Structural optimization method and system for large wind power blades, intelligent device and medium

Abstract

The application relates to the technical field of wind turbine blades, and particularly provides a large wind power blade structure optimization method, system, intelligent device and medium, and aims to solve the problem of lacking a large-size super-long flexible blade structure optimization design method. For this purpose, the large wind power blade structure optimization method provided by the application comprises the following steps: obtaining structure data and material data of a blade, establishing an initial model of the blade according to the structure data and the material data, taking minimization of the mass of the blade as an optimization target, taking a pre-set maximum value of blade tip deflection and a minimum value of frequency deviation as optimization constraint conditions, wherein the frequency deviation is the difference between the rotating frequency of the blade and the natural frequency of the blade, and optimizing the initial model based on the optimization target and the optimization constraint conditions to obtain an optimization model. The application provides a convenient and effective structure optimization design method for large-size super-long flexible blades.

Description

Structural optimization methods, systems, intelligent devices and media for large wind turbine blades Technical Field

[0001] This application relates to the field of wind turbine blade technology, specifically to a method, system, intelligent device, and medium for structural optimization of large wind turbine blades. Background Technology

[0002] With the rapid development of the wind power industry, the trend of increasing blade length and rotor diameter is obvious, and the trend of larger wind turbine units is inevitable, which brings new challenges to unit design. Finite element simulation can accurately simulate the overall and local strength and stiffness of blades and is a commonly used method for modern wind turbine blade structural design and analysis. At present, structural optimization methods for steady-state, small and medium-sized blades are relatively mature, but research is lacking in the field of structural optimization design methods for large-sized, ultra-long flexible blades.

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

[0004] In order to overcome the above-mentioned deficiencies, this application is made to solve or at least partially solve the technical problem of lacking an optimization design method for large-size ultra-long flexible blade structures.

[0005] In a first aspect, a method for structural optimization of a large wind turbine blade is provided. The method includes: acquiring structural data and material data of the blade; establishing an initial model of the blade based on the structural data and material data; taking minimizing the mass of the blade as the optimization objective; taking a pre-set maximum tip offset and minimum frequency deviation as optimization constraints, wherein the frequency deviation is the difference between the blade rotation frequency and the blade's natural frequency; and optimizing the initial model based on the optimization objective and the optimization constraints to obtain an optimized model.

[0006] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the structural data includes multiple cross-sectional positions and corresponding data such as twist angle, chord length, pitch position, load, and airfoil. The material data includes corresponding material distribution data. The step of establishing an initial model of the blade based on the structural data and material data includes: establishing an initial model of the blade based on the multiple cross-sectional positions and corresponding data such as twist angle, chord length, pitch position, load, airfoil, and material distribution.

[0007] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the cross-sectional position includes a first coordinate of any airfoil in the blade spanwise direction, and the airfoil data includes a second coordinate and a third coordinate representing the airfoil geometry, wherein the second coordinate represents the position of the airfoil geometry in the blade chordwise direction, and the third coordinate represents the position of the airfoil geometry in the blade thickness direction, wherein the blade thickness direction is perpendicular to the blade spanwise direction and the blade chordwise direction, respectively.

[0008] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the blade includes a first surface and a second surface opposite to each other, the first surface and the second surface being connected at the leading edge and trailing edge of the blade respectively, wherein the second coordinate increases monotonically along the first surface from the leading edge to the trailing edge, and decreases monotonically along the second surface from the trailing edge to the leading edge.

[0009] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the material distribution data includes material partitioning location, number of laminates, number of ply layers, ply materials, and material thickness.

[0010] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the material data also includes the mechanical property data of the material, and the method further includes: calculating the mass of the blade based on the initial model, the load data, and the mechanical property data.

[0011] In one technical solution of the above-mentioned structural optimization method for large wind turbine blades, the step of optimizing the initial model based on the optimization objective and the optimization constraints to obtain an optimized model includes: making a preliminary prediction of the ply material and material thickness based on the initial model, the optimization objective, and the optimization constraints; obtaining the optimal solution of the ply material and material thickness using an optimization algorithm based on the preliminary prediction results; and determining the optimized model of the blade based on the optimal solution of the ply material and material thickness.

[0012] In a second aspect, a structural optimization system for large wind turbine blades is provided. The system includes: a data acquisition module for acquiring structural and material data of the blade; a model building module for establishing an initial model of the blade based on the structural and material data; a target setting module for setting minimizing the blade's mass as the optimization target; a constraint setting module for setting a pre-set maximum tip offset and minimum frequency deviation as optimization constraints, wherein the frequency deviation is the difference between the blade's rotational frequency and its natural frequency; and a structural optimization module for optimizing the initial model based on the optimization target and the optimization constraints to obtain an optimized model.

[0013] In a third aspect, a smart device is provided, the smart device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the method described in any of the above-described technical solutions for the structural optimization method of large wind turbine blades.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the method described in any of the above-described technical solutions for the structural optimization method of large wind turbine blades.

[0015] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0016] In implementing the technical solution provided in this application, structural and material data of the blade are acquired, and an initial model of the blade is established based on these data. Minimizing the blade's mass is taken as the optimization objective, and pre-set maximum tip offset and minimum frequency deviation are used as optimization constraints. The initial model is then optimized based on the optimization objective and constraints. Since this application constructs the initial blade model based on its structural and material data, it is applicable not only to small and medium-sized blades but also to large-sized, ultra-long flexible blades. By minimizing the blade's mass as the optimization objective and using pre-set maximum tip offset and minimum frequency deviation as optimization constraints, the blade structure is made lightweight while satisfying the requirements for stiffness performance, structural stability, structural safety, and operational stability. Finally, the initial model is optimized based on the optimization objective and constraints to obtain an optimized model, providing a convenient and effective structural optimization design method for large-sized, ultra-long flexible blades. Attached Figure Description

[0017] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Wherein:

[0018] Figure 1 is a schematic flowchart of the main steps of a structural optimization method for a large wind turbine blade according to an embodiment of this application;

[0019] Figure 2 is a schematic diagram of the coordinate axes of an airfoil according to an embodiment of this application;

[0020] Figure 3a is a schematic diagram of materials according to an embodiment of this application;

[0021] Figure 3b is a schematic diagram of the thickness distribution of the beam cap according to an embodiment of this application;

[0022] Figure 3c is a schematic diagram of the thickness distribution of the leading edge according to an embodiment of this application;

[0023] Figure 3d is a schematic diagram of the thickness distribution of the trailing edge according to an embodiment of this application;

[0024] Figure 3e is a schematic diagram of the thickness distribution of the web plate according to an embodiment of this application;

[0025] Figure 4 is a schematic diagram comparing the mass distribution curves of the blade before and after optimization according to an embodiment of this application;

[0026] Figure 5a is a schematic diagram comparing the flapping stiffness curves of the blade before and after optimization according to an embodiment of this application;

[0027] Figure 5b is a schematic diagram comparing the shear stiffness curves of the blade before and after optimization according to an embodiment of this application;

[0028] Figure 5c is a schematic diagram comparing the torsional stiffness curves of the blade before and after optimization according to an embodiment of this application;

[0029] Figure 6 is a schematic diagram of the main structure of a structural optimization system for a large wind turbine blade according to an embodiment of this application;

[0030] Figure 7 is a schematic diagram of the overall architecture of a structural optimization program for a large wind turbine blade according to an embodiment of this application.

[0031] Figure 8 is a schematic diagram of the main structure of a smart device according to an embodiment of this application.

[0032] Figure label:

[0033] 11: Memory; 12: Processor. Detailed Implementation

[0034] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0035] In the description of this application, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection via an intermediate medium; a connection within two elements; a wireless connection or a wired connection.

[0036] Furthermore, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor can be implemented in software, in hardware, or a combination of both. Computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0037] Furthermore, if the term "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B," and can include only A, only B, or A and B. The singular forms of the terms "a" and "this" can also include plural forms.

[0038] Referring to Figure 1, Figure 1 is a schematic flowchart of the main steps of a structural optimization method for a large wind turbine blade according to an embodiment of this application. As shown in Figure 1, the structural optimization method for a large wind turbine blade in this embodiment mainly includes the following steps S2 to S10:

[0039] Step S2: Obtain the structural data and material data of the blade.

[0040] In this embodiment, the structural data and material data of the wind turbine blade are first obtained. The structural data includes a description of the blade's shape (such as blade length, chord length, twist angle, and aerodynamic profile geometry along the blade) and a description of the blade's internal structure (such as the number and location of shear webs within the blade). The material data includes material properties and mechanical performance data (such as the material's elastic modulus, shear modulus, Poisson's ratio, density, failure rate, and yield strength).

[0041] It is understood that the above structural and material data are used to build a model of wind turbine blades. The required data can be selected according to the specific situation. This embodiment does not impose any specific restrictions on comparison.

[0042] In one alternative implementation, the structural data includes multiple cross-sectional locations and corresponding twist angles, chord lengths, pitch positions, load data, and airfoil data, while the material data includes corresponding material distribution data for each of the multiple cross-sectional locations.

[0043] In one alternative implementation, the cross-sectional position includes a first coordinate of any airfoil in the blade spanwise direction, and the airfoil data includes a second coordinate and a third coordinate representing the airfoil geometry, wherein the second coordinate represents the position of the airfoil geometry in the blade chordwise direction, and the third coordinate represents the position of the airfoil geometry in the blade thickness direction, wherein the blade thickness direction is perpendicular to the blade spanwise direction and the blade chordwise direction, respectively.

[0044] In this embodiment, the blade chord direction refers to the direction along the blade cross-section (airfoil) from the leading edge to the trailing edge, i.e., the chord direction of the airfoil; the blade span direction refers to the direction of extension of the blade from the root to the tip; and the blade thickness direction refers to the thickness direction of the blade cross-section. These three directions are mutually perpendicular, and a xyz coordinate system for the blade can be constructed accordingly. The second and third coordinates representing the airfoil geometry correspond to the x and y coordinates, respectively, and the first coordinate along the blade span corresponds to the z coordinate.

[0045] In one alternative embodiment, the blade includes opposing first and second surfaces connected at the leading and trailing edges of the blade, respectively, wherein a second coordinate representing the airfoil geometry monotonically increases along the first surface from the leading edge to the trailing edge and monotonically decreases along the second surface from the trailing edge to the leading edge.

[0046] In this embodiment, as shown in Figure 2, assuming the leading edge M coordinate is (0,0) and the trailing edge N coordinate is (1,0), the x-coordinate monotonically increases along the upper surface from the leading edge to the trailing edge, and monotonically decreases along the lower surface from the trailing edge to the leading edge. Furthermore, the aerodynamic profile coordinates along the upper and lower surfaces must not intersect and must be defined as single-valued functions. This unifies the geometric description of different airfoils, avoids modeling errors caused by coordinate system confusion, and ensures that the airfoil profile is a closed, non-self-intersecting curve (a physically realizable shape).

[0047] In one alternative implementation, the material distribution data includes the material partition location, number of laminates, number of plies, ply material, and material thickness.

[0048] In this embodiment, the material distribution data further includes the material partitioning location, the number of laminates, the number of plies, the ply materials, and the material thickness. The laminates are stacks of different composite materials and different composite material plies with different main fiber directions. The material partitioning location can be set according to requirements, such as dividing it into three regions: an upper surface, a lower surface, and a sheared web. The upper and lower surfaces are defined as the upper and lower parts of the cross-section from the leading edge to the trailing edge, respectively. The upper and lower surfaces can be divided into any number of laminates. For each region, the number of laminates, the number of plies, the ply materials, and the material thickness can be set according to requirements such as aerodynamic performance, structural strength, and lightweighting. As an example, the number of upper surface laminates can be 3 to 5 layers, each containing 20 to 40 plies, with a total thickness of 10 to 30 mm. The material can be carbon fiber or high-modulus glass fiber unidirectional fabric, glass fiber bidirectional fabric, etc.; the number of lower surface laminates can be 3 to 5 layers (symmetrical to the upper surface or slightly fewer), each containing 15 to 30 plies, with a total thickness of 8 to 25 mm. The material can be glass fiber or carbon fiber unidirectional fabric, bidirectional fabric, etc.; the number of sheared web laminates can be 2 layers, each containing 10 to 20 plies, with a total thickness of 5 to 15 mm. The material can be glass fiber bidirectional fabric, lightweight foam, or balsa wood, etc.

[0049] In one implementation, the main fiber direction of each layer can also be set.

[0050] In one embodiment, the laminated plate distribution on the upper and lower surfaces can be set to be the same, and the laminated plate distribution of each shear web is the same, thereby simplifying the blade structure, reducing the amount of calculation, and saving computational efficiency.

[0051] In one embodiment, the upper or lower surface can be further divided into a beam cap area, a transition area, and an aerodynamic skin area. The beam cap area refers to the top extension region directly covering the main beam, with a width typically 15%–30% of the blade chord length. The transition area refers to the transition region between the beam cap area and the aerodynamic profile, with a width of approximately 10%–20% of the chord length. The aerodynamic skin area refers to the remaining area of ​​the upper surface (the aerodynamic profile portion away from the beam cap). It is understood that in this embodiment, the material partitioning location, the number of laminates, the number of ply layers, the ply material, and the material thickness can be freely set and represented based on the aforementioned xyz coordinate system.

[0052] Step S4: Establish an initial model of the blade based on the structural and material data.

[0053] In this embodiment, a finite element simulation model, i.e., the initial model of the blade, is established based on the above structural and material data.

[0054] In an optional implementation, step S4 above may further include the following step S42:

[0055] Step S42: Establish an initial model of the blade based on multiple cross-sectional positions and the corresponding twist angle, chord length, pitch position, load, airfoil data, and material distribution data.

[0056] In this embodiment, an initial model of the blade can be established using multiple cross-sectional locations and corresponding data such as twist angle, chord length, pitch position, load, airfoil data, and material distribution data. As an example, 60 cross-sections on the blade are selected, and the initial model of the blade is established using corresponding data such as twist angle, chord length, pitch position, load, airfoil data, and material distribution data. The cross-section location (Spanwise Location) is the normalized location along the blade spanwise, used to locate the analysis cross-section; the twist angle (°) refers to the twist angle of the airfoil relative to the plane of rotation, affecting the angle of attack distribution and aerodynamic performance; the chord length (m) refers to the chord length of the airfoil, determining the aerodynamic area and load distribution of the blade; the airfoil profile refers to the coordinate (x,y) data of the airfoil, used to define the aerodynamic shape; the material distribution data includes the material partition location, the number of laminates, the number of plyes, the ply materials, and the material thickness; the pitch angle (°) refers to the pitch angle of the blade around its longitudinal axis, affecting the initial conditions of aerodynamic loads; and the loads refer to the aerodynamic loads (pressure distribution), inertial loads (centrifugal force), and external loads (wind, gravity) at each cross-section. This embodiment uses finite element simulation to integrate the geometric, aerodynamic, structural, and load parameters of multiple cross-section locations to achieve the purpose of establishing the initial blade model.

[0057] In one implementation, the blade structure can be adjusted by modifying airfoil and material data to simulate any blade structure, solving the problem that current blade structure optimization methods are difficult to apply to large-size, ultra-long flexible blades.

[0058] In one alternative implementation, the material data also includes the mechanical property data of the material, such as the material number, tensile Young's modulus, lateral Young's modulus, shear modulus, Poisson's ratio, density, strength, etc.

[0059] Following step S4 above, the following step S5 may be further included:

[0060] Step S5: Calculate the mass of the blade based on the initial model, load data, and mechanical performance data.

[0061] In this embodiment, the mass of the blade is calculated by integrating the geometric information, material distribution data, load data (such as centrifugal force, gravity, etc.) and mechanical property data (Young's modulus, shear modulus, Poisson's ratio, etc.) of the initial model using finite element simulation.

[0062] Step S6: Minimize the mass of the blade as the optimization objective.

[0063] In this embodiment, in order to make the blades meet the requirements for lightweighting, minimizing the mass of the blades is taken as the optimization objective.

[0064] Step S8: The pre-set maximum tip offset and minimum frequency deviation are used as optimization constraints, where the frequency deviation is the difference between the blade rotation frequency and the blade's natural frequency.

[0065] In this embodiment, the difference between the blade's rotational frequency and its natural frequency is calculated using finite element simulation as the frequency deviation. Pre-set maximum tip offset (maximum allowable tip offset) and minimum frequency deviation (minimum allowable frequency deviation) are used as constraints for blade structural optimization. The maximum allowable tip offset limits the blade's deformation during operation, reflecting its stiffness and structural stability. The minimum allowable frequency deviation prevents resonance, improving the blade's structural safety and operational stability. It is understood that the optimization objectives and constraints can be set according to actual needs; this embodiment does not impose specific limitations on them.

[0066] Step S10: Optimize the initial model based on the optimization objective and optimization constraints to obtain the optimized model.

[0067] In this embodiment, based on the above optimization objective: minimizing the mass of the blade and the optimization constraints: maximum allowable tip offset and minimum allowable frequency deviation, the initial model is optimized using an optimization algorithm to obtain an optimized model.

[0068] In one implementation, the optimization algorithm includes, but is not limited to, Pattern Search (PS), Gradient Descent (GD), and Particle Swarm Optimization (PSO). It should be noted that the choice of optimization algorithm depends on the characteristics of the problem, computational cost, and optimization objective. For example, Pattern Search can be chosen for small-scale, discrete, or tightly constrained local optimization; Gradient Descent can be chosen for efficient local optimization of continuous, differentiable problems; and PSO can be chosen for global exploration, multi-objective, or mixed-variable problems.

[0069] In one implementation, a combined strategy can also be used, such as using PSO for global initial screening and then using gradient descent / pattern search for local refinement, in order to balance efficiency and accuracy.

[0070] In an optional implementation, step S10 may further include steps S102 to S106:

[0071] Step S102: Make preliminary predictions on the layup material and material thickness based on the initial model, optimization objective and optimization constraints.

[0072] In this embodiment, the layup material and material thickness are used as optimization parameters. The approximate solution or initial solution is obtained through rapid prediction (preliminary prediction), which serves as the starting point for subsequent precise optimization, thereby improving optimization efficiency.

[0073] Step S104: Based on the preliminary prediction results, the optimal solution for the layup material and material thickness is obtained using an optimization algorithm.

[0074] In this embodiment, based on the preliminary prediction results in step S102, the optimal solution for the layup material and material thickness is obtained through multiple iterations using an optimization algorithm.

[0075] Step S106: Determine the optimal model of the blade based on the optimal solution of the layup material and material thickness.

[0076] In this embodiment, the final optimized model of the blade is determined based on the optimal solution of the layup material and material thickness obtained in step S104.

[0077] In an alternative implementation, Figures 3a to 3e illustrate the optimized blade structure (layout information). As shown in Figure 3a, eight materials (blade-root, blade-shell, spar-uni, spar-core, LEP-core, TEP-core, web-shell, web-core) are represented by different dotted lines, with green dots (control points, data1) representing preset reference points. The horizontal axis in Figures 3b to 3e represents the blade's Z-axis coordinate, and the vertical axis represents the thickness of the aforementioned materials. In other words, Figures 3b to 3e respectively show the thickness distribution of these materials in the spar caps, leading edge panels, trailing edge panels, and shear webs. Based on this layup information, the final optimized model can be determined.

[0078] In an optional implementation, after step S10 above, the following step S11 may be further included:

[0079] Step S11: Perform structural analysis and comparison on the initial model and the optimized model.

[0080] In this embodiment, Figure 4 is a schematic diagram comparing the mass distribution curves before and after optimization. Optimize represents the optimized model (after optimization), and Analysis represents the initial model (before optimization). The horizontal axis represents the Z-axis coordinate of the blade, and the vertical axis represents the mass distribution. As can be seen from Figure 4, in the initial model, the root stress of the blade is relatively small, but the mass distribution is relatively large; the mid-section stress is relatively large, but the mass distribution is relatively small. In contrast, the optimized model has a smoother and more reasonable mass distribution, resulting in a superior structure. Furthermore, Figures 5a to 5c show the flapping stiffness (EL) of the initial and optimized models. flap ), shear stiffness (EL) edge The figures show the torsional stiffness (tor_stff) curves. As can be seen from the figures, the torsional stiffness does not change significantly before and after optimization, while the flapping stiffness and shear stiffness are improved to some extent in the mid-section of the blade in the optimized model (better anti-flapping and anti-shear capabilities). In summary, the optimized model obtained using the method in this embodiment performs better than the initial model.

[0081] This application constructs an initial model of the blade based on its structural and material data, applicable not only to small and medium-sized blades but also to large, ultra-long flexible blades. This application uses minimizing the blade's mass as the optimization objective and pre-set maximum tip offset and minimum frequency deviation as optimization constraints, achieving lightweight blade structure while satisfying the requirements for stiffness performance, structural stability, structural safety, and operational stability. Finally, based on the optimization objective and constraints, the initial model is optimized to obtain an optimized model, providing a convenient and effective structural optimization design method for large, ultra-long flexible blades.

[0082] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0083] Another aspect of this application provides a structural optimization system for large wind turbine blades, as shown in Figure 6, comprising: a data acquisition module 2 for acquiring structural and material data of the blade; a model building module 4 for establishing an initial model of the blade based on the structural and material data; a target setting module 6 for setting minimizing the blade mass as the optimization target; a constraint setting module 8 for setting a pre-set maximum tip offset and minimum frequency deviation as optimization constraints, wherein the frequency deviation is the difference between the blade rotation frequency and the blade's natural frequency; and a structural optimization module 10 for optimizing the initial model based on the optimization target and optimization constraints to obtain an optimized model.

[0084] It is understood that the above-mentioned structural optimization system for large wind turbine blades is used to execute the embodiment of the structural optimization method for large wind turbine blades shown in Figure 1. The technical principles, technical problems solved, and technical effects of the two are similar. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the structural optimization system for large wind turbine blades can be referred to the content described in the embodiment of the structural optimization method for large wind turbine blades, and will not be repeated here.

[0085] In one optional implementation, this embodiment develops an open-source software, Co-Blade, based on the Matlab simulation platform to implement the above-mentioned methods. Its structural calculations are based on traditional classical laminate theory, Euler Bernoulli beam theory, shear flow theory, and linear buckling analysis, enabling efficient and accurate simulation of the blade's mechanical behavior. Specifically, Euler Bernoulli beam theory (macroscopic deformation): using a lumped parameter approach, considering the relationship between load and deformation, a simplified beam model of the blade is established to calculate macroscopic flapping, oscillation, tension, and torsional deformation; classical laminate theory (microscopic deformation): using material mechanical properties, the lumped mechanical parameters of the blade section are calculated, and the stress and strain values ​​of different plies are inverted using the calculated section loads; shear flow theory (microscopic deformation): using material mechanical properties, the lumped torsional stiffness and shear center of the blade section are calculated; linear buckling analysis (overall structure): the upper and lower surfaces are simplified as bent thin-walled shells subjected to pressure and shear forces, with the shear web equivalent to a flat plate subjected to bending and shear forces. The application of these methods makes Co-Blade software highly suitable for rapid calculation and optimization of structural problems.

[0086] Specifically, the programming structure of the Co-Blade software is shown in Figure 7. CoBlade_init() is the initialization program, which can set the structural parameters of the blade (such as blade geometry, material distribution information, material database, etc.), structural numerical analysis parameters (such as the number of finite element meshes, the number of calculation modes, etc.), and layup optimization parameters (such as the number of control points, optimization algorithm, initial values, etc.).

[0087] In one branch, after initialization, the three-dimensional position data of the blade structure (definePanel) can be generated. Then, the structural analysis program structAnalysis() is executed on the blade to calculate cross-sectional structural properties such as blade cross-sectional area, cross-sectional moment of inertia, cross-sectional equivalent Young's modulus, elastic center position, mass center position, torsional stiffness, shear center, etc.; and to calculate blade structural stresses such as stressNormal (normal stress), stressShear (shear stress), buckling (stressBuckle), and deformation (bladeDisplacement).

[0088] In one branch, after the initialization process, the structure optimization program can be executed: structOptimize(), which specifically includes two parts: the structure optimization objective (structFitness) and the constraint optimization problem solution (fmincon). Optionally, the structure optimization program may also include: structure optimization variables (xo).

[0089] In one alternative implementation, the software includes the following two operating modes:

[0090] 1. It can be used as a structural analysis tool to calculate blade deformation, laminate stress and strain, buckling and other problems. It can calculate structural modes and natural frequencies by calling Bmodes. It can also be used as a preprocessing tool for FAST and Bladed to output parameters such as the elastic center, flapping stiffness, torsional stiffness and moment of inertia of the blade structure.

[0091] 2. Once the blade geometry and aerodynamic loads are determined, Co-Blade can provide an optimal layup scheme that minimizes blade weight while satisfying constraints such as strength, buckling, tip deformation, and natural frequency, making it suitable for wind turbine blade structure optimization design.

[0092] In an optional implementation, in analysis mode (optimization option set to FALSE), the structural data of the blade is first imported into the parameter input file. This includes information such as the description of the blade's shape, the description of the blade's internal structure, material properties, applied loads and unit operating conditions, and design constraints. Examples of relevant information include blade length, chord length, twist angle, and aerodynamic profile geometry along the blade, the number and location of shear webs within the blade, the elastic modulus, shear modulus, Poisson's ratio, density, failure and yield strength of the selected composite material, as well as applied aerodynamic forces, moment inclination angle, blade azimuth angle, blade cone angle, rotor rotation speed, and blade hub radius.

[0093] The specific steps for running the structural simulation calculation program include:

[0094] 1) In the parameter file, fill in FALSE for the preferred option. The parameter file is mainly used for function selection and contains a small part of the input content (such as optimization objectives, constraints, loads, modal frequencies, etc.).

[0095] 2) Confirm relevant blade information, including the number of cross sections, blade length, hub radius, and pitch angle;

[0096] 3) Specify the required material file (material data). If Bmodes needs to be called, the number of modal frequencies to be calculated needs to be set.

[0097] 4) Input the ply information for each section (material partition location, number of laminates, number of ply layers, ply material, and material thickness) and confirm the shear web location;

[0098] 5) Select the required blade structure analysis data in the output file list, select TRUE for the corresponding option, and confirm the output file information such as cross-sectional parameters, stress, strain, and modal frequencies;

[0099] 6) Run the Matlab main program CoBlade.m and output relevant results (such as the three-dimensional stress cloud map of wind turbine blades). For specific information, please refer to the .out file generated in the main folder.

[0100] The following points should be noted when inputting parameters:

[0101] In the blade geometry data, the leading edge has coordinates (0,0), while the trailing edge should have coordinates (1,0); the x-coordinate monotonically increases along the upper surface from the leading edge to the trailing edge, and monotonically decreases along the lower surface from the trailing edge to the leading edge; the aerodynamic profile coordinates along the upper and lower surfaces cannot intersect and must be defined as single-valued functions.

[0102] The internal composite laminate is defined as three parts: the upper surface, the lower surface, and the shear web. The upper and lower surfaces are defined as the upper and lower parts of the cross section from the leading edge to the trailing edge, respectively. The upper and lower surfaces can be divided into any number of laminates. The data input order is: first the upper surface, then the lower surface, and finally the shear web. Starting from laminate 1, input the number of material layers, layer thickness, main fiber direction of each layer, and material properties of each layer for each laminate. Layer numbering proceeds from the outer surface to the inside of the blade, with the outermost layer on the outer surface being the first layer.

[0103] The material input file contains the mechanical parameters and ultimate strength of the layers assigned to the laminate input file. When Co-Blade is running in analysis mode, any number of materials can be entered into the material input file; while when Co-Blade is running in optimization mode, it reads only 8 materials from the material file.

[0104] In an optional implementation, in optimization mode (optimization option set to TRUE), the specific steps of the Co-Blade layup optimization calculation program include:

[0105] 1) In the parameter file, set the optimization option to TRUE and select to fill in the optimization algorithm program of particle swarm optimization, gradient descent, or pattern search.

[0106] 2) Confirm relevant blade information, including the number of cross sections, blade length, hub radius, and pitch angle;

[0107] 3) Specify the required material documents (material data);

[0108] 4) Input the ply information for each section and confirm the location of the shear web;

[0109] 5) Run the Matlab main program CoBlade.m to view the optimization results.

[0110] In one implementation, under optimized mode, the laminated plate distributions on the upper and lower surfaces are identical, and the laminated plate distributions of each shear web are also identical. The optimization algorithm can change the chordal position of the cap and shear web. To use continuous design variables, each single layer is a single layer with continuously variable thickness, rather than a multilayer stack with discrete thicknesses. Design variables include the chordal width and position of the cap, the thickness of the blade root material, and the thickness of the laminated plates of the cap and shear web. Structural and modal simulations can be performed on the optimized wind turbine blade model (optimized model), outputting analysis results such as stress-strain, mass distribution, and stiffness distribution.

[0111] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0112] Another aspect of this application provides a computer-readable storage medium.

[0113] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program for performing the structural optimization method for large wind turbine blades described in the above-described method embodiments. This program can be loaded and run by a processor to implement the structural optimization method for large wind turbine blades. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0114] Another aspect of this application provides a smart device.

[0115] In one embodiment of a smart device according to this application, the smart device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the method described in any of the above embodiments. Referring to Figure 8, Figure 8 exemplarily illustrates a communication connection between a memory 11 and a processor 12 via a bus.

[0116] In some embodiments of this application, the smart device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in this application. Optionally, the smart device described in this application may be, but is not limited to, a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, in-vehicle device, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), augmented reality (AR) / virtual reality (VR) device, etc., and this application does not limit this.

[0117] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for structural optimization of large wind turbine blades, characterized in that, The method includes: acquiring structural and material data of the blade; establishing an initial model of the blade based on the structural and material data using finite element simulation; minimizing the blade mass as the optimization objective; using a pre-set maximum tip offset and minimum frequency deviation as optimization constraints, wherein the frequency deviation is the difference between the blade rotation frequency and the blade's natural frequency; optimizing the initial model based on the optimization objective and the optimization constraints to obtain an optimized model; the structural data includes multiple cross-sectional positions and corresponding twist angle, chord length, pitch position, load data, and airfoil data; the material data includes corresponding material distribution data; establishing the initial model of the blade based on the structural and material data using finite element simulation includes: establishing the initial model of the blade based on the multiple cross-sectional positions and corresponding twist angle, chord length, pitch position, load, airfoil data, and material distribution data.

2. The method according to claim 1, characterized in that, The cross-sectional position includes a first coordinate of any airfoil in the blade spanwise direction, and the airfoil data includes a second coordinate and a third coordinate representing the airfoil geometry, wherein the second coordinate represents the position of the airfoil geometry in the blade chordwise direction, and the third coordinate represents the position of the airfoil geometry in the blade thickness direction, wherein the blade thickness direction is perpendicular to the blade spanwise direction and the blade chordwise direction, respectively.

3. The method according to claim 2, characterized in that, The blade includes a first surface and a second surface opposite to each other, the first surface and the second surface being connected at the leading edge and trailing edge of the blade, respectively, wherein the second coordinate increases monotonically along the first surface from the leading edge to the trailing edge, and decreases monotonically along the second surface from the trailing edge to the leading edge.

4. The method according to claim 1, characterized in that, The material distribution data includes the material partition location, number of laminates, number of plies, ply materials, and material thickness.

5. The method according to claim 1, characterized in that, The material data also includes the mechanical property data of the material, and the method further includes: calculating the mass of the blade based on the initial model, the load data, and the mechanical property data.

6. The method according to claim 4, characterized in that, The optimization of the initial model based on the optimization objective and the optimization constraints to obtain an optimized model includes: making a preliminary prediction of the ply material and material thickness based on the initial model, the optimization objective, and the optimization constraints; obtaining the optimal solution of the ply material and material thickness using an optimization algorithm based on the preliminary prediction results; and determining the optimized model of the blade based on the optimal solution of the ply material and material thickness.

7. A structural optimization system for large wind turbine blades, characterized in that, The system includes: a data acquisition module for acquiring structural and material data of the blade; a model building module for establishing an initial model of the blade based on the structural and material data using finite element simulation; a target setting module for setting minimizing the blade's mass as the optimization target; a constraint setting module for using pre-set maximum tip offset and minimum frequency deviation as optimization constraints, wherein the frequency deviation is the difference between the blade's rotational frequency and its natural frequency; and a structural optimization module for optimizing the initial model based on the optimization target and the optimization constraints to obtain an optimized model. The structural data includes multiple cross-sectional positions and corresponding data such as twist angle, chord length, pitch position, load, and airfoil data. The material data includes corresponding material distribution data. The model building module is also used to establish the initial model of the blade based on the multiple cross-sectional positions and corresponding data such as twist angle, chord length, pitch position, load, airfoil, and material distribution data.

8. A smart device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the method of any one of claims 1 to 6.

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