Wind turbine blade design method apparatus and device, medium, and product

Abstract

The present application relates to the field of wind turbine blade design, and discloses a wind turbine blade design method, apparatus and device, a medium, and a product. The wind turbine blade design method comprises: acquiring inputted configuration data and value ranges respectively corresponding to a plurality of design variables; selecting variable values from the value ranges respectively corresponding to the plurality of design variables; performing blade shape analysis on the basis of the configuration data and the variable values to obtain candidate shape data and performance data of a blade; evaluating, by means of an objective function and a constraint condition, whether the performance data satisfies a preset condition; and if not, selecting variable values of the design variables again, and performing blade shape analysis again to obtain candidate shape data and performance data, until the performance data satisfies the preset condition or the number of iterations reaches a preset number of times, and determining candidate shape data obtained by means of the last analysis as final shape data of the blade.

Description

Wind turbine blade design methods, devices, equipment, media and products

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411824185.8, filed on December 11, 2024, entitled “Wind Turbine Blade Design Method, Apparatus, Equipment, Medium and Product”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of wind turbine blade design, and in particular relates to a wind turbine blade design method, device, equipment, medium and product. Background Technology

[0004] Currently, in wind turbine blade engineering design, blade design can be carried out based on the analytical design method of the classical blade element momentum theory (BEM). The analytical design method is a reverse design method, which mainly includes: based on the optimal wind energy conversion efficiency (optimal power coefficient Cp) assumption of BEM, under a specific airfoil selection and its spanwise distribution (relative thickness distribution, lift and drag coefficient distribution), the blade chord length and twist angle distribution under a certain design tip speed ratio condition are calculated, thereby obtaining the aerodynamic layout characteristics of the blade.

[0005] However, analytical design methods have the following drawbacks: 1) Single-condition design (tip speed ratio) is difficult to handle multi-condition problems; 2) Empirical design: The selection and adjustment of design variables such as airfoil thickness layout, lift distribution, and tip speed ratio are heavily dependent on the designer's experience, often requiring repeated trial and error, making it difficult to obtain the optimal solution in the global design space. Summary of the Invention

[0006] This application provides a wind turbine blade design method, device, equipment, medium, and product that can handle multi-condition problems and obtain the optimal solution in the global design space.

[0007] In a first aspect, embodiments of this application provide a wind turbine blade design method, including:

[0008] Obtain the input configuration data and the value range of each of the multiple design variables. The configuration data includes wind condition data, wind turbine data for blade adaptation, blade data, model-related data, and airfoil data.

[0009] Select the variable values ​​of the design variables from the respective value ranges of multiple design variables;

[0010] Based on the configuration data and selected variable values, the blade shape is analyzed to obtain candidate shape data and performance data of the blade.

[0011] Evaluate whether the performance data meets the preset conditions based on the objective function and constraints;

[0012] If the conditions are not met, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables. The blade shape analysis is then performed based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade. This process continues until the performance data meets the preset conditions or the number of iterations reaches the preset number. Finally, the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0013] In some embodiments of this application, multiple design variables include the design tip speed ratio and the design lift coefficient, induced trimming factor, and relative thickness at multiple different blade spanwise positions. The candidate shape data of the blade includes the twist angle distribution and chord length distribution of each blade element segment. The induced trimming factor is the percentage deviation between the actual design application induced factor of the blade element segment and the induced factor under the assumption of the theoretical optimal power coefficient.

[0014] In some embodiments of this application, multiple different blade spanwise positions include positions where the blade spanwise is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

[0015] In some embodiments of this application, the range of design lift coefficients at multiple different blade spanwise positions is determined in the following manner:

[0016] Based on the design lift coefficient distribution of the entire blade and the number of leaf element units to be discretized, the lift coefficient value at the corresponding leaf element position is generated according to the predetermined leaf element discretization criterion.

[0017] In some embodiments of this application, blade shape analysis is performed based on configuration data and selected variable values ​​to obtain candidate blade shape data and performance data, including:

[0018] Generate aerodynamic data tables for each airfoil element of the blade based on the configuration data and the selected variable values.

[0019] Based on the aerodynamic data table, the twist angle distribution and chord length distribution of each blade element segment were obtained through analysis.

[0020] By integrating the torsion angle distribution and chord length distribution of all leaf element segments, candidate leaf shape data are obtained;

[0021] Based on the candidate shape data of the blade, the performance of the blade is analyzed to obtain the blade performance data.

[0022] In some embodiments of this application, before integrating the twist angle distribution and chord length distribution of all blade elements to obtain candidate blade shape data, the wind turbine blade design method further includes:

[0023] The torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment in each leaf element segment are simplified to obtain the simplified torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment. The simplification process includes linearization and nonlinear pruning.

[0024] In some embodiments of this application, the performance data includes aerodynamic performance data and geometric feature data. The performance data is evaluated to determine whether it meets preset conditions using an objective function and constraints, including:

[0025] Evaluate whether the aerodynamic performance data and geometric feature data meet the first preset conditions;

[0026] If the aerodynamic performance data and geometric feature data meet the first preset conditions, then evaluate whether the aerodynamic performance data and geometric feature data meet the second preset conditions.

[0027] In some embodiments of this application, the aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity.

[0028] The first preset condition includes that at least one of absolute thickness, stall margin, power coefficient, and blade root load meets the preset constraint value. The stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack. The second preset condition includes that the function value of a multi-objective function with at least one of power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics, and at least one of absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity as the objective is maximized.

[0029] Secondly, embodiments of this application provide a wind turbine blade design device, comprising:

[0030] The acquisition module is used to acquire the input configuration data and the value range of each of the multiple design variables. The configuration data includes wind condition data, wind turbine whole machine data of blade adaptation, blade data, model-related data and airfoil data.

[0031] The selection module is used to select the value of a design variable from the value ranges corresponding to multiple design variables.

[0032] The parsing module is used to analyze the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade.

[0033] The evaluation module is used to evaluate whether the performance data meets the preset conditions based on the objective function and constraints.

[0034] The optimization module is used to select the variable values ​​of the design variables from the respective value ranges of multiple design variables again when the conditions are not met, and return the blade shape analysis based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0035] Thirdly, embodiments of this application provide a wind turbine blade design device, the device including: a processor and a memory storing computer program instructions;

[0036] The wind turbine blade design method of any of the above embodiments is implemented when the processor executes computer program instructions.

[0037] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the wind turbine blade design method of any of the above embodiments.

[0038] Fifthly, embodiments of this application provide a computer program product, wherein when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs the wind turbine blade design method of any of the above embodiments.

[0039] According to the wind turbine blade design method, apparatus, equipment, medium and product provided in the embodiments of this application, the blade shape can be analyzed based on configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single-condition design of related technologies, the solution of this application can handle multi-condition problems, and each design variable has its own value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 is a flowchart illustrating the wind turbine blade design method provided in an embodiment of this application;

[0042] Figure 2 is a schematic diagram of the relative thickness layout and lift coefficient layout of the airfoil;

[0043] Figure 3 shows the blade chord length and twist angle distribution obtained from conventional analytical design;

[0044] Figure 4 is a framework diagram of the wind turbine blade design optimization method;

[0045] Figure 5 shows a comparison of chord lengths in the case design results;

[0046] Figure 6 is a comparison chart of the load distribution of the case design results;

[0047] Figure 7 is a schematic diagram of the wind turbine blade design device provided in an embodiment of this application;

[0048] Figure 8 is a schematic diagram of the structure of the wind turbine blade design equipment provided in the embodiment of this application. Detailed Implementation

[0049] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0051] Currently, in wind turbine blade engineering design, blade design can be carried out based on the analytical design method of the classical blade element momentum theory (BEM). The analytical design method is a reverse design method, which mainly includes: based on the optimal wind energy conversion efficiency (optimal power coefficient Cp) assumption of BEM, under a specific airfoil selection and its spanwise distribution (relative thickness distribution, lift and drag coefficient distribution), the blade chord length and twist angle distribution under a certain design tip speed ratio condition are calculated, thereby obtaining the aerodynamic layout characteristics of the blade.

[0052] However, analytical design methods have the following drawbacks: 1) Single-condition design (tip speed ratio) is difficult to handle multi-condition problems; 2) Empirical design: the selection and adjustment of design variables such as airfoil thickness layout, lift distribution, and tip speed ratio heavily rely on the designer's experience, often requiring repeated trial and error, making it difficult to obtain the optimal solution in the global design space. Furthermore, the analytical results of analytical design methods represent the optimal Cp design under the ideal induction factor distribution, but may not meet low-load requirements, making further efficiency-load control impossible.

[0053] In this field, direct numerical optimization (DFO) methods can also be used for blade design. DFO is a forward design method, which mainly includes: parametrically characterizing the aerodynamic shape characteristics of the blade (such as chord length, twist angle, thickness distribution, etc.), driving the change of design parameters through mathematical optimization algorithms to generate a new blade shape, evaluating aerodynamic efficiency, load characteristics, and geometric structural characteristics, and iterating repeatedly until the performance of the new shape meets the design objectives. DFO has the following disadvantages: This method generally optimizes the macroscopic aerodynamic characteristics of wind turbine blades (such as power coefficient Cp, power generation, ultimate load, etc.), and it is difficult to control the operating load characteristics and aerodynamic risks (such as load distribution balance, local stall risk, etc.) of different blade elements in the spanwise direction of the blade.

[0054] To address the aforementioned issues, embodiments of this application provide a wind turbine blade design method, apparatus, equipment, medium, and product that can handle multiple operating conditions and obtain the optimal solution in the global design space. Furthermore, it can also achieve dual design control of macroscopic blade performance indicators and local aerodynamic loads.

[0055] Figure 1 is a flowchart illustrating the wind turbine blade design method provided in an embodiment of this application;

[0056] The following describes the wind turbine blade design method according to an embodiment of this application, with reference to Figure 1. The wind turbine blade design method includes the following steps S110-S150:

[0057] S110, obtain the input configuration data and the value range of each of the multiple design variables; the configuration data includes wind condition data, wind turbine unit data adapted to the blades, blade data, model-related data, and airfoil data;

[0058] S120, Select the variable value of the design variable from the value range corresponding to each of the multiple design variables;

[0059] S130: Based on the configuration data and the selected variable values, the blade shape is analyzed to obtain candidate shape data and performance data of the blade.

[0060] S140 evaluates whether the performance data meets the preset conditions based on the objective function and constraints.

[0061] S150, if the conditions are not met, select the variable values ​​of the design variables from the value ranges corresponding to the multiple design variables again, and return the blade shape analysis based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and determine the candidate shape data obtained from the last analysis as the final shape data of the blade.

[0062] According to the wind turbine blade design method provided in the embodiments of this application, the blade shape can be analyzed based on configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single-condition design of related technologies, the solution of this application can handle multi-condition problems, and each design variable has its own value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained.

[0063] Regarding the aforementioned S110, the configuration data includes: a) wind condition data, such as air density, annual average wind speed, turbulence intensity, wind shear index, wind speed probability distribution, etc.; b) wind turbine unit data adapted to the blades, such as the number of blades, rated power, rotor diameter, hub radius, tower height, rotor tilt and elevation angles, rotor speed range, cut-in and cut-out wind speeds, control type, etc.; c) blade data, such as blade root pitch circle diameter, maximum chord length; d) model-related data, such as the number of blade elements in the BEM model, the maximum number of iterations of the induction factor, the iteration tolerance of the induction factor, etc.; e) airfoil data, including airfoil geometric profile data and aerodynamic data. Among them, the airfoil geometric profile data is usually a two-dimensional coordinate with a certain format, while the aerodynamic data needs to provide complete aerodynamic coefficients (lift coefficient, drag coefficient, moment coefficient, etc.) in polar coordinates within the 360° angle of attack range.

[0064] Selecting airfoil data, or choosing a "base airfoil," involves considering the blade aerodynamic layout as a superposition of an infinite number of airfoil elements with varying thicknesses, chord lengths, and twist angles along a specific axis. The "base airfoil" is a finite family of airfoils with different relative thicknesses, encompassing the relative thickness distribution of the airfoils included in the target blade. The shapes and aerodynamic data of the infinite number of airfoil elements constituting the blade are generated from the base airfoil using specific algorithms, such as linearized or nonlinear interpolation algorithms. There are certain principles for selecting the base airfoil. Given a sufficient number of alternative airfoil families, a suitable airfoil family should be selected based on the specific blade design problem. For example, for wind turbine blades operating in low-wind-speed, high-turbulence, and high-wind-shear regions, an airfoil family with a relatively smooth stall should be selected; for wind turbines operating in environments prone to blade surface contamination and roughening, an airfoil family with lower sensitivity to leading-edge roughness should be selected.

[0065] In some embodiments of this application, multiple design variables include the design tip speed ratio and the design lift coefficient, induced trimming factor, and relative thickness at multiple different blade spanwise positions. The candidate shape data of the blade includes the twist angle distribution and chord length distribution of each blade element segment. The induced trimming factor is the percentage deviation between the actual design application induced factor of the blade element segment and the induced factor under the assumption of the theoretical optimal power coefficient.

[0066] Specifically, the analytical design method relied upon in this application is based on the BEM model. A complete blade is divided into a finite number of blade segments (blade elements). The shape parameters (chord length, twist angle, etc.) of a given blade element segment are obtained by solving for the parameters of that segment. After obtaining the shape parameter points of all blade element segments, the overall blade shape distribution is obtained. Design variables include the design tip speed ratio, the distribution of inducible factors (inducible factors of blade element segments at different positions along the blade span, including axial and tangential inducible factors), the relative thickness distribution of the blade element airfoil, and the aerodynamic characteristics of the blade element airfoil (including lift coefficient, drag coefficient, and their corresponding angle of attack).

[0067] Of the design variables mentioned above, except for the design tip speed ratio, all other design variables can be considered as numerical distributions along the blade span. The actual number of each design variable (such as the axial induction factor) depends on the number of blade element segments obtained by discretizing the blade; the more blade element segments the blade is divided into, the more actual design variables there are for that design variable. A larger number of blade element segments can improve the accuracy of blade aerodynamic analysis, but it increases computational cost. Generally, for blades of hundreds of meters in length, more than 50 blade element segments are required. If all design variables of all blade segments are used as design variables, the design variable matrix of the optimization problem will undoubtedly become too large and the design space too complex. To simplify the problem, this application adopts a method of defining the distribution curve of the design variable points at a limited number of specific blade spanwise positions to achieve parameterization of each design variable. The specific blade element position points selected in this application are 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, a total of 8 relative blade spanwise positions. Figure 2 shows a schematic diagram of the airfoil's relative thickness and lift coefficient layout. Combined with Figure 2, the definition points and control distribution curves of the design lift coefficient and relative thickness are given. The spline curves defined by the lift coefficient values ​​at the eight spanwise positions provide the design lift coefficient distribution for the entire blade. Furthermore, the lift coefficient values ​​at the corresponding blade element positions can be generated according to a certain blade element discretization criterion based on the required number of discretized blade element segments. This method effectively reduces the number of design variables in the optimization problem (in the example given in the figure, control is achieved through eight blade spanwise position definition points, but the blade root is a cylindrical segment and is usually set to a fixed value. This application uses a fixed blade spanwise position (the horizontal axis coordinate of the definition point) and only uses the design variable values ​​(the vertical axis coordinate of the definition point) as variables; therefore, there are seven variables related to the lift coefficient). The same method is used to define other design variables as parameters.

[0068] In this application, the design lift coefficient, drag coefficient, and design angle of attack are one-to-one correspondences for a specific airfoil. Once the design lift coefficient distribution is defined, the corresponding drag coefficient distribution and design angle of attack distribution can be obtained based on the aerodynamic characteristics of the airfoil, without the need for redundant definitions. Therefore, this application uses the airfoil's design lift coefficient as the main design variable for parameterization, and does not parameterize the drag coefficient and angle of attack distribution, in order to reduce the number of design variables.

[0069] In this application, the parameterized distribution of the induction factor is controlled using an additional factor approach. Classical blade analytical design is based on the optimal Cp assumption, which states that the axial induction factor of each blade element segment along the entire blade span satisfies the ideal conditions of momentum theory (a simplified solution is that the ideal axial induction factor for the entire blade is 1 / 3). To improve the extreme conditions of the classical analytical design method, the proposed design method treats the induction factor as a design variable, rather than limiting it to a single ideal value, thus achieving a balance between aerodynamic efficiency and load under multiple operating conditions. Furthermore, this application does not directly control the axial induction factor, but defines a clipping factor AF of the induction factor while retaining the optimal Cp induction assumption. This value is defined as the difference between the actual induction factor a applied to the blade element segment and the induction factor a under the theoretical optimal Cp assumption. ideal The percentage deviation. Its definition is as follows: when AF = 0, it means that the induction factor applied in the design of this leaf segment is the ideal induction factor value. Similarly, the entire induction trimming factor AF distribution is defined using control points at specific leaf span positions; this group of control points is the design variable related to the induction factor.

[0070] AF=(aa ideal ) / a ideal

[0071] After parameterizing the above design variables, such as the design tip speed ratio, induction factor distribution, airfoil relative thickness distribution, and airfoil lift coefficient distribution, the design variable matrix of the blade design method of this application is obtained, as shown in Table 1 below:

[0072] Table 1 Design Variables

[0073] It should be noted that the distribution curve can be defined using the curve distribution shown in the example or a linear distribution; the blade spanwise position points can be increased, decreased, or changed, but this does not affect the effectiveness of the method proposed in this application.

[0074] Regarding S120 and S130 above, the variable values ​​of the design variables are selected from the value ranges corresponding to the multiple design variables; based on the configuration data and the selected variable values, the blade shape is analyzed to obtain the candidate shape data and performance data of the blade.

[0075] In some embodiments of this application, blade shape analysis is performed based on configuration data and selected variable values ​​to obtain candidate blade shape data and performance data, including:

[0076] Generate aerodynamic data tables for each airfoil element of the blade based on the configuration data and the selected variable values.

[0077] Based on the aerodynamic data table, the twist angle distribution and chord length distribution of each blade element segment were obtained through analysis.

[0078] By integrating the torsion angle distribution and chord length distribution of all leaf element segments, candidate leaf shape data are obtained;

[0079] Based on the candidate shape data of the blade, the performance of the blade is analyzed to obtain the blade performance data.

[0080] Furthermore, before integrating the twist angle distribution and chord length distribution of all blade elements to obtain candidate blade shape data, wind turbine blade design methods may also include:

[0081] The torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment in each leaf element segment are simplified to obtain the simplified torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment. The simplification process includes linearization and nonlinear pruning.

[0082] Regarding blade shape analysis, the blade shape can be analyzed based on the BEM model to obtain candidate shape data and performance data of the blade.

[0083] The performance data of the blade can include aerodynamic performance data and geometric feature data. Aerodynamic performance analysis of the blade can be performed using aerodynamic calculation tools based on the BEM model to obtain the aerodynamic performance data of the blade, and geometric feature analysis of the blade can be performed using geometric analysis tools to obtain the geometric feature data of the blade.

[0084] Figure 3 shows the blade chord length and twist angle distribution obtained from conventional analytical design;

[0085] Regarding the simplified processing, under normal circumstances, the distribution of chord length and twist angle obtained from analytical design is shown in Figure 3. Excessive chord length and twist angle in the blade root region are detrimental to manufacturing. In reality, the blade root is typically designed as a cylindrical section, while the blade tip is designed with a tapered chord length distribution. Since the aerodynamic load at the blade root is relatively small, and the post-processing area at the blade tip is only a few meters, the geometric post-processing methods necessary for blade engineering design do not affect the validity of this application.

[0086] Regarding S140 and S150 above, the performance data is evaluated based on the objective function and constraints to determine whether the preset conditions are met. If not, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables, and the blade shape analysis is performed based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade. This process continues until the performance data meets the preset conditions or the number of iterations reaches the preset number. The candidate shape data obtained from the last analysis is then determined as the final shape data of the blade.

[0087] In some embodiments of this application, the performance data includes aerodynamic performance data and geometric feature data. The performance data is evaluated to determine whether it meets preset conditions using an objective function and constraints, including:

[0088] Evaluate whether the aerodynamic performance data and geometric feature data meet the first preset conditions;

[0089] If the aerodynamic performance data and geometric feature data meet the first preset condition, then evaluate whether the aerodynamic performance data and geometric feature data meet the second preset condition.

[0090] Among them, the aerodynamic performance data includes at least one of the following: power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics. The aerodynamic characteristics may include the design lift coefficient and the design angle of attack. The geometric characteristic data includes at least one of the following: absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity.

[0091] The first preset condition includes that at least one of absolute thickness, stall margin, power coefficient, and blade root load meets the preset constraint value. The stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack. The second preset condition includes that the function value of a multi-objective function with at least one of power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics, and at least one of absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity as the objective is maximized.

[0092] Specifically, after parameterizing the design variables and obtaining the design variable matrix, it is necessary to further define the objective function and constraint matrix for blade aerodynamic layout optimization in order to form a complete mathematical model of the optimization problem.

[0093] For example, the blade aerodynamic layout optimization objective function proposed in this application includes at least the annual power generation of the blade and the blade root load, in order to overcome the shortcomings of the original analytical design method in that it cannot perform multi-objective design. It may further include features such as blade power coefficient, axial thrust, and geometric parameters (e.g., blade surface area, absolute thickness, blade solidity, aspect ratio). The objective function expression can be represented as: F obj =s1·w1·f aerody +s2·w2·f geo

[0094] Where s is the normalization factor of the objective parameter, w is the weight coefficient; f aerody The target parameters related to blade aerodynamics can be composed of one or more of the following aerodynamic parameters: such as blade power coefficient, annual power generation, blade root load, axial thrust, etc.; f geo The target parameters related to the blade geometry can be composed of one or more of the following geometric parameters: absolute thickness, surface area, realism, aspect ratio, etc.

[0095] In this application, multiple objective parameters (power generation, load, geometric parameters, etc.) can be controlled through an objective function to achieve a balance of objective parameters for multiple objectives.

[0096] Regarding constraints, firstly, the method proposed in this application can impose constraints on all the aforementioned target parameters (especially core target parameters such as power generation, load, Cp, etc.) to form a constraint matrix. Furthermore, to address the difficulty in directly controlling local aerodynamic risks of blade elements in traditional direct numerical optimization methods for blades, the proposed constraint matrix also includes aerodynamic characteristics related to local blade load and stall characteristics, such as stall margin. The stall margin proposed in this application is limited by either the design lift coefficient or the design angle of attack, requiring the design lift coefficient to be at least 0.4 lower than the maximum lift coefficient and the design angle of attack to be at least 4 degrees lower than the critical stall angle of attack. In addition, to improve the structural stiffness feasibility of the aerodynamic design results, the absolute thickness of a specific cross-section is further selected as a constraint. An example of the main constraints proposed in this application is summarized in the table below (the specific values ​​in the table are for illustrative purposes only).

[0097] Table 2 Constraints

[0098] Based on the wind turbine blade design method of this application, the new design meets the objectives by defining design variables, generating blade shape, analyzing blade performance, and judging whether the design objectives and constraints are met. If not, the design variables are driven to change through optimization algorithms to generate a new blade shape and enter the next iteration until the objective is achieved or the set number of iterations is terminated. This application combines numerical optimization design and analytical design methods for wind turbine blades, which can achieve the balance of macroscopic multi-objective parameters (power generation, load, geometric parameters, etc.) of wind turbine blades under multiple operating conditions, while controlling the local load conditions and aerodynamic risks at different spanwise positions, thus improving the aerodynamic adaptability of the design results.

[0099] Figure 4 is a schematic diagram of the wind turbine blade design optimization method;

[0100] Figure 4 is a simplified schematic diagram of the entire optimization process for wind turbine blades, which mainly includes the following steps:

[0101] S401, design input configuration, includes wind condition parameters, platform parameters, blade parameters, model parameters and airfoil configuration. The airfoil configuration includes geometric coordinate data and aerodynamic Polar data.

[0102] S402, design variables, including lift distribution control points, induced shearing control points, relative thickness distribution control points, and design tip speed ratio;

[0103] S403, analytical design preprocessing;

[0104] S404, analytical design of the shape, analytical twist angle distribution and analytical chord length distribution;

[0105] S405, geometric processing, processing the chord length and twist angle of the leaf root and leaf tip;

[0106] S406, blade model, obtaining the morphological leaf element matrix and performing BEM data modeling;

[0107] S407, Performance Analysis: Perform performance analysis on the obtained blade model to obtain performance data;

[0108] S4071, Aerodynamic performance analysis, obtaining aerodynamic performance data;

[0109] S4072, Geometric feature analysis, to obtain geometric feature data;

[0110] S408, constraint analysis, analyzes whether the performance data of the blade meets the constraints. If the constraints are met, the next target judgment is performed. If the constraints are not met, the optimization algorithm is used to select the values ​​of the design variables again for the next round of iteration. Constraints can be set for absolute thickness, stall margin, power coefficient and blade root load.

[0111] S409, Target Judgment: The performance data of the blade is judged using the objective function. When the performance meets the design target, the optimized version of the blade shape is obtained. When the performance does not meet the design target, the optimization algorithm is used to select the value of the design variable again for the next round of iteration.

[0112] S410, optimization algorithm, uses an optimization algorithm to select the values ​​of design variables again;

[0113] S411, the optimized version, which means the blade shape is optimized.

[0114] Figure 5 shows a comparison of chord lengths in the case design results;

[0115] Figure 6 is a comparison chart of the load distribution of the case design results;

[0116] The wind turbine blade design method of this application embodiment will be described below with reference to Figures 5 and 6 through a specific example.

[0117] This paper presents a case study design for a 10MW wind turbine platform with a blade length of 110m+. The design is based on the common DU airfoil family (six airfoils in total, with relative thicknesses of 40%, 35%, 30%, 35%, and 21% for the basic airfoil, plus a 100% thickness circular cross-section airfoil). Table 3 provides some key input parameters. The target blade is designed for a 10MW wind turbine platform, with a blade length limited to approximately 110m.

[0118] Table 3. Main input parameters for blade case design

[0119] Table 4 defines the upper and lower boundaries of the main design variables. The design tip speed ratio ranges from 10.5 to 12.5. The thickness distribution range and lift characteristics of the airfoil are set considering the aerodynamic characteristics of the DU airfoil family used. Generally, the relative thickness distribution of the blades decreases monotonically from the blade root to the blade shoulder. Since the thick airfoils (40% thickness airfoils) in the DU airfoil series are more sensitive to leading-edge roughness, the spanwise range of the thick airfoils needs to be limited to ensure low roughness sensitivity of the blades. In this case, the relative thickness of the airfoil near 25% of the blade is limited to 37.5%–41%. The lift coefficient design variables are set based on two criteria: firstly, to ensure that the design space covers the optimal Cp point, the lift coefficient at the definition point must cover the lift coefficient corresponding to the maximum lift-drag ratio of the corresponding basic airfoil; secondly, to actively control the stall risk of local blade elements, the upper boundary of the airfoil lift coefficient at the definition point must maintain an interval of more than 0.4 from the corresponding maximum lift coefficient value of the airfoil. The induced trimming factor is defined with the definition point within 85% set to 0 (following the ideal induced distribution), and a certain low induced trimming space is set for the three definition points at the blade tip to optimize the design under low load.

[0120] It should be noted that in another case optimization, all induced clipping factors were set to 0, and the design was carried out without considering induced clipping for comparative analysis.

[0121] Table 4 Variable Matrix of Case Design

[0122] Table 5 presents the main constraint parameters for the case optimization design, where the lower boundary of the optimal Cp is set to 0.45, and the maximum value of the blade root load My is limited to no more than 35000 kNm. Furthermore, constraints are imposed on the absolute thickness at key spanwise locations of the blade structure to ensure that the optimized shape provides sufficient structural stiffness.

[0123] Table 5 Constraints on Case Design

[0124] The specific objective function for optimization in this case is a multi-objective function of annual power generation, blade root ultimate load, and absolute thickness, as shown below. It should be noted that the specific objective function can be expanded as the complexity of the problem increases; the following formula is merely an illustrative example. F obj =s 11 ·w 11 ·AEP+s 12 ·w 12My_Max+s 21 ·w 21 ·AbsThick

[0125] As explained above, s 11 ,s 12 w is the aerodynamically related normalization factor. 11 ,w 12 For aerodynamic weighting coefficients; s 21 w is a normalization factor related to geometric structure. 21 These are the weighting coefficients related to the geometric structure. The normalization factor is calculated based on the magnitude of each parameter, and the weighting coefficients are distributed in a ratio of 1, 2, and 1. Therefore, the specific expression for the objective function is: F obj =0.000025AEP-0.00005My_Max+AbsThick

[0126] Figure 5 shows two design results based on this application: Optimized Case 1 and Optimized Case 2. These case designs use a design result (initial case) obtained using a traditional forward direct numerical optimization method as the initial solution for iteration. Optimized Case 1 sets all induced trimming variables to 0, meaning that the induced factors adopt an ideal induced factor distribution during iteration. Optimized Case 2 incorporates all design variables from Table 4 into the iteration. A comparison of the core blade performance parameters of the three design results is shown in Table 6. The leaf element loading characteristic distribution (circulation distribution) of the three design schemes is shown in Figure 6.

[0127] Table 6 Comparison of Key Performance Parameters of Case Design Results

[0128] Table 6 presents the aerodynamic characteristic parameters under their respective optimal conditions. The three schemes compared are not intended to demonstrate that any one result is optimal, but are only used to illustrate the advantages of the scheme proposed in this application and the potential for further improvement in blade performance. As can be seen from the table, based on the initial case, the optimized case 1 obtained in this application further improves the power generation of the initial blade while significantly reducing the blade root ultimate bending moment load My_Max, better balancing the aerodynamic efficiency-load ratio of the blade. The power generation to load ratio increases from 0.986 to 1.057. Note that, as mentioned earlier, the result of optimized case 1 is based on the case where all induced trimming factors are set to ideal values ​​(all induced trimming factor variables are set to 0). When the induced trimming factor variables are further activated, i.e., using the settings in Table 4, the design result obtained is optimized case 2. It can be seen that by trimming and optimizing the induced factors at the blade tip, it is possible to reduce the power generation by about 1% compared to the initial reference blade while reducing the blade root bending moment load by 10%. At this point, the aerodynamic load efficiency ratio is further optimized, and the power generation to load ratio increases from 0.986 to 1.098.

[0129] On the other hand, the load comparison of the three types of blades is shown in Figure 6. It can be seen that the two results obtained by the aerodynamic layout hybrid design optimization method proposed in this application are more balanced and smoother than the load of the initial reference blade, thus improving the aerodynamic risks caused by the sudden changes in local load of the initial reference blade.

[0130] The results of the above cases are only used as a verification of the blade optimization method proposed in this application.

[0131] The wind turbine blade design method proposed in this application, on the one hand, utilizes the load balancing and controllable local blade element aerodynamic risks of traditional analytical design methods, avoiding the problems of sudden changes in local blade element loads and uncontrollable aerodynamic risks in traditional direct numerical optimization design methods for blade shape. On the other hand, it applies the global and automated design advantages of numerical optimization design technology to the analytical design method of transmission, thus improving it; in particular, by parametrically controlling the design elements, it overcomes the single-condition and experience-dependent characteristics of the original analytical design method; more importantly, by parametrically controlling the distribution of inducing factors, it improves the limit condition constraints (optimal Cp condition) of the original design method, fully expanding the design space of the analytical design method, and enabling dual design control of macroscopic blade performance indicators and local aerodynamic loads.

[0132] In addition, the wind turbine blade design method of this application can be improved in the following ways:

[0133] (1) Slightly change the expansion position of the design variables and the number of design variables;

[0134] (2) Simply expand the structure or constraints of the optimization objective function;

[0135] (3) Simply change the weight coefficients, normalization factors, etc. of the sub-objective function;

[0136] (4) Slightly change the geometric post-processing method of the non-primary wind energy capture area at the blade root or blade tip;

[0137] Figure 7 is a schematic diagram of the wind turbine blade design device provided in an embodiment of this application;

[0138] Referring to Figure 7, the wind turbine blade design device provided in the embodiments of this application is introduced. The wind turbine blade design device includes:

[0139] The acquisition module 701 is used to acquire the input configuration data and the value range of each of the multiple design variables. The configuration data includes wind condition data, wind turbine whole machine data of blade adaptation, blade data, model related data and airfoil data.

[0140] The selection module 702 is used to select the variable value of the design variable from the value ranges corresponding to multiple design variables;

[0141] The parsing module 703 is used to analyze the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade.

[0142] Evaluation module 704 is used to evaluate whether the performance data meets preset conditions based on the objective function and constraints.

[0143] The optimization module 705 is used to select the variable values ​​of the design variables from the value ranges of the multiple design variables again when the conditions are not met, and return the blade shape analysis based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

[0144] In some embodiments of this application, multiple design variables include the design tip speed ratio and the design lift coefficient, induced trimming factor, and relative thickness at multiple different blade spanwise positions. The candidate shape data of the blade includes the twist angle distribution and chord length distribution of each blade element segment. The induced trimming factor is the percentage deviation between the actual design application induced factor of the blade element segment and the induced factor under the assumption of the theoretical optimal power coefficient.

[0145] In some embodiments of this application, multiple different blade spanwise positions include positions where the blade spanwise is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

[0146] In some embodiments of this application, the range of design lift coefficients at multiple different blade spanwise positions is determined in the following manner:

[0147] Based on the design lift coefficient distribution of the entire blade and the number of leaf element units to be discretized, the lift coefficient value at the corresponding leaf element position is generated according to the predetermined leaf element discretization criterion.

[0148] In some embodiments of this application, the parsing module 703 includes:

[0149] The generation unit is used to generate aerodynamic data tables for each airfoil element of the blade based on the configuration data and the selected variable values.

[0150] The analytical unit is used to analyze the aerodynamic data table to obtain the twist angle distribution and chord length distribution of each blade element segment;

[0151] The integration unit is used to integrate the twist angle distribution and chord length distribution of all leaf element segments to obtain candidate shape data of the leaf.

[0152] The analysis unit is used to perform performance analysis on the blade based on the candidate shape data of the blade, and obtain the performance data of the blade.

[0153] In some embodiments of this application, the parsing module 703 further includes:

[0154] The processing unit is used to simplify the torsion angle distribution and chord length distribution of the leaf root segment and the leaf tip segment in each leaf element segment before integrating the torsion angle distribution and chord length distribution of all leaf element segments to obtain the candidate shape data of the leaf. The simplification process includes linearization and nonlinear clipping.

[0155] In some embodiments of this application, the performance data includes aerodynamic performance data and geometric feature data, and the evaluation module 704 includes:

[0156] The first evaluation unit is used to evaluate whether the aerodynamic performance data and geometric feature data meet the first preset conditions.

[0157] The second evaluation unit is used to evaluate whether the aerodynamic performance data and geometric feature data meet the second preset conditions, provided that the aerodynamic performance data and geometric feature data meet the first preset conditions.

[0158] In some embodiments of this application, the aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity.

[0159] The first preset condition includes that at least one of absolute thickness, stall margin, power coefficient, and blade root load meets the preset constraint value. The stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack. The second preset condition includes that the function value of a multi-objective function with at least one of power coefficient, annual power generation, blade root ultimate load, and aerodynamic characteristics, and at least one of absolute thickness distribution, blade surface area, blade aspect ratio, and wind turbine solidity as the objective is maximized.

[0160] According to the wind turbine blade design device provided in the embodiments of this application, the blade shape can be analyzed based on configuration data and multiple design variables to obtain candidate shape data and performance data of the blade. Compared with the single-condition design of related technologies, the solution of this application can handle multi-condition problems, and each design variable has its own value range. Through continuous iteration, the variable value that makes the blade performance meet the optimal performance can be found. Therefore, the optimal solution in the global design space can be obtained.

[0161] Figure 8 is a structural schematic diagram of the wind turbine blade design equipment provided in an embodiment of this application;

[0162] The wind turbine blade design equipment may include a processor 801 and a memory 802 storing computer program instructions.

[0163] Specifically, the processor 801 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0164] Memory 802 may include mass storage for data or instructions. For example, and not limitingly, memory 802 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 802 may include removable or non-removable (or fixed) media. Where appropriate, memory 802 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 802 is non-volatile solid-state memory.

[0165] Memory 802 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0166] The processor 801 reads and executes computer program instructions stored in the memory 802 to implement the wind turbine blade design method in the above embodiment.

[0167] In one example, the wind turbine blade design device may also include a communication interface 803 and a bus 810. As shown in Figure 8, the processor 801, memory 802, and communication interface 803 are connected via the bus 810 and communicate with each other.

[0168] The communication interface 803 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0169] Bus 810 includes hardware, software, or both, that couples components of a wind turbine blade design device together. For example, and not as a limitation, bus 810 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 810 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0170] The wind turbine blade design equipment executes the wind turbine blade design method in the embodiments of this application, thereby realizing the wind turbine blade design method of Figure 1.

[0171] Furthermore, in conjunction with the wind turbine blade design methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the wind turbine blade design methods in the above embodiments.

[0172] In conjunction with the wind turbine blade design method in the above embodiments, this application also provides a computer program product. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the wind turbine blade design method of any of the above embodiments.

[0173] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0174] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0175] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0176] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0177] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A wind turbine blade design method, the method comprising: The system obtains the input configuration data and the value ranges of multiple design variables. The configuration data includes wind condition data, wind turbine data for blade adaptation, blade data, model-related data, and airfoil data. Select the variable values ​​of the design variables from the value ranges corresponding to each of the multiple design variables; Based on the configuration data and the selected variable values, the blade shape is analyzed to obtain candidate shape data and performance data of the blade. The performance data is evaluated to determine whether it meets the preset conditions based on the objective function and constraints. If the conditions are not met, the variable values ​​of the design variables are selected again from the value ranges corresponding to the multiple design variables, and the blade shape analysis is performed based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade. This process continues until the performance data meets the preset conditions or the number of iterations reaches the preset number. The candidate shape data obtained from the last analysis is then determined as the final shape data of the blade.

2. The wind turbine blade design method according to claim 1, wherein, The multiple design variables include the design tip speed ratio and the design lift coefficient, induced trimming factor, and relative thickness at multiple different blade spanwise positions. The candidate shape data of the blade includes the twist angle distribution and chord length distribution of each blade element segment. The induced trimming factor is the percentage deviation between the actual design application induced factor of the blade element segment and the induced factor under the theoretical optimal power coefficient assumption.

3. The wind turbine blade design method according to claim 2, wherein, The multiple different blade span positions include positions where the blade span is at 0%, 15%, 25%, 35%, 55%, 85%, 95%, and 100%, respectively.

4. The wind turbine blade design method according to claim 3, wherein, The range of design lift coefficients at the multiple different blade spanwise positions is determined in the following way: Based on the design lift coefficient distribution of the entire blade and the number of leaf element units to be discretized, the lift coefficient value at the corresponding leaf element position is generated according to the predetermined leaf element discretization criterion.

5. The wind turbine blade design method according to claim 2, wherein, The step of analyzing the blade shape based on the configuration data and selected variable values ​​to obtain candidate blade shape data and performance data includes: Based on the configuration data and the selected variable values, generate aerodynamic data tables for each airfoil element of the blade. Based on the aerodynamic data table, the twist angle distribution and chord length distribution of each blade element segment are obtained through analysis. By integrating the torsion angle distribution and chord length distribution of all leaf element segments, candidate leaf shape data are obtained; The performance data of the blade is obtained by performing performance analysis on the blade based on the candidate shape data of the blade.

6. The wind turbine blade design method according to claim 5, before integrating the twist angle distribution and chord length distribution of all blade element segments to obtain the candidate shape data of the blade, the method further includes: The torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment in each leaf element segment are simplified to obtain the simplified torsion angle distribution and chord length distribution of the leaf root segment and leaf tip segment. The simplification process includes linearization and nonlinear pruning.

7. The wind turbine blade design method according to any one of claims 1 to 6, wherein, The performance data includes aerodynamic performance data and geometric feature data. The evaluation of whether the performance data meets preset conditions using an objective function and constraints includes: Evaluate whether the aerodynamic performance data and the geometric feature data meet the first preset condition; If the aerodynamic performance data and the geometric feature data satisfy the first preset condition, then evaluate whether the aerodynamic performance data and the geometric feature data meet the second preset condition.

8. The wind turbine blade design method according to claim 7, wherein, The aerodynamic performance data includes at least one of power coefficient, annual power generation, blade root ultimate load and aerodynamic characteristics, and the geometric characteristic data includes at least one of absolute thickness distribution, blade surface area, blade aspect ratio and wind turbine solidity. The first preset condition includes at least one of absolute thickness, stall margin, power coefficient, and blade root load satisfying a preset constraint value. The stall margin includes the stall margin of the design lift coefficient or the stall margin of the design angle of attack. The second preset condition includes maximizing the function value of a multi-objective function with at least one of the power coefficient, the annual power generation, the blade root ultimate load, and the aerodynamic characteristics, and at least one of the absolute thickness distribution, the blade surface area, the blade aspect ratio, and the wind turbine solidity as objectives.

9. A wind turbine blade design device, the device comprising: The acquisition module is used to acquire the input configuration data and the value range of each of the multiple design variables. The configuration data includes wind condition data, wind turbine whole machine data of blade adaptation, blade data, model related data and airfoil data. The selection module is used to select the variable value of the design variable from the value ranges corresponding to the multiple design variables; The parsing module is used to analyze the blade shape based on the configuration data and the selected variable values ​​to obtain candidate shape data and performance data of the blade. The evaluation module is used to evaluate whether the performance data meets preset conditions based on the objective function and constraints. The optimization module is used to select the variable values ​​of the design variables from the value ranges corresponding to the multiple design variables again when the conditions are not met, and return the blade shape analysis based on the configuration data and the selected variable values ​​to obtain the candidate shape data and performance data of the blade, until the performance data meets the preset conditions or the number of iterations reaches the preset number, and the candidate shape data obtained from the last analysis is determined as the final shape data of the blade.

10. A wind turbine blade design device, the device comprising: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the wind turbine blade design method as described in any one of claims 1 to 8.

11. A computer-readable storage medium storing computer program instructions that, when executed by a processor, represent the wind turbine blade design method as described in any one of claims 1 to 8.

12. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the wind turbine blade design method as described in any one of claims 1 to 8.

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