A method and device for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions

Abstract

The present invention provides a method and device for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions, including: considering design methods such as sweepback and bend-twist coupling adopted in the design process of large flexible wind turbine blades to reduce loads, and establishing a fitting curve for the elastic twist angle of the blade; considering the vibration velocity generated by large flexible blades under turbulent wind conditions, and establishing a fitting curve for the blade vibration velocity according to the mode superposition method; establishing a momentum blade element method load calculation process considering the elastic twist angle and the vibration velocity caused by turbulent wind; defining the prediction of the ultimate aerodynamic load as an optimization process under certain constraint conditions, and using the above-mentioned method for predicting the ultimate aerodynamic load to predict the ultimate aerodynamic load of large flexible blades. The present invention can simplify the load evaluation process in the traditional wind turbine blade design process and realize the rapid prediction of the ultimate aerodynamic load of wind turbine blades.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and particularly relates to a method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions. Background Art

[0002] With the geometric growth of the blade weight and blade load brought about by the large-scale of wind turbine blades, it has a great impact on the blade structure design and load assessment. At present, for the assessment method of wind turbine blade loads, it is usually to use commercial software such as Bladed to evaluate the blade loads under different working conditions. This method not only requires a detailed blade model and data, but also takes a long time and has low efficiency.

[0003] Therefore, in order to support the rapid iterative design of large flexible blades, it is very urgent to establish a fast method for predicting the ultimate aerodynamic load of large flexible wind turbine blades under turbulent wind conditions, and it has important theoretical research and application value. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method and device for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions, which can realize the fast prediction function of the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent working conditions.

[0005] The present invention is realized through the following technical solutions:

[0006] A method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions, comprising:

[0007] S1: Considering the design method of sweep and bend-twist coupling adopted in the current design process of large flexible wind turbine blades to reduce loads, establish a fitting curve of the elastic twist angle of the blade from the root to the tip;

[0008] S2: Considering the blade vibration velocity generated by the random change of wind speed of a large flexible wind turbine blade under turbulent wind conditions, establish a fitting curve of the vibration velocity of the blade from the root to the tip according to the mode superposition method;

[0009] S3: Take the blade elastic twist angle fitting curve formed in S1 and the blade vibration velocity fitting curve formed in S2 as the correction parameters of the twist angle and relative wind speed of the current blade element in the process of calculating the load by the momentum blade element method, and establish a load calculation process of the momentum blade element method considering the blade elastic twist angle and the vibration velocity under turbulent working conditions;

[0010] S4: Adopt the load calculation process of the momentum blade element method considering the blade elastic twist angle and the vibration velocity under turbulent working conditions in S3, use the particle swarm algorithm, take the maximum root moment as the optimization target, and realize the fast prediction of the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions through optimization calculation.

[0011] Preferably, in S1, the control parameters of the fitting curve of the elastic twist angle from the blade root to the blade tip are the elastic twist angle values at 0%, 50%, 66.7%, 83.3% and 100% of the blade.

[0012] Preferably, in S2, the control parameters of the fitting curve of the vibration velocity from the blade root to the blade tip are the superposition parameters of the first 4 modes in the flap and lead-lag directions of the blade and the maximum vibration velocity of the blade.

[0013] Preferably, in S3, a momentum blade element method load calculation process considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions is established, and the input parameters include wind speed, pitch angle, rotational speed, air density, blade structural parameters, blade aerodynamic parameters, blade elastic twist angle fitting parameters and blade vibration velocity fitting parameters.

[0014] Preferably, in S3, the momentum blade element method load calculation process considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions takes the elastic twist angle and the vibration velocity as the correction parameters for calculating the angle of attack and the relative wind speed of each blade element.

[0015] More preferably, in S3, the momentum blade element method load calculation process considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions obtains the corrected axial induction factor a and tangential induction factor a′ of each blade element through iterative calculation, so as to calculate the load and root bending moment of each blade element.

[0016] Preferably, in S4, the design parameters for predicting the ultimate load of the blade using the particle swarm optimization algorithm are wind speed, pitch angle, rotational speed, blade elastic twist angle curve fitting parameters, and blade vibration velocity fitting parameters.

[0017] More preferably, in S4, the optimization objective for predicting the ultimate load of the blade using the particle swarm optimization algorithm is the root bending moment, and the optimization constraints are the pitch angle and rotational speed under normal power generation conditions.

[0018] More preferably, in S4, the set parameters of the particle swarm optimization algorithm include the number of particles in each generation, the maximum number of iterations, the convergence criterion, the optimization objective function, and the optimization constraint function.

[0019] More preferably, in S4, the criterion for the end of the particle swarm optimization algorithm is that the optimization result converges, that is, the difference between the results of 10 consecutive rounds of iterative calculations is less than 1e -4 , or the set maximum number of iterations is reached. The calculation result of the final convergence of the optimization algorithm is the ultimate aerodynamic load of the large flexible wind turbine blade under turbulent wind conditions.

[0020] The present invention discloses a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions are implemented.

[0021] The present invention discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects:

[0023] The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions disclosed by the present invention establishes a load calculation process for a large flexible blade under turbulent wind conditions by using the elastic twist angle of the blade and the vibration velocity of the blade under turbulent wind conditions as the correction amounts of the element twist angle and the relative wind speed in the load calculation process of the momentum-element method. The maximum load calculation process screened by the traditional load calculation is defined as an optimization problem under certain constraints, and the particle swarm algorithm is used to optimize to obtain the maximum aerodynamic load at the blade root, so as to realize the rapid prediction of the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions. Compared with the prior art, the present invention can flexibly adjust the curve fitting method of the elastic twist angle curve of the blade according to the use of the blade sweep and bending-torsion coupling design method, and can adapt to the load calculation requirements of different types of blades; by considering the element method load calculation with elastic twist angle and relative wind speed correction, the solution of the dynamic blade load under turbulent wind conditions is converted into the solution of the static blade load. The prediction method provided by the present invention does not require detailed blade structure models and data such as chord length, twist angle, section thickness, section bending stiffness, section centroid position, and unit length weight at all stations of the blade, and only requires a few parameters such as blade aerodynamic shape data and modal vibration modes to quickly predict the ultimate aerodynamic load of the blade under turbulent wind conditions, greatly simplifying the calculation process of the ultimate aerodynamic load of the wind turbine blade under turbulent wind conditions, and having good application prospects. Description of the Drawings

[0024] The following further explains the present invention with reference to the drawings and embodiments:

[0025] Figure 1 It is a schematic flow chart of the method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions of the present invention;

[0026] Figure 2 It is a schematic diagram of the fitting curve of the elastic twist angle of the blade and the superimposed fitting curve of the structural / elastic twist angle;

[0027] Figure 3Schematic diagram of the angle of attack calculation method under different wind speeds, rotational speeds, and vibration speeds. Detailed implementation mode

[0028] The present invention will be described in detail below. The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Figure 1 Shows a flowchart of a method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to an exemplary embodiment of the present invention.

[0030] Refer to Figure 1 , in step S1, a fitting curve of the elastic twist angle of the blade from the root to the tip is established.

[0031] Preferably, the elastic twist angle of the above-mentioned blade is generated by the blade sweep design, the bending-torsion coupling design, and the blade structural twist angle under the action of wind load. The blade elastic twist angle can be regarded as the superposition of three effects. In specific use, the curve fitting method of the blade elastic twist angle can be flexibly adjusted according to the use of the blade sweep and bending-torsion coupling design methods in the actual design.

[0032] Among them, the sweep design method refers to the blade offsetting towards the trailing edge direction to achieve the purpose of reducing the root load. The bending-torsion coupling design method refers to generating the bending-torsion coupling characteristic by offsetting the fiber angle on the blade surface, making the aeroelastic center of the blade unbalanced with the torsion axis, so as to achieve the purpose of reducing the root load.

[0033] Figure 2 Schematic diagram of the fitting curve of the blade elastic twist angle and the superposition fitting curve of the structure / elastic twist angle. Among them, the dotted line represents the blade structural twist angle curve, the dashed line represents the blade elastic twist angle curve, and the solid line represents the superposition curve of the blade structure / elastic twist angle.

[0034] Preferably, the control parameters of the above-mentioned blade elastic twist angle fitting curve are the elastic twist angle values at 0%, 50%, 66.7%, 83.3%, and 100% of the blade. The elastic twist angle value at 0% is 0°. Among them, the fitting curve in the 0% - 50% section is a quadratic curve with the axis of symmetry x = 0, and the control points are the elastic twist angle values at 0% and 50% of the blade. The fitting curve in the 50% - 100% section is a cubic curve, and the control points are the elastic twist angle values at 50%, 66.7%, 83.3%, and 100% of the blade.

[0035] Further preferably, to approximate the torsional angle situation generated by the actual elastic deformation of the blade, the torsional angle values at 66.7% and 83.3% of the blade have a range limit, and the range is the area between the straight line and the quadratic curve of the elastic torsional angles at 50% and 100% of the blade, that is Figure 2 the shaded part in

[0036] In step S2, a fitting curve of the blade vibration velocity caused by the turbulent wind condition is established.

[0037] Preferably, the above-mentioned turbulent wind refers to the wind condition with random fluctuations in wind speed within a short period of time. Under the turbulent wind condition, the blade will generate vibration velocity.

[0038] Preferably, the above-mentioned blade vibration velocity fitting curve is obtained by superimposing the vibration velocity v flap in the flapping direction of the blade and the vibration velocity v edge in the pitching direction. The calculation process is as follows: after superimposing the vibration velocities in the flapping and pitching directions of the blade, the resultant velocity is normalized, then multiplied by the specified maximum vibration velocity of the blade, and finally the resultant velocity is decomposed into the component velocities in the flapping and pitching directions.

[0039]

[0040]

[0041] v flap and v edge represent the vibration velocity of the blade in the flapping or pitching direction after superposition, and v flapi and v edgei represent the vibration velocity components in the flapping and pitching directions of the first 8 modes of the blade. The fitting control parameters are ω i and v max . Among them, ω i represents the superposition parameter of the first 8 modes of the blade, and the value range is 0 to 1. v max represents the maximum value of the blade vibration velocity. max(v sum ) represents the maximum value of the resultant velocity after superimposing the vibration velocities in the flapping and pitching directions of the blade.

[0042] In step S3, a load calculation process of the momentum blade element method considering the elastic torsional angle of the blade and the vibration velocity under the turbulent working condition is established.

[0043] Preferably, the calculation process is as follows: for each blade element, the angle of attack is calculated, and by iteratively solving the axial induction factor a and the tangential induction factor a′, the aerodynamic load on each blade element is calculated. After the loads of all blade elements are solved, the root bending moment of the blade is calculated.

[0044] Further preferably, during the above process of calculating the angle of attack, the elastic torsional angle of the blade is considered, and the elastic torsional angle of the blade is used as the correction amount of the current blade element structural torsional angle, and the two are superimposed to obtain the corrected blade torsional angle.

[0045] Further preferably, during the above process of calculating the angle of attack, the blade vibration speed caused by the turbulent wind condition is considered, and the blade vibration speed is used as the correction amount of the current blade element relative wind speed. Figure 3 It is a calculation method of the current blade element angle of attack under different wind speeds, rotational speeds and vibration speeds. Among them, φ represents the angle between the relative resultant velocity of the current blade element and the rotation plane, and can be obtained by φ = arctan(Z1 / Z2) or φ = arctan(Z1 / Z2) ± π under different wind conditions. α represents the angle of attack of the current blade element, and β represents the sum of the pitch angle, structural torsional angle and elastic torsional angle of the current blade element. Z1 and Z2 represent the relative velocities of the blade in the wind speed direction and the rotation direction. Among them, U ∞ represents the incoming flow wind speed, Ω represents the blade rotational speed, r represents the distance from the midpoint of the current blade element to the blade root, a represents the axial induction factor, and a′ represents the tangential induction factor. represents the vibration speed of the current blade element in the wind speed direction and the rotation direction.

[0046] Further preferably, Figure 3 (1) means that the net velocity of the current blade element in the incoming flow direction is greater than the vibration speed in the wind speed direction, and the sum of the rotational speed of the current blade element and the tangential induction velocity is greater than the vibration speed in the rotation direction, that is, when Z1 > 0 and Z2 > 0, the angle between the relative resultant velocity of the current blade element and the rotation plane can be obtained by φ = arctan(Z1 / Z2), and the angle of attack α of the current blade element is α = φ - β. Figure 3 (2) means that the net velocity of the current blade element in the incoming flow direction is less than the vibration speed in the wind speed direction, and the sum of the rotational speed of the current blade element and the tangential induction velocity is greater than the vibration speed in the rotation direction, that is, when Z1 < 0 and Z2 > 0, the angle between the relative resultant velocity of the current blade element and the rotation plane can be obtained by φ = arctan(Z1 / Z2), and the angle of attack α of the current blade element is α = φ - β. Figure 3 (3) means that the net velocity of the current blade element in the incoming flow direction is greater than the vibration speed in the wind speed direction, and the sum of the rotational speed of the current blade element and the tangential induction velocity is less than the vibration speed in the rotation direction, that is, when Z1 > 0 and Z2 < 0, the angle between the relative resultant velocity of the current blade element and the rotation plane can be obtained by φ = arctan(Z1 / Z2) + π, and the angle of attack α of the current blade element is α = φ - β. Figure 3 (4) means that the net velocity of the current blade element in the incoming flow direction is less than the vibration speed in the wind speed direction, and the sum of the rotational speed of the current blade element and the tangential induction velocity is less than the vibration speed in the rotation direction, that is, when Z1 < 0 and Z2 < 0, the angle between the relative resultant velocity of the current blade element and the rotation plane can be obtained by φ = arctan(Z1 / Z2) - π, and the angle of attack α of the current blade element is α = φ - β.

[0047] In step S4, the prediction of the maximum aerodynamic load of the blade is defined as an optimization problem, and the variable value range, constraint conditions, and optimization objectives are set.

[0048] Preferably, the design parameters of the above-mentioned optimization problem of the maximum aerodynamic load of the blade include wind speed, pitch angle, rotational speed, curve fitting parameters of the blade elastic torsion angle, and curve fitting parameters of the blade vibration velocity. Among them, the value ranges of the wind speed, pitch angle, and rotational speed are determined according to the normal power generation condition of the wind turbine, and the value ranges of the curve fitting parameters of the blade elastic torsion angle and the curve fitting parameters of the blade vibration velocity are determined according to different turbulent wind conditions.

[0049] Preferably, the constraint conditions of the above-mentioned optimization problem of the maximum aerodynamic load of the blade include the pitch angle and the rotational speed. The constraints of the pitch angle and the rotational speed are determined by the control law of the wind turbine, and by specifying the upper and lower limits of the pitch angle and the rotational speed at the current wind speed, the state of the wind turbine blade will not deviate too far from the normal operating condition.

[0050] Preferably, the optimization objective of the above-mentioned optimization problem of the maximum aerodynamic load of the blade is the bending moment at the blade root in the Cartesian coordinate system, and the goal is to find the maximum value.

[0051] In step S5, the particle swarm optimization algorithm is used to optimize and obtain the maximum bending moment at the blade root.

[0052] Preferably, the optimization process of the above-mentioned particle swarm optimization algorithm is to initialize the population, calculate the fitness of each particle, find the optimal values of the individual and the group, and update the velocities and positions of each particle until the optimization converges.

[0053] Further preferably, the set parameters of the above-mentioned particle swarm optimization algorithm include the number of particles in each generation, the maximum number of iterations, the convergence criterion, the optimization objective function, and the optimization constraint function.

[0054] Further preferably, in S5, the criterion for the end of the particle swarm optimization algorithm is that the optimization result converges, that is, the difference between the results of continuous 10 rounds of iterative calculations is less than 1e-4, or the set maximum number of iterations is reached. The calculation result of the final convergence of the optimization algorithm is the ultimate aerodynamic load of the large flexible wind turbine blade under turbulent wind conditions.

[0055] Through the above process, the rapid prediction of the ultimate aerodynamic load of the large flexible wind turbine blade under turbulent wind conditions is realized.

[0056] The following further explains the present invention with a specific embodiment.

[0057] For a 110m large flexible blade, the wind speed, pitch angle, rotational speed, curve fitting parameters of the blade elastic torsion angle, and curve fitting parameters of the blade vibration velocity are used as input parameters, and the root bending moment of the blade is used as the output parameter. First, the elastic torsion angle values at 0%, 50% (y1), 66.7% (y m1 ), 83.3% (y m2 ), and 100% (y2) of the blade are used to establish the curve fitting curve of the blade elastic torsion angle. The superposition parameters (ω i , i = 1, 2, 3, 4, 5, 6, 7, 8) of the first 8 modes of the blade and the maximum vibration velocity (v max ) of the blade are used to establish the curve fitting curve of the vibration velocity of the blade in the flapwise and lead-lag directions caused by the turbulent wind condition. Then, a momentum blade element method load calculation process considering the blade elastic torsion angle and the vibration velocity under the turbulent condition is established, which is used as a calculation function, and the particle swarm optimization algorithm is used to solve the maximum value. In this embodiment, the wind speed value range is 3 - 25m / s, the pitch angle value range is 0 - 21.9786°, the rotational speed value range is 4.5 - 8.4rpm, the curve fitting parameter value range of the blade elastic torsion angle is y1: -2.5° - 2.5°, y2: -5° - 5°. Since the values of y m1 , y m2 are restricted to the region between the straight line and the quadratic curve of the elastic torsion angles at 50% and 100% of the blade, which are related to y1 and y2, and the absolute values cannot be determined. Therefore, y m1 , y m2 are determined by two relative position parameters with a value range of 0 - 1. The value range of the curve fitting parameter of the blade vibration velocity is ω i : -1 - 1, v max : 0 - 5m / s. The constraint conditions are determined according to the blade control law. In this embodiment, it is stipulated that the floating range of the pitch angle from the pitch angle under the normal power generation condition is -10° - 4.951°, and the floating range of the rotational speed from the rotational speed under the normal power generation condition is -2 - 2rpm. The parameters set for the particle swarm optimization algorithm are 200 particles per generation, the maximum number of iterations is 500, and the convergence criterion is that the difference between the calculation results of 10 consecutive rounds of iterations is less than 1e -4 . The optimal value finally obtained by the solution is the maximum root load of the blade under the current turbulent wind condition.

[0058] Through the above discussion and examples, the rapid prediction of the ultimate aerodynamic load of large flexible wind turbine blades under turbulent wind conditions is realized.

[0059] According to an exemplary embodiment of the present invention, a computing device is further provided. The computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions are implemented.

[0060] According to an exemplary embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions are implemented. Examples of computer-readable recording media include: read-only memory, random access memory, compact disc read-only memory, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission via the Internet through a wired or wireless transmission path).

[0061] It should be noted that the above is only a part of the embodiments of the present invention. Equivalent changes made to the system described according to the present invention are all included in the protection scope of the present invention. Those skilled in the art of the present invention can make similar alternative ways to the specific examples described as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, and they all belong to the protection scope of the present invention.

Claims

1. A method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions, characterized in that, Comprising: S1: Establish a fitting curve of the elastic twist angle of the blade from the root to the tip, where the elastic twist angle takes into account the sweep design, the bend-twist coupling design, and the structural twist angle factor; S2: Establish a fitting curve of the vibration velocity of the blade from the root to the tip according to the modal superposition method, where the vibration velocity takes into account the blade vibration velocity under turbulent wind conditions; S3: Use the blade elastic twist angle fitting curve formed in S1 and the blade vibration velocity fitting curve formed in S2 as the correction parameters of the twist angle and the relative wind speed for each section of the blade element during the calculation of the load by the momentum blade element method, and establish a load calculation process of the momentum blade element method considering the elastic twist angle of the blade and the vibration velocity under turbulent wind conditions; S4: Adopt the load calculation process of the momentum blade element method considering the elastic twist angle of the blade and the vibration velocity under turbulent wind conditions in S3, use the particle swarm algorithm, take the maximum root moment as the optimization target, and realize the rapid prediction of the ultimate aerodynamic load of large flexible wind turbine blades under turbulent wind conditions through optimization calculation.

2. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, wherein In S1, the control parameters of the fitting curve of the elastic twist angle from the root to the tip are the elastic twist angle values at 0%, 50%, 66.7%, 83.3%, and 100% of the blade spanwise direction.

3. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions as described in claim 1, characterized in that In S2, the control parameters of the fitting curve of the vibration velocity from the root to the tip are the superposition parameters of the first 8 modes of the blade and the maximum vibration velocity of the blade.

4. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, characterized in that In S3, establish a load calculation process of the momentum blade element method considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions, and the input parameters include wind speed, pitch angle, rotational speed, air density, blade structure parameters, blade aerodynamic parameters, blade elastic twist angle fitting parameters, and blade vibration velocity fitting parameters.

5. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, characterized in that, In S3, the load calculation process of the momentum blade element method considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions is specifically to use the elastic twist angle and the vibration velocity as the correction parameters of the current blade element twist angle and the relative wind speed and calculate the angle of attack of the current blade element.

6. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions as described in claim 1, characterized in that, In S3, for the load calculation process of the momentum blade element method considering the elastic twist angle of the blade and the vibration velocity under turbulent conditions, the corrected axial induction factor a and tangential induction factor a' of each section of the blade element are obtained through iterative calculation, so as to calculate the load of each section of the blade element and the root moment.

7. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions as described in claim 1, wherein In S4, the design parameters for predicting the ultimate load of the blade using the particle swarm optimization algorithm are wind speed, pitch angle, rotational speed, blade elastic twist angle curve fitting parameters, and blade vibration velocity fitting parameters.

8. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, characterized in that, In S4, the optimization constraints for predicting the ultimate load of the blade using the particle swarm optimization algorithm are the pitch angle and rotational speed under normal power generation conditions.

9. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, wherein, In S4, the set parameters of the particle swarm optimization algorithm include the number of particles in each generation, the maximum number of iterations, the convergence criterion, the optimization objective function, and the optimization constraint function.

10. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions as described in claim 1, wherein, In S4, the criterion for the particle swarm optimization algorithm to end is that the optimization result converges, specifically that the difference between the results of consecutive 10 rounds of iterative calculations is less than 1e -4 , or the set maximum number of iterations is reached.

11. The method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to claim 1, characterized in that, In S4, the optimization calculation result of the final convergence of the optimization algorithm is the ultimate aerodynamic load of the large flexible wind turbine blade under turbulent wind conditions.

12. A computing device, characterized in that, Including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, it implements the method for predicting the ultimate aerodynamic load of a large flexible wind turbine blade under turbulent wind conditions according to any one of claims 1 to 11.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for predicting the ultimate aerodynamic load under turbulent wind conditions of a large flexible wind turbine blade according to any one of claims 1 to 11.

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