A design method for large and ultra-large wind turbines based on compressible flow principles

Abstract

The present invention discloses a design method for large and super-large wind turbines based on the compressible flow principle, comprising: constructing a high tip speed ratio C P ‑λ curve, which converts the maximum wind energy capture efficiency C Pmax The corresponding optimal tip speed ratio λ opt Substantially increased, overall widening of C P The proposed method employs a novel λ-λ curve; optimizes blade parameters; utilizes high-modulus, low-density carbon fiber materials and employs a structural property optimization design method based on biomimetic structure development to optimize blade structural design; employs owl-like serrated trailing edge noise reduction technology and design methods to suppress blade tip trailing edge noise, the primary aerodynamic noise source; constructs an aerodynamic-servo-elastic coupling model for the entire turbine, employing multidisciplinary optimization design and model-based systems engineering methods to jointly model and systematically optimize the subsystems. This invention achieves the design of large and ultra-large wind turbines with high efficiency, high reliability, and low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set design, and in particular to a design method for large and super-large wind turbine sets based on the compressible flow principle. Background Art

[0002] With the continued development of the wind power industry, the large-scale and ultra-large-scale wind turbines have become a key development trend both now and in the future. This trend significantly increases the power generation capacity of each unit while also, to a certain extent, reducing the subsequent infrastructure construction, operation and maintenance, and transmission costs, thereby improving the overall return on investment.

[0003] In recent years, large-capacity wind turbines have achieved continuous breakthroughs in both onshore and offshore applications, with single-unit capacity gradually increasing, offering enhanced power generation performance and adaptability. For example, the capacity of offshore floating wind turbines has reached 20MW and above, and onshore wind turbines have achieved installed capacity in the 15MW range. These technological advances have promoted the efficient utilization of wind resources in deep-sea and high-wind-speed areas and provided important support for the large-scale development of renewable energy. In the long term, mainstream wind turbine manufacturers will gradually begin developing 30-40MW turbines.

[0004] However, as the power generation capacity of large-scale wind turbines continues to increase, if the traditional turbine amplification design method is continued, the following technical challenges will be faced: (1) the size and capacity of the main components of the turbine, such as blades, gearboxes, main shafts, motors, towers, and foundations, will increase significantly, greatly increasing the material and processing costs, and the current manufacturing process technology has reached its limit; (2) the cost of turbine hoisting and operation and maintenance will increase significantly; (3) the ultra-long and flexible blades used in ultra-large wind turbines face structural strength and stability issues, manufacturing and transportation have difficulties with precision processes and transportation restrictions, and operation and maintenance have difficulties in detection and maintenance and performance degradation management. All of these will lead to further increases in costs, which will directly affect the large-scale application of large wind turbines.

[0005] To sum up, traditional design ideas will gradually become a technical bottleneck for the development of super-large units, and will seriously restrict the process of large-scale, efficient, highly reliable and low-cost wind energy development. It is not conducive to the in-depth development and utilization of wind energy resources, and it is difficult to support the industry's future transformation and upgrading to a high-quality development stage.

[0006] Therefore, how to provide a design method for large and ultra-large wind turbines based on the compressible flow principle has become a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides a design method for large and ultra-large wind turbines based on the compressible flow principle, which realizes the design of large and ultra-large wind turbines with high efficiency, high reliability and low cost.

[0008] The present invention solves the technical problem by adopting the following technical solutions:

[0009] A design method for large and ultra-large wind turbines based on compressible flow principles, comprising:

[0010] C P -λ curve high tip speed ratio design: Construct high tip speed ratio C P -λ curve, first of all, the unit is working C P -λ curve is designed to maximize the wind energy capture efficiency C Pmax The corresponding optimal tip speed ratio λ opt Substantially increase and broaden C P -λ curve;

[0011] Blade design under compressible flow conditions: Design the optimal airfoil and blade geometry, involving optimization of blade chord length, twist angle, and thickness parameters, so that the optimized blades can better adapt to compressible flow conditions;

[0012] Blade structure design: Using high modulus, low-density carbon fiber materials, and adopting a structural property optimization design method based on bionic structure development to perform blade structure design optimization;

[0013] Bionic noise reduction design of blades under compressible flow: The owl-like serrated trailing edge noise reduction technology and design methods are used to suppress the blade tip trailing edge noise, the main aerodynamic sound source of the blade;

[0014] Systematic collaborative design of the whole machine: Build an aerodynamic-servo-elastic coupling model of the whole machine, adopt multidisciplinary optimization design and model-based system engineering methods, and conduct joint modeling and systematic collaborative optimization design of subsystems such as wind turbine tower, hub, main shaft, drive chain, generator, control, yaw and pitch.

[0015] Further, the method of optimizing the blade includes:

[0016] The first step is to use the high tip speed ratio C P -λ curve design requirements, taking into account local wind farm wind resources and wind load conditions as input conditions for customized design;

[0017] The second step is to build an automated design platform based on Isight software to achieve coupled modeling of airfoils and blades, and use high-precision CFD calculations to establish an airfoil-blade aerodynamic training set.

[0018] In the third step, based on the airfoil-blade aerodynamic training set, an intelligent optimization platform is built to train the Kriging proxy model until the accuracy requirements are met. Then, combined with the NSGA-II optimization algorithm, multi-objective optimization is performed in the trained proxy model to finally output the optimal blade geometric parameters.

[0019] Furthermore, the blade structure design optimization method includes:

[0020] The first step is to determine the main beam location and cross-sectional parameters. The main beam is placed along the maximum thickness line along the blade span, covering the transition area from the blade root to the blade tip. The main beam cross-section completely covers the cross-sectional web structure, and the root area requires additional strength to resist the blade root bending moment.

[0021] The second step is to determine the required material properties and material application space, using a hybrid carbon fiber approach to achieve the required blade strength and weight reduction. Furthermore, the material's applicability is further improved during the optimization phase based on biomimetic properties.

[0022] The third step is to determine the relevant parameters of the blade webs and design them based on the blade geometry and main beam parameters. Two to three webs are set, one on the leading edge and one on the trailing edge of the main beam. The web spacing is determined by the blade cross-section height and the selected material properties, and the web thickness is determined by the shear load and shear strength.

[0023] The fourth step is to determine the blade ply parameters, based on the ultimate bending moment in the flapping direction and material allowable stress Calculate the number of plies for the main beam; then calculate the ply angles, with 0° plies being the main beam and ±45° plies inserted in the blade root area to resist torsional loads;

[0024] The fifth step is to determine the core material filled inside the web, the parameters of the sandwich layer, and the connection method between the web end and the main beam;

[0025] The sixth step is to determine the fitness function and constraints based on the optimization algorithm based on the preliminary determination of the basic parameters. The design variables and optimization variables of the optimization algorithm are established based on the principle of minimum blade mass. The structural characteristics, load distribution and stress magnitude of the blade are analyzed, and then the structural parameters of the ply thickness are optimized according to the strength criterion. Based on the above design, a blade structure model is established, and finite element analysis is used to optimize the carbon fiber blade structure design. The fatigue test bench is used to simulate the full life cycle load to verify the fatigue resistance of the carbon fiber blade.

[0026] Furthermore, the blade web related parameters include web position, web spacing, and web structure.

[0027] Furthermore, the blade ply parameters include ply angle, ply thickness, and ply position information.

[0028] Furthermore, the bionic noise reduction design method for blades under compressible flow includes:

[0029] In the first step, combining the sawtooth trailing edge modeling and surrogate modeling methods, high-precision CFD was used to calculate the blade flow and acoustic field data corresponding to different wind speeds, blade parameters, sawtooth structural parameters, and overall operating parameters.

[0030] In the second step, the aforementioned flow and acoustic field data are used as a driver to train a neural network model to construct a blade noise prediction model under the influence of different incoming flows, bionic structures, blades, and overall machine operation.

[0031] The third step is to optimize the blade noise reduction design of the serration structure parameters based on the above prediction model.

[0032] Furthermore, the method of systematic collaborative design of the whole machine includes:

[0033] The first step is system modeling and parameter definition. Based on the MBSE method, a complete system architecture model is constructed. The interfaces, dependencies, and functional relationships between subsystems such as blades, hubs, main shafts, towers, gearboxes, generators, control systems, yaw systems, and pitch control systems are defined. A model hierarchy is established to achieve a complete mapping from the requirements layer to the physical layer.

[0034] The second step is to establish an aerodynamic-servo-elastic coupling model. Driven by the MBSE system model, an aerodynamic-elastic-control coupling model for the entire turbine is constructed, integrating blade flexibility, tower vibration, main shaft torsion, and the interactions between the generator and control system. This model is parameterized to support the unified management and iterative optimization of key design variables in the subsequent MDO process.

[0035] The third step is to define design variables and optimization objectives, and to clarify adjustable design parameters through system modeling. Based on multi-objective collaborative optimization, the objective function includes maximizing annual power generation, minimizing structural mass, minimizing fatigue loads, and optimizing control response performance.

[0036] The fourth step is to set typical and extreme load conditions and use them in the MDO process to evaluate the dynamic response, peak stress, and fatigue cumulative damage of each design combination under multiple scenarios;

[0037] The fifth step is collaborative optimization of the entire aircraft design. Using the MDO method, under the system parameter constraints and interface consistency provided by MBSE, a multi-objective optimization algorithm is used to explore and converge the design space. The parameters of each subsystem are co-evolved in a unified model to achieve dynamic coupling adjustment between design variables. Control strategy parameters and structural parameters are collaboratively optimized to balance aerodynamic performance, load control, and reliability.

[0038] The sixth step is to verify system consistency and model feedback. After optimization is complete, the optimal solution is backfilled into the MBSE system model, automatically verifying the consistency of each subsystem's constraints and the compatibility of system interfaces to ensure that the optimization results are feasible for engineering implementation. If there are any conflicts or design violations, the MBSE model is used to track the source of the problem, allowing for rapid structural reconstruction and re-optimization.

[0039] The seventh step is to output the final design plan, the configuration parameters and control settings of the key components of the whole machine that meet the collaborative optimization goals, and provide a digital design basis for the whole machine prototype manufacturing and engineering implementation.

[0040] Furthermore, adjustable design parameters include tower height and wall thickness distribution, spindle size, gear ratio, controller bandwidth, and yaw response time.

[0041] Furthermore, the configuration parameters and control settings of the key components of the whole machine include tower structure layout, main shaft design scheme, transmission chain parameters, controller regulator parameters, yaw and pitch strategy configuration.

[0042] Beneficial effects of the present invention:

[0043] First, on a technical level, by optimizing blade design to increase the tip speed ratio under rated operating conditions, cost reductions have been achieved for key wind turbine components, including lightweighting and performance improvements in the mechanical structure (such as the nacelle, drive chain, tower, etc.) and blades, significantly reducing overall machine load and manufacturing costs. Second, the current market demand for renewable energy continues to grow, and the development prospects for onshore and offshore wind turbines are broad, providing a huge market space for the commercial application of new wind turbines. Finally, based on the above technical feasibility and market demand, it can be clearly concluded that this new wind turbine has great potential in terms of economic efficiency. It can not only meet the industry's demand for cost reduction and efficiency improvement, but also inject new impetus into the development of the renewable energy field. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flow chart of the method of the present invention.

[0045] Figure 2 C before and after blade optimization P -λ curve comparison chart;

[0046] Figure 3 This is a parameter comparison chart before and after blade optimization. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0048] Addressing the limitations of existing technologies, the present invention aims to significantly increase wind turbine speed, enabling it to operate at a high tip speed ratio (λ), thereby reducing the loads on key transmission chain components such as the low-speed shaft, gearbox, high-speed shaft, and motor, as well as on the tower and foundation. The invention also employs integrated airfoil-blade design to compensate for the power generation loss associated with high speeds. All-carbon fiber blades replace traditional glass fiber materials to address the reduced blade fatigue life associated with high speeds and further reduce overall loads. Furthermore, biomimetic noise reduction technology is employed to further suppress the primary aerodynamic noise generated by high speed blades. Consequently, the invention provides a highly efficient, reliable, and cost-effective design method for large and ultra-large wind turbines.

[0049] Reference Attachment Figure 1 The present invention provides a design method for large and ultra-large wind turbines based on the compressible flow principle, comprising:

[0050] C P -λ curve high tip speed ratio design: Construct high tip speed ratio C P -λ curve (such as Figure 2 As shown), first, when the unit is working, C P -λ curve is designed to reduce the traditional maximum wind energy capture efficiency C Pmax The corresponding optimal tip speed ratio λ opt (=8, shown by the black line) increases significantly (shown by the red line, where the speed is doubled and λ opt = 16 as an example), and widen C P -λ curve;

[0051] Blade design under compressible flow conditions: Design the optimal airfoil and blade geometry, involving optimization of blade chord length, twist angle, and thickness parameters to make the optimized blade better adapted to compressible flow conditions. The specific steps are detailed below:

[0052] The first step is to use the high tip speed ratio C P -λ curve design requirements, while taking into account the local wind farm wind resources and wind load conditions as input conditions for customized design.

[0053] The second step is to build an automated design platform based on Isight software to achieve coupled modeling of airfoils and blades, and use high-precision CFD calculations to establish an airfoil-blade aerodynamic training set.

[0054] The third step is to build an intelligent optimization platform based on the airfoil-blade aerodynamic training set, train the Kriging proxy model until it meets the accuracy requirements, and then combine it with the NSGA-II optimization algorithm to perform multi-objective optimization in the trained proxy model, and finally output the optimal blade geometric parameters.

[0055] Blade structure design: Using high modulus, low density carbon fiber materials, and adopting a structural property optimization design method based on bionic structure development, we perform blade structure design optimization to further achieve blade lightweighting and effectively increase fatigue life. Blade structure design optimization is achieved through the following steps:

[0056] The first step is to determine the main beam location and cross-sectional parameters. The main beam is placed along the maximum thickness line along the blade span, covering the transition area from the blade root to the blade tip. The main beam cross-section completely covers the cross-sectional web structure, and the root area requires additional strength to resist the blade root bending moment.

[0057] The second step is to determine the required material properties and material application space. Selecting the right material can not only improve the performance of wind turbine blades but also reduce production costs. This method uses hybrid carbon fiber to achieve the requirements of increasing blade strength and reducing weight, and further improves the material applicability during the optimization stage based on biomimetic properties.

[0058] The third step is to determine the relevant parameters of the blade web, including web position, web spacing, and web structure. The design is based on the blade geometry and main beam parameters. Two to three webs are set, one on the leading edge of the main beam (to resist flapping loads) and the other on the trailing edge (to resist shimmying loads). The web spacing is determined by the blade cross-section height and the selected material properties, and the web thickness is determined by the shear load and shear strength.

[0059] The fourth step is to determine the blade laying parameters, including laying angle, laying thickness, laying position and other information. and material allowable stress Calculate the number of main beam plies; then calculate the ply angles. The main beam is mainly laid with 0° plies (fibers along the span direction), and ±45° plies are inserted in the blade root area to resist torsional loads.

[0060] The fifth step is to determine the core material filled inside the web, the parameters of the sandwich layer, and the connection method between the web end and the main beam;

[0061] The sixth step is to determine the fitness function and constraints based on the optimization algorithm based on the preliminary determination of the basic parameters. The design variables and optimization variables of the optimization algorithm are established based on the principle of minimum blade mass. The structural characteristics, load distribution and stress magnitude of the blade are analyzed, and then the structural parameters such as the ply thickness are optimized according to the strength criterion. Based on the above design, a blade structure model is established, and finite element analysis (FEA) is used to optimize the carbon fiber blade structure design. The fatigue test bench is used to simulate the full life cycle load to verify the fatigue resistance of the carbon fiber blade.

[0062] Bionic noise reduction design of blades under compressible flow: The owl-like serrated trailing edge noise reduction technology and design methods are used to suppress the blade tip trailing edge noise, the main aerodynamic sound source of the blade. The bionic noise reduction design method of blades under compressible flow includes:

[0063] In the first step, combining the sawtooth trailing edge modeling and surrogate modeling methods, high-precision CFD was used to calculate the blade flow and acoustic field data corresponding to different wind speeds, blade parameters, sawtooth structural parameters, and overall operating parameters.

[0064] In the second step, the aforementioned flow and acoustic field data are used as a driver to train a neural network model to construct a blade noise prediction model under the influence of different incoming flows, bionic structures, blades, and overall machine operation.

[0065] The third step is to optimize the blade noise reduction design of the serration structure parameters based on the above prediction model.

[0066] Systematic collaborative design of the entire machine: Build an aerodynamic-servo-elastic coupling model for the entire machine, and use multidisciplinary design optimization (MDO) and model-based systems engineering (MBSE) methods to jointly model and systematically collaboratively optimize the design of subsystems such as the wind turbine tower, hub, main shaft, drive train, generator, control, yaw, and pitch control, aiming to balance the relationship between the aerodynamic performance, structural stiffness, dynamic control response capability, and fatigue life of the entire machine. The methods for systematic collaborative design of the entire machine include:

[0067] The first step is system modeling and parameter definition. Based on the MBSE method, a complete system architecture model is constructed. The interfaces, dependencies, and functional relationships between subsystems such as blades, hubs, main shafts, towers, gearboxes, generators, control systems, yaw systems, and pitch control systems are defined. A model hierarchy is established to achieve a complete mapping from the requirements layer to the physical layer.

[0068] The second step is to establish an aerodynamic-servo-elastic coupling model. Driven by the MBSE system model, an aerodynamic-elastic-control coupling model for the entire turbine is constructed, integrating blade flexibility, tower vibration, main shaft torsion, and the interactions between the generator and control system. This model is parameterized to support the unified management and iterative optimization of key design variables in the subsequent MDO process.

[0069] The third step is to define design variables and optimization objectives. Through system modeling, adjustable design parameters are clearly defined, including tower height and wall thickness distribution, main shaft size, gear ratio, controller bandwidth, yaw response time, etc. Based on multi-objective collaborative optimization, the objective function includes maximizing annual energy production, minimizing structural mass, minimizing fatigue load, and optimizing control response performance.

[0070] The fourth step is to set typical and extreme load conditions and use them in the MDO process to evaluate the dynamic response, peak stress, and fatigue cumulative damage of each design combination under multiple scenarios;

[0071] The fifth step involves collaborative optimization of the entire system. Using the MDO approach, under the system parameter constraints and interface consistency guarantees provided by MBSE, a multi-objective optimization algorithm (such as NSGA-II) is used to explore and converge the design space. Subsystem parameters evolve together within a unified model, enabling dynamic coupling adjustment between design variables. Control strategy parameters (such as pitch PI gain and generator torque response) are collaboratively optimized with structural parameters (such as tower flexibility and main shaft support spacing) to balance aerodynamic performance, load control, and reliability.

[0072] The sixth step is to verify system consistency and model feedback. After optimization is complete, the optimal solution is backfilled into the MBSE system model, automatically verifying the consistency of each subsystem's constraints and the compatibility of system interfaces to ensure that the optimization results are feasible for engineering implementation. If there are any conflicts or design violations, the MBSE model is used to track the source of the problem, allowing for rapid structural reconstruction and re-optimization.

[0073] The seventh step is to output the final design plan, which includes the configuration parameters and control settings of the key components of the whole machine that meet the collaborative optimization goals, including the tower structure layout, main shaft design plan, transmission chain parameters, controller regulator parameters, yaw and pitch strategy configuration, etc., to provide a digital design basis for the whole machine prototype manufacturing and engineering implementation.

[0074] The beneficial effects of the present invention are described by taking a typical 10MW unit recently launched by the International Energy Agency (IEA) as an example.

[0075] (1) High power generation: By increasing the tip speed ratio and widening the CP-λ curve, the average wind energy capture efficiency when the tip speed ratio fluctuates is improved; at the same time, by adopting the integrated aerodynamic design technology of airfoil blades, the new blades can better adapt to the compressible flow state. The lift-to-drag ratio of the optimized blade airfoil in the compressible flow state can be increased by about 12%-18%. The excellent aerodynamic performance of the corresponding blade directly increases the power generation by about 10%.

[0076] Strong reliability: After the speed is increased, the low-speed shaft torque of the unit is reduced by about 50% compared with the same level units. The speed comparison before and after blade optimization is as follows: Figure 3 As shown in (a), the torque comparison before and after blade optimization is as follows: Figure 3 As shown in (b); the pitching and overturning moments of the yaw component are reduced by 11% and 50%, respectively. The comparison of the yaw and pitching moments before and after blade optimization is shown in (b). Figure 3 As shown in (c), the comparison of the yaw overturning moment before and after blade optimization is as follows: Figure 3 As shown in (d) in the figure, the tower top pitching and overturning moments decreased by 15% and 50%, respectively. The tower top pitching moments before and after blade optimization are compared. Figure 3 As shown in (e), the tower top overturning moment before and after blade optimization is compared. Figure 3 As shown in (f), the tower bottom pitching and overturning moments decreased by 12% and 52%, respectively. The tower bottom pitching moments before and after blade optimization are compared. Figure 3 As shown in (g), the overturning moment at the bottom of the tower before and after blade optimization is compared. Figure 3 As shown in (h) in the figure, it can be seen that the load of the entire machine is reduced to a certain extent, and the safety and reliability are greatly improved.

[0077] At the same time, in the optimization of the blade structure, high modulus, low density materials such as carbon fiber are chosen to replace the original traditional blade glass fiber material, which effectively improves the fatigue and life problems of the high-speed blade structure and ensures the stability and safety of the blade under high-speed rotation.

[0078] (3) Low cost: The high tip speed ratio wind turbine proposed in the present invention has a significantly lower R&D cost than units of the same capacity. Furthermore, the power generation capacity is increased by 10% due to the new integrated aerodynamic design technology of airfoil blades, and additional economic benefits can be obtained during the entire life cycle of the unit (based on the local wind resources and grid-connected electricity price of a certain onshore wind farm). In addition, the application of carbon fiber blades can reduce the weight and load of the entire unit, greatly improve the reliability of the unit, and significantly reduce the subsequent operation and maintenance costs during the entire life cycle of the unit.

[0079] Secondly, by using easy-to-install, low-cost serrated trailing edge technology on the blade tip, the main acoustic intensity of the blade can be very economically reduced, reducing reliance on external noise reduction facilities such as sound barriers, thereby avoiding noise reduction costs. Furthermore, bionic noise reduction technology can reduce equipment operating restrictions caused by excessive noise levels, improve power generation efficiency, and increase revenue.

[0080] In addition, the innovative technology of this invention plays an important supporting role in reducing costs in multiple links such as transportation, lifting, debugging, and operation and maintenance involved in subsequent large-scale industrialization.

[0081] (4) Environmentally friendly: The owl-like serrated trailing edge noise reduction technology suppresses the main aerodynamic sound source of the blade - the blade tip trailing edge noise, solves the high tip speed blade noise problem, and meets the requirements of environmental impact assessment indicators.

[0082] In summary, first, on a technical level, by optimizing blade design to improve the tip speed ratio under rated operating conditions, cost reductions have been achieved for key components of wind turbines, including lightweighting and performance improvements in mechanical structures (such as the nacelle, drive chain, tower, etc.) and blades, significantly reducing overall machine load and manufacturing costs. Secondly, the current market demand for renewable energy continues to grow, and the development prospects for onshore and offshore wind turbines are broad, providing a huge market space for the commercial application of new wind turbines. Finally, based on the above technical feasibility and market demand, it can be clearly concluded that this new wind turbine has great potential in terms of economic efficiency. It can not only meet the industry's demand for cost reduction and efficiency improvement, but will also inject new impetus into the development of the renewable energy field.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A design method for large and ultra-large wind turbines based on the compressible flow principle, characterized in that: include: C P -λ curve high tip speed ratio design: Construct high tip speed ratio C P -λ curve, first of all, the unit is working C P -λ curve is designed to maximize the wind energy capture efficiency C Pmax The corresponding optimal tip speed ratio λ opt Double and widen C P -The period of the lambda curve; Blade design under compressible flow conditions: Design the optimal airfoil and blade geometry, involving optimization of blade chord length, twist angle, and thickness parameters, so that the optimized blades can better adapt to compressible flow conditions; Blade structure design: Using high modulus, low-density carbon fiber materials, and adopting a structural property optimization design method based on bionic structure development to perform blade structure design optimization; Bionic noise reduction design of blades under compressible flow: The owl-like serrated trailing edge noise reduction technology and design methods are used to suppress the blade tip trailing edge noise, the main aerodynamic sound source of the blade; Systematic collaborative design of the entire turbine: Build an aerodynamic-servo-elastic coupling model for the entire turbine. Adopt multidisciplinary optimization design and model-based systems engineering methods to jointly model and systematically collaboratively optimize the design of subsystems such as the wind turbine tower, hub, main shaft, drive train, generator, control, yaw, and pitch control. Methods for optimizing blades include: The first step is to use the high tip speed ratio C P -λ curve design requirements, taking into account local wind farm wind resources and wind load conditions as input conditions for customized design; The second step is to build an automated design platform based on Isight software to achieve coupled modeling of airfoils and blades, and use high-precision CFD calculations to establish an airfoil-blade aerodynamic training set. The third step is to build an intelligent optimization platform based on the airfoil-blade aerodynamic training set, train the Kriging proxy model until it meets the accuracy requirements, and then combine it with the NSGA-II optimization algorithm to perform multi-objective optimization in the trained proxy model, ultimately outputting the optimal blade geometry parameters. Blade structure design optimization methods include: The first step is to determine the main beam location and cross-sectional parameters. The main beam is placed along the maximum thickness line along the blade span, covering the transition area from the blade root to the blade tip. The main beam cross-section completely covers the cross-sectional web structure, and the root area requires additional strength to resist the blade root bending moment. The second step is to determine the required material properties and material application space, using a hybrid carbon fiber approach to achieve the required blade strength and weight reduction. Furthermore, the material's applicability is further improved during the optimization phase based on biomimetic properties. The third step is to determine the blade web parameters and design them based on the blade geometry and main beam parameters. Two to three webs are set, one on the leading and one on the trailing edge of the main beam. The web spacing is determined by the blade cross-section height and the selected material properties, and the web thickness is determined by the shear load and shear strength. The blade web parameters include web position, web spacing, and web structure. The fourth step is to determine the blade ply parameters, based on the ultimate bending moment in the flapping direction and material allowable stress Calculate the number of plies on the main beam; then calculate the ply angle. The main beam is mainly ply at 0°, and ±45° plies are inserted in the blade root area to resist torsional loads. The blade ply parameters include ply angle, ply thickness, and ply position information. The fifth step is to determine the core material filled inside the web, the parameters of the sandwich layer, and the connection method between the web end and the main beam; In the sixth step, based on the preliminary determination of basic parameters, the fitness function is determined according to the optimization algorithm, the constraints are clarified, and the design and optimization variables of the optimization algorithm are established based on the principle of minimum blade mass. The structural characteristics, load distribution and stress magnitude of the blade are analyzed, and the structural parameters of the ply thickness are optimized according to the strength criterion. Based on the above design, a blade structural model is established, and the carbon fiber blade structural design is optimized using finite element analysis. The fatigue resistance of the carbon fiber blade is verified by simulating the full life cycle load on a fatigue test bench. The bionic noise reduction design method for blades under compressible flow includes: In the first step, combining the sawtooth trailing edge modeling and surrogate modeling methods, high-precision CFD was used to calculate the blade flow and acoustic field data corresponding to different wind speeds, blade parameters, sawtooth structural parameters, and overall operating parameters. In the second step, the aforementioned flow and acoustic field data are used as a driver to train a neural network model to construct a blade noise prediction model under the influence of different incoming flows, bionic structures, blades, and overall machine operation. The third step is to optimize the blade noise reduction design based on the sawtooth structural parameters according to the above prediction model; The methods for systematic collaborative design of the entire machine include: The first step is system modeling and parameter definition. Based on the MBSE method, a complete system architecture model is constructed. The interfaces, dependencies, and functional relationships between subsystems such as blades, hubs, main shafts, towers, gearboxes, generators, control systems, yaw systems, and pitch control systems are defined. A model hierarchy is established to achieve a complete mapping from the requirements layer to the physical layer. The second step is to establish an aerodynamic-servo-elastic coupling model. Driven by the MBSE system model, an aerodynamic-elastic-control coupling model for the entire turbine is constructed, integrating blade flexibility, tower vibration, main shaft torsion, and the interactions between the generator and control system. This model is parameterized to support the unified management and iterative optimization of key design variables in the subsequent MDO process. The third step is to define design variables and optimization objectives, and to clarify adjustable design parameters through system modeling. Based on multi-objective collaborative optimization, the objective function includes maximizing annual power generation, minimizing structural mass, minimizing fatigue loads, and optimizing control response performance. The fourth step is to set typical and extreme load conditions and use them in the MDO process to evaluate the dynamic response, peak stress, and fatigue cumulative damage of each design combination under multiple scenarios; The fifth step is collaborative optimization of the entire aircraft design. Using the MDO method, under the system parameter constraints and interface consistency provided by MBSE, a multi-objective optimization algorithm is used to explore and converge the design space. The parameters of each subsystem are co-evolved in a unified model to achieve dynamic coupling adjustment between design variables. Control strategy parameters and structural parameters are collaboratively optimized to balance aerodynamic performance, load control, and reliability. The sixth step is to verify system consistency and model feedback. After optimization is complete, the optimal solution is backfilled into the MBSE system model, automatically verifying the consistency of each subsystem's constraints and the compatibility of system interfaces to ensure that the optimization results are feasible for engineering implementation. If there are any conflicts or design violations, the MBSE model is used to track the source of the problem, allowing for rapid structural reconstruction and re-optimization. The seventh step is to output the final design plan, the configuration parameters and control settings of the key components of the whole machine that meet the collaborative optimization goals, and provide a digital design basis for the whole machine prototype manufacturing and engineering implementation.

2. A design method for large and super-large wind turbines based on the compressible flow principle according to claim 1, characterized in that: Adjustable design parameters include tower height and wall thickness distribution, spindle size, gear ratio, controller bandwidth, and yaw response time.

3. The design method for large and super-large wind turbines based on the compressible flow principle according to claim 1, characterized in that: The configuration parameters and control settings of the key components of the whole machine include tower structure layout, main shaft design scheme, transmission chain parameters, controller regulator parameters, and yaw and pitch strategy configuration.

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