variables and the basic parameters; a target function construction module for constructing an optimization target function according to the various optimization parameters; and an optimization solution module for performing optimization solution on the plurality of geometric characteristic variables based on the optimization target function.

[0018] In a third aspect, there is provided an intelligent device, the intelligent device including at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in the first aspect or any corresponding technical solution thereof above is implemented.

[0019] In a fourth aspect, there is provided a computer-readable storage medium, which stores multiple program codes, and the program codes are adapted to be loaded and run by a processor to execute the method described in the first aspect or any corresponding technical solution thereof above.

[0020] One or more of the above technical solutions of the present application have at least one or more of the following beneficial effects:

[0021] In implementing the technical solution provided by the present application, an initial airfoil is determined according to the basic parameters of the target airfoil, the initial airfoil is analyzed into a plurality of geometric characteristic variables, then various optimization parameters characterizing the aerodynamic performance of the airfoil are constructed according to the plurality of geometric characteristic variables and the basic parameters, an optimization target function is constructed according to the various optimization parameters, and finally the plurality of geometric characteristic variables are optimized and solved according to the optimization target function to obtain an optimized airfoil. Since the present application uses various optimization parameters characterizing the aerodynamic performance of the airfoil when constructing the optimization target function, the complex and changeable operating conditions of the ultra-long flexible blades are fully considered, thereby improving the adaptability of the optimized airfoil to complex conditions and providing a systematic and highly robust wind turbine airfoil optimization design method for long flexible blades.

[0022] In implementing the technical solution provided by the present application, an optimization objective function is jointly constructed by combining the aerodynamic efficiency parameter, the stall safety parameter, the gradual stall characteristic parameter, and the aerodynamic performance stability parameter, achieving the purpose of improving the adaptability of the airfoil to complex working conditions. By reducing the performance fluctuation and the stall probability, the effect of making the optimized airfoil better match the ultra-long flexible blade is achieved.

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

[0024] In implementing the technical solution provided by the present application, by performing an order-of-magnitude normalization process on multiple optimization parameters, the effect of reducing the numerical difference between multiple optimization objective functions and improving the convergence speed and stability of the optimization algorithm is achieved.

[0025] In implementing the technical solution provided by the present application, the constraint conditions for the optimization solution are determined according to the boundaries of multiple geometric feature variables and the maximum relative thickness, achieving the purpose of ensuring that the optimization results meet the actual requirements, avoiding invalid solutions, reducing the search space of the optimization problem, and improving the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Referring to the accompanying drawings, the disclosure of the present application will become more readily understood. It is easily understood by those skilled in the art that these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application. Among them:

[0027] Figure 1 is a schematic diagram of the main step flow of an airfoil optimization method for a wind turbine facing long flexible blades according to an embodiment of the present application;

[0028] Figure 2 is a schematic diagram of the comparison of the airfoil shapes before and after optimization according to an embodiment of the present application;

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

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

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

[0032] Figure 6 is a schematic diagram of the main structural block diagram of an airfoil optimization device for a wind turbine facing long flexible blades according to an embodiment of the present application;

[0033] Figure 7 It is a schematic diagram of the connection relationship between the processor and the memory of an intelligent device according to an embodiment of the present application.

[0034] Reference numerals:

[0035] 11: Memory; 12: Processor. Detailed implementation manners

[0036] Some implementation manners of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0037] In the description of the present application, the terms "first", "second", etc. are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The terms "install", "connect", and "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can also be the communication inside two elements. It can be a wireless connection or a wired connection.

[0038] In addition, a "module" and a "processor" can include hardware, software, or a combination of both. A module can include a hardware circuit, various appropriate sensors, communication ports, memories, and can also include a software part, such as program code, or can be a combination of software and hardware. A processor can be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, in hardware, or in a combination of both. A computer-readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and so on.

[0039] In addition, if the meaning of "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 where both A and B are satisfied simultaneously. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or is 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 required by this application. The terms "at least one of A or B" or "at least one of A and B" have a meaning similar to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" and "this" can also include the plural form.

[0040] The airfoil is the most basic element that makes up the wind turbine blade. The quality of the airfoil's aerodynamic performance directly determines the output performance and load characteristics of the blade, and has an important impact on the overall performance and service life of the whole machine. Therefore, the airfoil optimization design is the basic method and necessary means to ensure the wind energy conversion efficiency and operation reliability of the wind turbine. In recent years, wind power equipment has developed rapidly towards large-scale and lightweight. The blade size of ultra-large units has reached more than 100 meters. Such blades have a huge flexible deformation effect during operation, which may lead to problems such as poor wind 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 the art for a new high-robustness wind turbine airfoil design method suitable for long flexible blades to solve the above problems.

[0041] Refer to the appendix Figure 1 , Figure 1 is a schematic diagram of the main step flow of a wind turbine airfoil optimization method for long flexible blades according to an embodiment of the present application. As Figure 1 shown, this method mainly includes the following steps S2 to step 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 chordwise position, the design Reynolds number, the working angle of attack range, the blunt trailing edge thickness, etc.

[0044] Step S4, determine the initial airfoil according to the basic parameters.

[0045] In this embodiment, the initial airfoil is selected from a preset airfoil library according to the basic parameters in step S2. The preset airfoil library is, for example, the NACA airfoil database, the DU airfoil database, etc.

[0046] In one implementation, the initial airfoil can be selected from the preset airfoil library according to the maximum relative thickness.

[0047] Step S6: Parse the initial airfoil into multiple geometric feature variables.

[0048] In this embodiment, the parametric method is used to parse the initial airfoil selected in step S6 into multiple geometric feature variables. Among them, 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 embodiment, the mathematical expression of the CST method is:

[0050] y(x) = C(x)·S(x) + x·Δy TE

[0051] where x represents the abscissa of the airfoil, and y(x) represents the ordinate of the upper or lower surface of the airfoil; C(x) is the class function, used to represent the type of airfoil, usually C(x) = x N1 ·(1 - x) N2 , for a general airfoil, set N1 to 0.5 and N2 to 1; S(x) is the shape function, used to accurately describe the geometric shape of the airfoil, usually where A i is an adjustable parameter, and S i is the Bernstein function; Δy TE represents the thickness of the blunt trailing edge.

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

[0053] In one embodiment, through the above CST method, the initial airfoil can be parsed into multiple geometric feature variables (such as 5 adjustable parameters on the upper surface and 5 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 characterizing the aerodynamic performance of the airfoil based on multiple geometric feature variables and basic parameters.

[0055] In this embodiment, various optimization parameters characterizing the aerodynamic performance of the airfoil are constructed according to the multiple geometric feature variables parsed in step S6 and the basic parameters in step S2.

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

[0057] Step S10: Construct an optimization objective function according to the various optimization parameters.

[0058] In this embodiment, an optimization objective function of the airfoil is constructed according to multiple optimization parameters in step S8.

[0059] In one embodiment, the optimization objective function of the airfoil 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, an optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, etc.) is 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 by this application, an initial airfoil is determined according to the basic parameters of the target airfoil, the initial airfoil is parsed into multiple geometric feature variables, then multiple optimization parameters characterizing the aerodynamic performance of the airfoil are constructed according to the above multiple geometric feature variables and the basic parameters, an optimization objective function is constructed according to the multiple optimization parameters, and finally multiple geometric feature variables are optimized and solved according to the optimization objective function to obtain the optimized airfoil. Since this application uses multiple optimization parameters characterizing the aerodynamic performance of the airfoil when constructing the optimization objective function, fully considering the complex and changeable operating conditions of the ultra-long flexible blade, the adaptability of the optimized airfoil to complex conditions is improved, providing a systematic and highly robust wind turbine airfoil optimization design method for long flexible blades.

[0063] The above steps S8 to S12 are further described below.

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

[0065] Step S82: Calculate the aerodynamic parameters of the airfoil according to multiple geometric feature variables and the design Reynolds number.

[0066] In this embodiment, numerical simulation is used to calculate the aerodynamic parameters of the airfoil according to the design Reynolds number and multiple geometric feature variables, where the numerical simulation is, for example, the panel method based on potential flow theory, and the aerodynamic parameters are, for example, the lift coefficient curve and the drag coefficient curve.

[0067] Step S84: Use the aerodynamic parameters to construct the aerodynamic efficiency parameter, stall safety parameter, gentle stall characteristic parameter, and aerodynamic performance stability parameter characterizing the aerodynamic performance of the airfoil.

[0068] In this embodiment, the following formulas are used to calculate the aerodynamic efficiency parameter, stall safety parameter, mild stall characteristic parameter, and aerodynamic performance stability parameter:

[0069]

[0070] f2 = α stall -α design

[0071]

[0072]

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

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

[0075] In an implementation manner of the embodiment of the present application, the above step S10 may further include the following steps S102 and S104:

[0076] Step S102, obtain a plurality of weight coefficients, and the weight coefficients correspond to the optimization parameters one by one.

[0077] In this embodiment, the weight coefficients corresponding to the optimization parameters can be determined according to 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 make the aerodynamic performance of the optimized airfoil optimal, f1 and f2 need to be maximized, and f3 and f4 need to be minimized. At this time, the target can be unified by adjusting the weight direction.

[0078] Step S104: Use the sum of the products of each weight coefficient and each optimization parameter 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, by constructing the optimization objective function according to the sum of the products of each weight coefficient and each optimization parameter, the importance of multiple optimization parameters is balanced and dynamically adjusted according to actual application requirements, so as to adapt to different optimization scenarios.

[0081] In an alternative embodiment, before the above step S102, the following step S101 is further included:

[0082] Step S101: Perform magnitude normalization processing on multiple optimization parameters.

[0083] In this embodiment, since the dimensions and value ranges of different optimization parameters may vary greatly, direct addition may cause some optimization parameters to be ignored. Therefore, in this embodiment, magnitude normalization processing is performed on multiple optimization parameters, so that each optimization parameter can be compared on the same scale (magnitude), and the optimization result is 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 an alternative embodiment, the optimization objective function F is as follows:

[0085]

[0086] where s i represents the i-th magnitude normalization coefficient, w i represents the i-th weight coefficient, f i represents the i-th type of optimization parameter, n represents the total number of optimization parameters, and the optimization parameters at least include the aerodynamic efficiency parameter f1, stall safety parameter f2, and gradual stall characteristic parameter f3, and aerodynamic performance stability parameter f4 of the airfoil.

[0087] In an embodiment of the embodiment of this application, the above step S12 can be further included the following 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 according to the parameters of the initial airfoil. For example, increasing and decreasing the initial airfoil parameters by 10% are used as the upper and lower bounds of the geometric feature variables.

[0090] Step S124: Determine the constraint conditions for the optimization solution according to 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 determined in step S122 and the maximum relative thickness are used as the constraint conditions for the optimization solution 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 constraint conditions and the optimization objective function.

[0093] In this embodiment, an optimization algorithm is used to optimize and solve multiple geometric feature variables according to the above constraint conditions and the optimization objective function. The optimization algorithm can be selected according to the optimization objective function, and this embodiment does not make specific limitations on this.

[0094] In an example of an application scenario of the present application, a high-robustness wind turbine airfoil design method suitable for long and flexible blades is provided, including 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 chordwise position, the design Reynolds number, the working angle of attack range, and the blunt trailing edge thickness.

[0096] In this embodiment, the maximum relative thickness is 25%, the chordwise position of the maximum relative thickness is 32%, the design Reynolds number is 3×10 6 , the working angle of attack range is 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 according to the maximum relative thickness of the target airfoil.

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

[0099] Step 3: Analyze the airfoil into multiple geometric feature variables by using the CST method.

[0100] Step 4: Parametrically characterize the aerodynamic performance of the airfoil based on the design Reynolds number and multiple geometric feature variables, and construct the following optimization objective function F:

[0101]

[0102] Among them, f iincluding the aerodynamic efficiency parameter f1, the airfoil stall safety parameter f2, the airfoil gentle stall characteristic parameter f3, and the airfoil aerodynamic performance stability parameter f4 within the working angle of attack range of the airfoil. The specific calculation formula of f i can refer to the content described in other embodiments of this application and will not be elaborated here.

[0103] In this embodiment, f1 is used to represent the maximum lift coefficient and lift-drag ratio within the working angle of attack range. By maximizing f1, better performance can be maintained under fluctuating angle of attack changes, and it has better adaptability to complex working conditions; f2 represents stall safety. By maximizing f2, the space from the working angle of attack to the stall angle of attack is expanded, and the stall risk is reduced, which is more compatible with ultra-long flexible blades; f3 represents the gentle stall characteristic. By minimizing f3, the lift drop after the airfoil stalls is reduced, and the impact caused by stall is minimized; f4 represents the airfoil aerodynamic performance stability parameter within the working angle of attack range, that is, the slope of the lift coefficient change. By minimizing f4, the load under fluctuating angle of attack changes can be better reduced, which is more compatible with ultra-long flexible blades. In summary, the method provided in this embodiment can adapt to the complex and changeable working conditions faced by ultra-long flexible blades, cope with the frequent changes in the angle of attack, reduce the stall risk, enhance the overall aerodynamic performance of the airfoil under fluctuating angle of attack, and reduce the load caused by the fluctuating angle of attack.

[0104] Optionally, the order-of-magnitude normalization coefficient s i and the weight coefficient w i take the values shown in Table 1, and set s CL = 100.

[0105] Table 1 Coefficient Value Table <![CDATA[f1]]> <![CDATA[f2]]> <![CDATA[f3]]> <![CDATA[f4]]> Order of magnitude normalization coefficient 1 20 2500 1500 Weight coefficient 0.3 0.1 0.2 0.4

[0106] Step 5: According to Step 3 and Step 4, select an optimization algorithm to optimize the shape of the initial airfoil to obtain an optimized high-robustness wind turbine airfoil suitable for long flexible blades. Specifically, in the optimization algorithm, set F as the optimization objective function, and multiple geometric feature variables as the optimization variables. According to the airfoil structure and geometric compatibility requirements, set the upper and lower bounds of multiple geometric feature variables and the maximum relative thickness of the airfoil as constraint conditions.

[0107] In a specific embodiment, a genetic algorithm is selected for optimization calculation, and the upper and lower bounds of the airfoil geometric feature variables are set to increase and decrease the initial airfoil parameters by 10%.

[0108] In this embodiment, the comparison between the shape of the optimized high-robustness wind turbine airfoil (New Airfoil) and the initial airfoil (DU 91-W2-250) is as Figure 2 shown, and the comparison of the lift-drag ratio curve, lift coefficient curve, and drag coefficient curve between the two is respectively as Figure 3 ,Figure 4 and Figure 5 as shown. By Figures 3 to 5 It can be seen that compared with the original airfoil, the optimized airfoil in this embodiment has a lift-to-drag ratio increased by 0.4% within the working angle of attack range, a lift coefficient increased by 6.3%, and better aerodynamic performance; the distance between the working angle of attack and the stall angle of attack increases by 0.8°, the gentle stall characteristic coefficient decreases by 81.3%, and the stall risk and the resulting output decrease and load fluctuation are significantly reduced; the slope of the lift coefficient change decreases by 14.0%, effectively reducing the load change caused by the change of the angle of attack. Therefore, the optimized airfoil obtained by the method of this embodiment has better aerodynamic performance and higher robustness, and can better cope with the complex operating characteristics of ultra-long flexible blades.

[0109] In this embodiment, an optimization algorithm is used to conveniently and simply obtain the wind turbine airfoil with the best aerodynamic performance, and the overall optimal solution can be directly obtained, with high design efficiency, avoiding the complexity and uncertainty brought by repeated manual adjustment. Through a new aerodynamic performance evaluation method, the designed airfoil has excellent performance such as excellent aerodynamic efficiency, small performance fluctuation, low stall probability, and gentle stall characteristics within the working angle of attack range. It solves the problem that the existing airfoil optimization methods are less applicable to ultra-long flexible blades. Using the embodiments of this application can effectively broaden the working range of the airfoil, improve the working performance of the blade, and reduce the load fluctuation of the unit. Compared with the conventional design method, the airfoil designed by the method in this embodiment has better performance on ultra-long flexible blades, and the aerodynamic characteristics of the airfoil have good robustness.

[0110] It should be noted that although the above steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this application, different steps do not necessarily have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent technical solutions to the technical solutions described in this application, and therefore will also fall within the protection scope of this application.

[0111] On the other hand, this application also provides a wind turbine airfoil optimization device for long flexible blades, as Figure 6 shown, including: a basic parameter acquisition module 2 for acquiring the basic parameters of the target airfoil; an initial airfoil determination module 4 for determining the initial airfoil according to the basic parameters; a characteristic variable analysis module 6 for analyzing the initial airfoil into multiple geometric characteristic variables; an optimization parameter construction module 8 for constructing various optimization parameters characterizing the aerodynamic performance of the airfoil based on the multiple geometric characteristic variables and the basic parameters; a target function construction module 10 for constructing an optimization target function according to the various optimization parameters; and an optimization solution module 12 for performing optimization solution on the multiple geometric characteristic variables based on the optimization target function.

[0112] It should be noted that the above-mentioned wind turbine airfoil optimization device for long flexible blades is used to execute Figure 1 the embodiments of the wind turbine airfoil optimization method for long flexible blades shown in the figure. The technical principles, technical problems solved and technical effects produced by the two are similar. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the device can refer to the content described in the embodiments of the method, which will not be elaborated here.

[0113] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiments of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code, etc.

[0114] On the other hand, the present application also provides a computer-readable storage medium.

[0115] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for executing the wind turbine airfoil optimization method for long flexible blades in the above-mentioned method embodiments. The program can be loaded and run by a processor to implement the above-mentioned wind turbine airfoil optimization method for long flexible blades. For the convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present application is a non-transitory computer-readable storage medium.

[0116] On the other hand, the present application also provides an intelligent device.

[0117] In an embodiment of an intelligent device according to the present application, the intelligent device can include at least one processor; and a memory communicatively connected to at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by at least one processor, the method described in any of the above embodiments is implemented. Refer to the attached Figure 7 , Figure 7 It is exemplarily shown in the figure that the memory 11 and the processor 12 are communicatively connected through a bus.

[0118] In some embodiments of the present application, the intelligent device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in the present application. Optionally, the intelligent device described in the present application may be, but is not limited to, a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, a vehicle-mounted device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), an augmented reality (AR) / virtual reality (VR) device, etc., and the embodiments of the present application do not limit this.

[0119] So far, the technical solution of the present application has been described in conjunction with one embodiment shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present 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 protection scope of the present application.

Claims

1. A wind turbine airfoil optimization method for long flexible blades, characterized in that: The method comprises: Obtain the basic parameters of the target airfoil; Determine an initial airfoil according to the basic parameters; Resolving the initial airfoil into a plurality of geometric characteristic variables; Constructing a plurality of optimization parameters characterizing 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 multiple optimization parameters; The plurality of geometric feature variables are optimized and solved based on the optimization objective function.

2. The method according to claim 1, characterized in that: The basic parameters include a design Reynolds number, the multiple optimization parameters include an aerodynamic efficiency parameter, a stall safety parameter, a gentle stall characteristic parameter, and an aerodynamic performance stability parameter of the airfoil, and the multiple optimization parameters characterizing the aerodynamic performance of the airfoil are constructed based on the multiple geometric characteristic variables and the basic parameters, including: Calculate aerodynamic parameters of the airfoil according to the plurality of geometric characteristic variables and the design Reynolds number; The aerodynamic parameters are used to construct the aerodynamic efficiency parameters, stall safety parameters, gentle stall characteristic parameters and aerodynamic performance stability parameters that characterize the aerodynamic performance of the airfoil.

3. The method according to claim 2, characterized in that The expressions of the aerodynamic efficiency parameter, stall safety parameter, gentle stall characteristic parameter and aerodynamic performance stability parameter are as follows: f2＝α stall -α design Wherein, f1 represents the aerodynamic efficiency parameter, s CL represents the normalization coefficient, C L is the lift coefficient, C D represents the drag coefficient, α represents the angle of attack, and α D represents the working angle of attack range, represents the average value; f2 represents the stall safety parameter, α stall represents the stall angle of attack, α design represents the working angle of attack; f3 represents the gentle stall characteristic parameter, C Lstall represents the lift coefficient at the stall angle of attack, max(·) represents the maximum value; f4 represents the aerodynamic performance stability parameter, and grad(·) represents the gradient.

4. The method according to any one of claims 1 to 3, characterized in that The constructing of the optimization objective function according to the multiple optimization parameters comprises: Acquire a plurality of weight coefficients, wherein the weight coefficients correspond one-to-one to the optimization parameters; The sum of the products of each weight coefficient and each optimization parameter is used as the optimization objective function.

5. The method according to claim 4, characterized in that The method further comprises: The multiple optimization parameters are normalized in magnitude.

6. The method according to claim 1, characterized in that The basic parameters of the target airfoil include the maximum relative thickness, and the optimizing and solving the multiple geometric characteristic variables based on the optimization objective function includes: Obtaining boundaries of the plurality of geometric feature variables; Determining the constraint conditions of the optimization solution according to the boundaries of the plurality of geometric feature variables and the maximum relative thickness; The plurality of geometric feature variables are optimized and solved according to the constraint conditions and the optimization objective function.

7. The method according to claim 6, characterized in that Determining the initial airfoil according to the basic parameters comprises: An initial airfoil is selected from a preset airfoil library according to the maximum relative thickness.

8. A wind turbine airfoil optimization device for long flexible blades, characterized in that: The device comprises: A basic parameter acquisition module is used to obtain the basic parameters of the target airfoil; An initial airfoil determination module, used to determine the initial airfoil according to the basic parameters; A characteristic variable analysis module, used for analyzing the initial airfoil into a plurality of geometric characteristic variables; An optimization parameter construction module, used to construct a plurality of optimization parameters characterizing the aerodynamic performance of the airfoil based on the plurality of geometric characteristic variables and the basic parameters; An objective function construction module, used to construct an optimization objective function according to the multiple optimization parameters; The optimization solution module is used to optimize and solve the multiple geometric feature variables based on the optimization objective function.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program, and when the computer program is executed by the at least one processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to execute the method according to any one of claims 1 to 7.

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