Blade aeroelastic stability optimization method, device, equipment and medium

Abstract

The invention discloses a blade aeroelastic stability optimization method and device, equipment and a medium, and relates to the technical field of blade aeroelastic optimization, and the method comprises the steps: carrying out the frequency domain evaluation of a blade initial model, and if the blade initial model does not meet a preset frequency domain evaluation condition, determining the blade aeroelastic stability; if yes, optimizing the blade shape parameters based on a genetic algorithm to obtain a blade shape optimization model, and determining optimized blade shape parameters based on the model; when the optimized blade appearance parameters meet a first preset parameter condition, blade structure parameters of the blade initial model are optimized to obtain a blade structure optimization model, and optimized blade structure parameters are determined based on the model; when the optimized blade structure parameters meet a second preset parameter condition, whether the time sequence load of the blade structure optimization model is abnormal or not is judged based on a time domain evaluation method; and re-optimizing the blade structure parameters of the blade structure optimization model based on the judgment result. Therefore, the optimization of the aeroelastic stability of the blade is efficiently realized.

Description

Technical Field

[0001] The present invention relates to the technical field of blade aeroelastic optimization, and in particular to a blade aeroelastic stability optimization method, device, equipment and medium. Background Art

[0002] With the increasingly fierce competition in the wind power industry, in order to improve competitiveness and reduce the cost of electricity, large-scale and long and flexible blades have become the development trend of wind turbine blades. However, compared with traditional blades, the aeroelastic stability of long and flexible blades shows obvious nonlinear characteristics, which makes long and flexible blades more prone to aeroelastic instability. Affect the stability of wind turbines and reduce the reliability and fatigue life of blades and wind turbines. At present, the aeroelastic stability analysis of blades is mainly carried out through traditional frequency domain solutions. The evaluation and optimization methods are relatively single, and the reliability can no longer cover the current needs of long and flexible blades. In addition, the traditional blade aeroelastic evaluation method relies more on the personal experience of engineers, and the accuracy and efficiency of the optimization design cannot be guaranteed. The existing blade aeroelastic optimization method mainly relies on manual experience to evaluate blade damping, which is very dependent on the personal experience of engineers and cannot form a reasonable and accurate optimal solution.

[0003] Therefore, how to improve the efficiency of blade aeroelastic optimization and quickly find the optimal solution is an urgent problem to be solved. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for optimizing the aeroelastic stability of blades, which can improve the efficiency of aeroelastic optimization of blades and quickly find the optimal solution. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a method for optimizing blade aeroelastic stability, comprising:

[0006] Performing frequency domain evaluation on the initial blade model to determine whether the initial blade model meets a preset frequency domain evaluation condition;

[0007] If the initial blade model does not meet the preset frequency domain evaluation condition, optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model, and determining the optimized blade shape parameters based on the blade shape optimization model;

[0008] When the optimized blade shape parameters meet the first preset parameter condition, optimizing the blade structure parameters of the initial blade model to obtain a blade structure optimization model, and determining the optimized blade structure parameters based on the blade structure optimization model;

[0009] When the optimized blade structure parameters meet the second preset parameter conditions, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method to obtain a corresponding judgment result;

[0010] The blade structural parameters of the blade structural optimization model are re-optimized based on the judgment result to complete the final optimization of the blade.

[0011] Optionally, if the initial blade model does not meet the preset frequency domain evaluation condition, optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model, and determining the optimized blade shape parameters based on the blade shape optimization model, including:

[0012] If the initial blade model does not meet the preset frequency domain evaluation conditions, the chord length, twist angle, and relative thickness of the initial blade model are simultaneously optimized based on a genetic algorithm to obtain an optimized blade shape model;

[0013] The blade damping is determined based on the blade shape optimization model.

[0014] Optionally, the blade aeroelastic stability optimization method further includes:

[0015] If the initial blade model meets the preset frequency domain evaluation condition, it is directly determined based on the time domain evaluation method whether the time series load of the initial blade model is abnormal to obtain a corresponding judgment result.

[0016] Optionally, after determining the blade damping based on the current blade shape optimization model, the method further includes:

[0017] Determining whether the blade damping is a positive value;

[0018] If the value of the blade damping is not a positive value, the chord length, twist angle, and relative thickness of the blade shape optimization model are synchronously optimized based on the genetic algorithm to obtain a new blade shape optimization model, and then the process jumps to the step of determining the blade damping based on the current blade shape optimization model;

[0019] Correspondingly, when the optimized blade shape parameters meet the first preset parameter condition, optimizing the blade structure parameters of the initial blade model to obtain a blade structure optimization model includes:

[0020] If the value of the blade damping is a positive value, the flapping stiffness, the swing stiffness, and the torsional stiffness of the blade shape optimization model are simultaneously optimized to obtain a blade structure optimization model.

[0021] Optionally, determining optimized blade structure parameters based on the blade structure optimization model includes:

[0022] Determining optimized blade damping based on the blade structure optimization model;

[0023] Correspondingly, when the optimized blade structure parameters meet the second preset parameter conditions, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method includes:

[0024] Determining whether the optimized blade damping value is a positive value;

[0025] If the value of the optimized blade damping is a positive value, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method;

[0026] If the value of the blade damping after optimization is not a positive value, the process jumps back to the step of synchronously optimizing the flapping stiffness, swing stiffness, and torsional stiffness of the blade shape optimization model to obtain the blade structure optimization model.

[0027] Optionally, the synchronous optimization of the flapping stiffness, the swing stiffness, and the torsional stiffness of the blade shape optimization model includes:

[0028] Based on the adjustment of the blade beam ply, the flapping stiffness of the blade shape optimization model is optimized;

[0029] Based on the adjustment of the UD area of ​​the blade trailing edge, the optimization of the swing array stiffness of the blade shape optimization model is completed;

[0030] Based on the adjustment of the blade shell plies and the blade web spacing, the torsional stiffness of the blade shape optimization model is optimized.

[0031] Optionally, after judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method, the method further includes:

[0032] If the sequential load of the blade structure optimization model does not show abnormality, the blade optimization is terminated;

[0033] If the sequential load of the blade structure optimization model is abnormal, the process jumps to the step of optimizing the blade structure parameters of the initial blade model until the sequential load is normal, so as to complete the final optimization of the blade.

[0034] In a second aspect, the present application provides a blade aeroelastic stability optimization device, comprising:

[0035] A frequency domain evaluation module, used to perform frequency domain evaluation on the initial blade model to determine whether the initial blade model meets a preset frequency domain evaluation condition;

[0036] A shape parameter optimization module, configured to optimize the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model if the initial blade model does not meet the preset frequency domain evaluation condition, and determine the optimized blade shape parameters based on the blade shape optimization model;

[0037] a structural parameter determination module, configured to optimize the blade structural parameters of the initial blade model to obtain a blade structural optimization model when the optimized blade shape parameters meet a first preset parameter condition, and determine the optimized blade structural parameters based on the blade structural optimization model;

[0038] A time series load judgment module, used for judging whether the time series load of the blade structure optimization model is abnormal based on a time domain evaluation method when the optimized blade structure parameters meet the second preset parameter conditions, so as to obtain a corresponding judgment result;

[0039] The structural parameter optimization module is used to re-optimize the blade structural parameters of the blade structural optimization model based on the judgment result to complete the final optimization of the blade.

[0040] In a third aspect, the present application provides an electronic device, including:

[0041] Memory, used to store computer programs;

[0042] A processor is used to execute the computer program to implement the aforementioned blade aeroelastic stability optimization method.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned blade aeroelastic stability optimization method is implemented.

[0044] In the present application, the frequency domain evaluation is first performed on the initial blade model to determine whether the initial blade model meets the preset frequency domain evaluation conditions; if the initial blade model does not meet the preset frequency domain evaluation conditions, the blade shape parameters of the initial blade model are optimized based on the genetic algorithm to obtain the blade shape optimization model, and the optimized blade shape parameters are determined based on the blade shape optimization model; when the optimized blade shape parameters meet the first preset parameter conditions, the blade structure parameters of the initial blade model are optimized to obtain the blade structure optimization model, and the optimized blade structure parameters are determined based on the blade structure optimization model; when the optimized blade structure parameters meet the second preset parameter conditions, the time domain evaluation method is used to determine whether the timing load of the blade structure optimization model is abnormal to obtain the corresponding judgment result; based on the judgment result, the blade structure parameters of the blade structure optimization model are re-optimized to complete the final optimization of the blade. As can be seen from the above, the present application greatly improves the reliability of blade aeroelastic calculation through the dual compounding of frequency domain and time domain; at the same time, it is combined with the intelligent genetic algorithm to effectively improve the efficiency of blade aeroelastic optimization, and the optimal solution can be quickly found. In this way, the aeroelastic stability of the blade is optimized efficiently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0046] Figure 1 A flow chart of a blade aeroelastic stability optimization method disclosed in this application;

[0047] Figure 2 This is a schematic diagram of blade parameter changes disclosed in this application; wherein, Figure 2 (a) is a schematic diagram of frequency change. Figure 2 (b) is a schematic diagram of damping change;

[0048] Figure 3 This is a schematic diagram of a blade change disclosed in this application; wherein, Figure 3 (a) is a schematic diagram of the change of swing direction. Figure 3 (b) is a schematic diagram of the torsion angle change. Figure 3 (c) is a schematic diagram of the rotation speed change. Figure 3 (d) is a schematic diagram of relative wind speed changes;

[0049] Figure 4 A system flow chart of a blade aeroelastic stability optimization method disclosed in this application;

[0050] Figure 5 This is a schematic diagram of the structure of a blade aeroelastic stability optimization device disclosed in this application;

[0051] Figure 6 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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 creative work are within the scope of protection of the present invention.

[0053] With the increasingly fierce competition in the wind power industry, in order to improve competitiveness and reduce the cost of electricity, large-scale and long and flexible blades have become the development trend of wind turbine blades. However, compared with traditional blades, the aeroelastic stability of long and flexible blades shows obvious nonlinear characteristics, which makes long and flexible blades more prone to aeroelastic instability. Affect the stability of wind turbines and reduce the reliability and fatigue life of blades and wind turbines. At present, the aeroelastic stability analysis of blades is mainly carried out through traditional frequency domain solutions. The evaluation and optimization methods are relatively single, and the reliability can no longer cover the current needs of long and flexible blades. In addition, the traditional blade aeroelastic evaluation method relies more on the personal experience of engineers, and the accuracy and efficiency of the optimization design cannot be guaranteed. The existing blade aeroelastic optimization method mainly relies on manual experience to evaluate the blade damping, which is very dependent on the personal experience of engineers and cannot form a reasonable and accurate optimal solution. Therefore, this application will specifically introduce a blade aeroelastic stability optimization method that can solve the above problems.

[0054] See also Figure 1 As shown, an embodiment of the present invention discloses a method for optimizing blade aeroelastic stability, which may include:

[0055] Step S11 : performing frequency domain evaluation on the initial blade model to determine whether the initial blade model meets a preset frequency domain evaluation condition.

[0056] In this embodiment, the frequency domain evaluation is performed on the initial blade model based on the frequency domain evaluation method to determine whether the initial blade model meets the preset frequency domain evaluation conditions. The frequency domain evaluation conditions may include the vibration characteristics, natural frequency distribution, resonance avoidance, and dynamic response of the blade under various working conditions.

[0057] Step S12: If the initial blade model does not meet the preset frequency domain evaluation conditions, the blade shape parameters of the initial blade model are optimized based on a genetic algorithm to obtain a blade shape optimization model, and the optimized blade shape parameters are determined based on the blade shape optimization model.

[0058] In this embodiment, if the initial blade model does not meet the preset frequency domain evaluation conditions, the chord length, torsion angle, and relative thickness of the initial blade model are synchronously optimized based on a genetic algorithm to obtain a blade shape optimization model; and the blade damping is determined based on the current blade shape optimization model. It should be noted that when the chord length, torsion angle, and relative thickness of the initial blade model are synchronously optimized, the blade shape parameters need to be optimized within a certain amplitude range, and the minimum solution is found for the chord length and relative thickness of the initial blade model within the amplitude range, and the solution that ensures the optimal aerodynamic performance is found for the torsion angle of the initial blade model. The frequency domain reference equation of the optimization algorithm in this embodiment is as follows:

[0059]

[0060]

[0061]

[0062] From the above dynamic equilibrium equation, the eigenvalue solution matrix is ​​obtained:

[0063]

[0064] Among them, M represents the mass matrix, C represents the damping matrix, G represents the gyro matrix, and K represents the stiffness matrix. The modal matrix, Indicates frequency, represents a feature vector.

[0065] According to this matrix, we can find the eigenvalue and the eigenvector , and then obtain the blade's aeroelastic damping, frequency, mode and other parameters. Based on the above frequency domain reference equation, the frequency and damping of the fan blade at different wind speeds are obtained, and then according to the optimization objectives such as Figure 2 As shown, the aerodynamic and structural parameters of the blade are optimized, and the blade properties that ultimately meet the preset conditions are obtained.

[0066] In this embodiment, if the initial model of the blade meets the preset frequency domain evaluation condition, then the time domain evaluation method is directly used to determine whether the time series load of the initial model of the blade is abnormal to obtain a corresponding judgment result. Specifically, if the chord length and relative thickness of the initial model of the blade are the smallest solutions within the preset parameter optimization amplitude range, and the torsion angle is the optimal solution within the preset parameter optimization amplitude range, then it means that the initial model of the blade meets the preset frequency domain evaluation condition. At this time, the time domain evaluation method is directly used to determine whether the time series load of the initial model of the blade is abnormal to obtain a corresponding judgment result.

[0067] In this embodiment, after determining the blade damping based on the current blade shape optimization model, it also includes: judging whether the blade damping is a positive value; if the value of the blade damping is not a positive value, then based on the genetic algorithm, the chord length, torsion angle, and relative thickness of the blade shape optimization model are synchronously optimized to obtain a new blade shape optimization model, and then jump to the step of determining the blade damping based on the current blade shape optimization model. In other words, if the value of the blade damping is not a positive value, then based on the genetic algorithm, the parameters of the blade shape optimization model are continuously synchronously optimized to obtain a new blade shape optimization model, and the blade damping of the new blade shape optimization model is re-determined until the value of the blade damping is a positive value.

[0068] Step S13: When the optimized blade shape parameters meet the first preset parameter conditions, the blade structure parameters of the initial blade model are optimized to obtain a blade structure optimization model, and the optimized blade structure parameters are determined based on the blade structure optimization model.

[0069] In this embodiment, if the value of the blade damping is positive, the flapping stiffness, swing array stiffness, and torsional stiffness of the blade shape optimization model are synchronously optimized to obtain a blade structure optimization model. The optimized blade damping is determined based on the blade structure optimization model. It should be noted that when the flapping stiffness, swing array stiffness, and torsional stiffness of the blade shape optimization model are synchronously optimized, it is necessary to ensure that the layer solution with the smallest blade root load and the layer solution with the power generation closest to the target value are obtained, and the number of layers and positioning should be within the program setting range.

[0070] In this embodiment, it is determined whether the value of the optimized blade damping is a positive value; if the value of the optimized blade damping is not a positive value, the step of synchronously optimizing the flapping stiffness, the swing array stiffness, and the torsional stiffness of the blade shape optimization model to obtain the blade structure optimization model is jumped again. In other words, if the value of the optimized blade damping is not a positive value, the flapping stiffness, the swing array stiffness, and the torsional stiffness of the blade shape optimization model are synchronously optimized again to obtain a new blade structure optimization model, and the optimized blade damping of the new blade structure optimization model is re-determined until the value of the optimized blade damping is a positive value.

[0071] In this embodiment, the flapping stiffness, swing stiffness and torsional stiffness of the structural part are adjusted by the blade ply respectively. That is to say, based on the adjustment of the blade beam ply, the optimization of the flapping stiffness of the blade shape optimization model is completed; based on the adjustment of the blade trailing edge UD (Unidirectional fabric, i.e., trailing edge unidirectional fabric) area, the optimization of the swing array stiffness of the blade shape optimization model is completed; based on the adjustment of the blade shell ply and the blade web spacing, the optimization of the torsional stiffness of the blade shape optimization model is completed.

[0072] Step S14: when the optimized blade structure parameters meet the second preset parameter conditions, it is determined based on a time domain evaluation method whether the time series load of the blade structure optimization model is abnormal, so as to obtain a corresponding determination result.

[0073] In this embodiment, if the value of the optimized blade damping is positive, then the time domain evaluation method is used to determine whether the time series load of the blade structure optimization model is abnormal. It should be noted that the dynamic response of the blade and the unit under time is confirmed based on the time domain evaluation method, such as Figure 3 As shown, the blade frequency and deformation over time are described under different wind speeds.

[0074] Step S15: re-optimize the blade structural parameters of the blade structural optimization model based on the judgment result to complete the final optimization of the blade.

[0075] In the present embodiment, if there is no abnormality in the timing load of the blade structure optimization model, the blade optimization is terminated; if there is an abnormality in the timing load of the blade structure optimization model, the step of optimizing the blade structure parameters of the initial blade model is jumped to until the timing load is normal, so as to complete the final optimization of the blade. In other words, if there is no abnormality in the timing load of the blade structure optimization model, it means that the optimization of the blade has achieved the preset effect, and the optimization of the blade is terminated at this time; if there is an abnormality in the timing load of the blade structure optimization model, the structural parameters of the blade are re-optimized until the timing load is normal, and the optimization of the blade is terminated. In summary, the system flow chart of the present application is as follows: Figure 4 shown.

[0076] In this embodiment, the frequency domain evaluation is first performed on the initial model of the blade to determine whether the initial model of the blade meets the preset frequency domain evaluation conditions; if the initial model of the blade does not meet the preset frequency domain evaluation conditions, the blade shape parameters of the initial model of the blade are optimized based on the genetic algorithm to obtain the blade shape optimization model, and the optimized blade shape parameters are determined based on the blade shape optimization model; when the optimized blade shape parameters meet the first preset parameter conditions, the blade structure parameters of the initial model of the blade are optimized to obtain the blade structure optimization model, and the optimized blade structure parameters are determined based on the blade structure optimization model; when the optimized blade structure parameters meet the second preset parameter conditions, the time domain evaluation method is used to determine whether the time series load of the blade structure optimization model is abnormal to obtain the corresponding judgment result; based on the judgment result, the blade structure parameters of the blade structure optimization model are re-optimized to complete the final optimization of the blade. As can be seen from the above, this embodiment greatly improves the reliability of the blade aeroelastic calculation through the dual compound of the frequency domain and the time domain; at the same time, it is combined with the intelligent genetic algorithm to effectively improve the efficiency of the blade aeroelastic optimization, and the optimal solution can be quickly found. In this way, the aeroelastic stability of the blade is optimized efficiently and accurately.

[0077] Accordingly, see Figure 5 As shown, the embodiment of the present application also provides a blade aeroelastic stability optimization device, which may include:

[0078] The frequency domain evaluation module 11 is used to perform frequency domain evaluation on the initial blade model to determine whether the initial blade model meets the preset frequency domain evaluation conditions;

[0079] A shape parameter optimization module 12 is used for optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model if the initial blade model does not meet the preset frequency domain evaluation condition, and determining the optimized blade shape parameters based on the blade shape optimization model;

[0080] A structural parameter determination module 13 is used to optimize the blade structural parameters of the initial blade model to obtain a blade structural optimization model when the optimized blade shape parameters meet the first preset parameter condition, and determine the optimized blade structural parameters based on the blade structural optimization model;

[0081] A time series load judgment module 14 is used to judge whether the time series load of the blade structure optimization model is abnormal based on a time domain evaluation method when the optimized blade structure parameters meet the second preset parameter conditions, so as to obtain a corresponding judgment result;

[0082] The structural parameter optimization module 15 is used to re-optimize the blade structural parameters of the blade structural optimization model based on the judgment result to complete the final optimization of the blade.

[0083] As can be seen from the above, the present application first performs frequency domain evaluation on the initial model of the blade to determine whether the initial model of the blade meets the preset frequency domain evaluation conditions; if the initial model of the blade does not meet the preset frequency domain evaluation conditions, the blade shape parameters of the initial model of the blade are optimized based on the genetic algorithm to obtain the blade shape optimization model, and the optimized blade shape parameters are determined based on the blade shape optimization model; when the optimized blade shape parameters meet the first preset parameter conditions, the blade structure parameters of the initial model of the blade are optimized to obtain the blade structure optimization model, and the optimized blade structure parameters are determined based on the blade structure optimization model; when the optimized blade structure parameters meet the second preset parameter conditions, the time domain evaluation method is used to determine whether the timing load of the blade structure optimization model is abnormal to obtain the corresponding judgment result; based on the judgment result, the blade structure parameters of the blade structure optimization model are re-optimized to complete the final optimization of the blade. As can be seen from the above, the present application greatly improves the reliability of blade aeroelastic calculation through the dual compounding of frequency domain and time domain; at the same time, it combines the intelligent genetic algorithm to effectively improve the efficiency of blade aeroelastic optimization, and can quickly find the optimal solution. In this way, the aeroelastic stability of the blade is optimized efficiently and accurately.

[0084] In some specific implementations, the shape parameter optimization module 12 may include:

[0085] A model acquisition unit, configured to synchronously optimize the chord length, twist angle, and relative thickness of the initial blade model based on a genetic algorithm to obtain an optimized blade shape model if the initial blade model does not meet a preset frequency domain evaluation condition;

[0086] The damping determination submodule is used to determine the blade damping based on the current blade shape optimization model.

[0087] In some specific embodiments, the blade aeroelastic stability optimization device may further include:

[0088] The abnormality judgment unit is used to directly judge whether the time series load of the initial blade model is abnormal based on the time domain evaluation method to obtain a corresponding judgment result if the initial blade model meets the preset frequency domain evaluation condition.

[0089] In some specific implementations, the damping determination submodule may further include:

[0090] A damping judgment unit, used for judging whether the blade damping is a positive value;

[0091] A model optimization unit, for synchronously optimizing the chord length, twist angle, and relative thickness of the blade shape optimization model based on the genetic algorithm if the value of the blade damping is not a positive value, so as to obtain a new blade shape optimization model, and then jumping to the step of determining the blade damping based on the current blade shape optimization model;

[0092] Accordingly, the structural parameter determination module 13 includes:

[0093] The model optimization unit is used to simultaneously optimize the flapping stiffness, swing array stiffness and torsional stiffness of the blade shape optimization model to obtain a blade structure optimization model if the value of the blade damping is a positive value.

[0094] In some specific implementations, the structural parameter determination module 13 may include:

[0095] A damping determination unit, configured to determine optimized blade damping based on the blade structure optimization model;

[0096] Accordingly, the time series load determination module 14 includes:

[0097] A damping judgment unit, used to judge whether the value of the optimized blade damping is a positive value;

[0098] A time series load judgment unit, configured to judge whether the time series load of the blade structure optimization model is abnormal based on a time domain evaluation method if the value of the optimized blade damping is a positive value;

[0099] The model optimization submodule is used to jump back to the step of synchronously optimizing the flapping stiffness, swing array stiffness and torsional stiffness of the blade shape optimization model to obtain the blade structure optimization model if the value of the blade damping after optimization is not a positive value.

[0100] In some specific implementations, the model optimization submodule may include:

[0101] A flapping stiffness optimization unit, used for optimizing the flapping stiffness of the blade shape optimization model based on the adjustment of the blade beam ply;

[0102] A swing array stiffness optimization unit, used to optimize the swing array stiffness of the blade shape optimization model based on the adjustment of the blade trailing edge UD area;

[0103] The torsional stiffness optimization unit is used to optimize the torsional stiffness of the blade shape optimization model based on the adjustment of the blade shell plies and the blade web spacing.

[0104] In some specific implementations, the time series load determination module 14 may further include:

[0105] An optimization completion unit, used for ending blade optimization if the sequential load of the blade structure optimization model does not show abnormality;

[0106] The parameter optimization unit is used to jump to the step of optimizing the blade structure parameters of the initial blade model if the sequential load of the blade structure optimization model is abnormal until the sequential load is normal, so as to complete the final optimization of the blade.

[0107] Furthermore, the present application also discloses an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the blade aeroelastic stability optimization method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0108] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0109] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0110] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the blade aeroelastic stability optimization method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include a computer program that can be used to complete other specific tasks.

[0111] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the blade aeroelastic stability optimization method disclosed above is implemented. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0112] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0113] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0115] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0116] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for optimizing blade aeroelastic stability, characterized in that: include: Performing frequency domain evaluation on the initial blade model to determine whether the initial blade model meets a preset frequency domain evaluation condition; If the initial blade model does not meet the preset frequency domain evaluation condition, optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model, and determining the optimized blade shape parameters based on the blade shape optimization model; When the optimized blade shape parameters meet the first preset parameter condition, optimizing the blade structure parameters of the initial blade model to obtain a blade structure optimization model, and determining the optimized blade structure parameters based on the blade structure optimization model; When the optimized blade structure parameters meet the second preset parameter conditions, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method to obtain a corresponding judgment result; The blade structural parameters of the blade structural optimization model are re-optimized based on the judgment result to complete the final optimization of the blade.

2. The blade aeroelastic stability optimization method according to claim 1, characterized in that: If the initial blade model does not meet the preset frequency domain evaluation condition, optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model, and determining the optimized blade shape parameters based on the blade shape optimization model, including: If the initial blade model does not meet the preset frequency domain evaluation conditions, the chord length, twist angle, and relative thickness of the initial blade model are simultaneously optimized based on a genetic algorithm to obtain an optimized blade shape model; The blade damping is determined based on the blade shape optimization model.

3. The blade aeroelastic stability optimization method according to claim 1, characterized in that: Also includes: If the initial blade model meets the preset frequency domain evaluation condition, it is directly determined based on the time domain evaluation method whether the time series load of the initial blade model is abnormal to obtain a corresponding judgment result.

4. The blade aeroelastic stability optimization method according to claim 2, characterized in that: After determining the blade damping based on the current blade shape optimization model, the method further includes: Determining whether the blade damping is a positive value; If the value of the blade damping is not a positive value, the chord length, twist angle, and relative thickness of the blade shape optimization model are synchronously optimized based on the genetic algorithm to obtain a new blade shape optimization model, and then the process jumps to the step of determining the blade damping based on the current blade shape optimization model; Correspondingly, when the optimized blade shape parameters meet the first preset parameter condition, optimizing the blade structure parameters of the initial blade model to obtain a blade structure optimization model includes: If the value of the blade damping is a positive value, the flapping stiffness, the swing stiffness, and the torsional stiffness of the blade shape optimization model are simultaneously optimized to obtain a blade structure optimization model.

5. The blade aeroelastic stability optimization method according to claim 4, characterized in that: The step of determining optimized blade structure parameters based on the blade structure optimization model includes: Determining optimized blade damping based on the blade structure optimization model; Correspondingly, when the optimized blade structure parameters meet the second preset parameter conditions, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method includes: Determining whether the optimized blade damping value is a positive value; If the value of the optimized blade damping is a positive value, judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method; If the value of the blade damping after optimization is not a positive value, the process jumps back to the step of synchronously optimizing the flapping stiffness, swing stiffness, and torsional stiffness of the blade shape optimization model to obtain the blade structure optimization model.

6. The blade aeroelastic stability optimization method according to claim 5, characterized in that: The synchronous optimization of the flapping stiffness, the swing stiffness, and the torsional stiffness of the blade shape optimization model includes: Based on the adjustment of the blade beam ply, the flapping stiffness of the blade shape optimization model is optimized; Based on the adjustment of the UD area of ​​the blade trailing edge, the optimization of the swing array stiffness of the blade shape optimization model is completed; Based on the adjustment of the blade shell plies and the blade web spacing, the torsional stiffness of the blade shape optimization model is optimized.

7. The blade aeroelastic stability optimization method according to any one of claims 1 to 6, characterized in that: After judging whether the time series load of the blade structure optimization model is abnormal based on the time domain evaluation method, the method further includes: If the sequential load of the blade structure optimization model does not show abnormality, the blade optimization is terminated; If the sequential load of the blade structure optimization model is abnormal, the process jumps to the step of optimizing the blade structure parameters of the initial blade model until the sequential load is normal, so as to complete the final optimization of the blade.

8. A blade aeroelastic stability optimization device, characterized in that: include: A frequency domain evaluation module, used to perform frequency domain evaluation on the initial blade model to determine whether the initial blade model meets a preset frequency domain evaluation condition; A shape parameter optimization module, for optimizing the blade shape parameters of the initial blade model based on a genetic algorithm to obtain a blade shape optimization model if the initial blade model does not meet the preset frequency domain evaluation condition, and determining the optimized blade shape parameters based on the blade shape optimization model; a structural parameter determination module, configured to optimize the blade structural parameters of the initial blade model to obtain a blade structural optimization model when the optimized blade shape parameters meet a first preset parameter condition, and determine the optimized blade structural parameters based on the blade structural optimization model; A time series load judgment module, used for judging whether the time series load of the blade structure optimization model is abnormal based on a time domain evaluation method when the optimized blade structure parameters meet the second preset parameter conditions, so as to obtain a corresponding judgment result; The structural parameter optimization module is used to re-optimize the blade structural parameters of the blade structural optimization model based on the judgment result to complete the final optimization of the blade.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the blade aeroelastic stability optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the blade aeroelastic stability optimization method according to any one of claims 1 to 7 is implemented.

Images