A wind turbine blade fatigue life prediction method, system, device and medium

Abstract

The application discloses a wind turbine blade fatigue life prediction method, system, device and medium, and relates to the field of wind turbine blade fatigue life prediction. The method comprises the following steps: constructing a wind turbine blade life prediction model; establishing a numerical calculation model of a wind turbine blade to be predicted for fatigue life; determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade to be predicted for fatigue life; preprocessing the single-channel time-stress load spectrum, utilizing double statistical parameters, and establishing a two-dimensional joint probability density function model; determining parameters in the wind turbine blade life prediction model through a fatigue test under a multi-stress ratio of a composite material according to the two-dimensional joint probability density function model; and inputting the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade to be predicted for fatigue life. The application can improve the life prediction precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind turbine blade fatigue life prediction, and particularly to a wind turbine blade fatigue life prediction method, system, device and medium. BACKGROUND

[0002] At present, most of the fatigue damage models of wind turbine blades need to be researched from the fatigue damage mechanism of materials. Since the damage mechanism of composite materials has not formed a relatively perfect system in the field of fatigue damage, most scholars study the damage evolution behavior of composite materials from the perspective of phenomenological theory. The commonly used phenomenological theory models include the following:

[0003] 1. Residual strength model: the establishment of the residual strength model needs to be based on the following assumptions: (1) In the early stage of fatigue life, the residual strength decreases rapidly due to the appearance of fiber micro-cracks in the matrix; (2) In the middle stage of fatigue life, random fiber fracture and delamination occur, and the residual strength decreases slowly and stably; (3) In the later stage of fatigue damage, when the residual strength decreases to the peak value of the next random load, sudden fracture occurs, which is called "sudden death". However, there is no way to do non-destructive testing, and the application in actual operation is relatively difficult.

[0004] 2. Residual stiffness model: since the residual stiffness model is non-destructive during detection, it has relative advantages in describing the damage change of composite laminated plates. The following assumptions are needed to establish the model: (1) In the initial stage of life, the initial damage of the early material and the formation of matrix cracks will cause the residual stiffness to decrease rapidly; (2) When the matrix cracks reach a certain density, the characteristic damage state is considered to appear, and the matrix cracks will no longer increase, and the residual stiffness will basically show a linear downward trend; (3) In the later stage of damage, the rapid decrease of residual stiffness corresponds to the rapid accumulation and concentrated evolution of damage, and it is generally considered that the residual stiffness reaches 85% to 90% of the initial stiffness, which is considered as fatigue failure. Since this process has human factors, there is no systematic theoretical derivation and a large number of material performance tests are needed.

[0005] 3. Residual life model: the residual life model does not consider the changes of stiffness and strength in the damage accumulation process. The material damage is stimulated by the load on the component, and the change law of the material with the input load cycle number is studied. In general engineering, the S-N curve of the material is often used to describe the functional relationship between load and life, the fatigue damage of each stress cycle is calculated through the stress-life relationship, and then the fatigue life is calculated or whether it is failed is judged by using the damage accumulation rule.

[0006] The above phenomenological model, the residual strength theory and the residual stiffness theory need a large number of material performance tests, the residual life model does not need to consider the complex fiber layer fracture and interface debonding process in the composite material, but obtains the physical law by summarizing the facts. Therefore, the key of the residual life model is the selection of the S-N curve type, the statistical processing of the fatigue data, the selection of the appropriate equivalent life fatigue limit model, the fatigue failure criterion and the damage summation rule. Among them, the equivalent life fatigue limit model determines the main error of life estimation. The early Goodman model is only applicable to metal materials. Since the metal material has high compression performance, that is, the compression performance of the metal material increases with compression, in the equivalent life fatigue limit diagram of the metal material, when the average stress is less than zero, it shows an upward trend. For composite materials, they generally do not have similar compression performance as metal materials. When the compression performance of the composite material is ignored, the predicted fatigue life will have a large error. In actual engineering applications, damage prediction of components or systems is crucial. Therefore, an equivalent life fatigue limit model considering the performance of the composite material needs to be established to improve the life prediction accuracy and provide guidance for the maintenance of the structure in practice. SUMMARY

[0007] The purpose of the present application is to provide a wind turbine blade fatigue life prediction method, system, device and medium, which can improve the life prediction accuracy.

[0008] To achieve the above purpose, the present application provides the following scheme:

[0009] A wind turbine blade fatigue life prediction method, the method comprising:

[0010] Constructing a wind turbine blade life prediction model;

[0011] Establishing a numerical calculation model of a wind turbine blade whose fatigue life is to be predicted;

[0012] Determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted;

[0013] Pretreating the single-channel time-stress load spectrum, using double statistical parameters to establish a two-dimensional joint probability density function model;

[0014] Determining parameters in the wind turbine blade life prediction model according to the two-dimensional joint probability density function model through fatigue tests of the composite material under multiple stress ratios;

[0015] inputting the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade with the fatigue life to be predicted.

[0016] Optionally, the wind turbine blade life prediction model is:

[0017]

[0018] n = n1 + n2;

[0019]

[0020]

[0021] wherein N * is the fatigue life when the random load acts n times, is the damage of the random stress S (S a , S m ) to the blade in the material tensile stage n1 times, is the damage of the random stress S (S a , S m ) to the blade in the material compression stage n2 times, n1 is the cycle number of the random stress acting in the tensile area, n2 is the cycle number of the random stress acting in the compression area, S a is the cycle stress amplitude, S m is the average stress of the cycle stress, A is a whole rotation conversion coefficient of the blade, f (S a , S m ) is a joint probability density function of the average stress and the stress amplitude conforming to a normal distribution, C t is a constant parameter in the tensile stage, C c is a constant parameter in the compression stage, S t is the ultimate tensile strength of the material, S c is the ultimate compressive strength of the material, D (S a , S m ) is a damage function, S i,-1 is the i-th level of symmetric cyclic load, m, and C is a material performance parameter.

[0022] Optionally, the numerical calculation model of the wind turbine blade with the fatigue life to be predicted is established, and specifically includes:

[0023] establishing a three-dimensional model of the wind turbine blade with the fatigue life to be predicted;

[0024] performing finite element analysis on the three-dimensional model to establish the numerical calculation model of the wind turbine blade with the fatigue life to be predicted.

[0025] Optionally, the determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted specifically comprises:

[0026] The stress distribution of the critical dangerous position of the wind turbine blade whose fatigue life is to be predicted is calculated according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted, so as to obtain a single-channel time-stress load spectrum; the critical dangerous position includes a root of the wind turbine blade and a one-third position of the wind turbine blade.

[0027] Optionally, the preprocessing the single-channel time-stress load spectrum, establishing a two-dimensional joint probability density function model by using double statistical parameters specifically comprises:

[0028] The single-channel time-stress load spectrum is preprocessed to determine a random asymmetric cyclic load.

[0029] The distribution of the average stress and the stress amplitude in the random asymmetric cyclic load is analyzed by using double statistical parameters, and a two-dimensional joint probability density function model is established.

[0030] Optionally, the preprocessing the single-channel time-stress load spectrum to determine a random asymmetric cyclic load specifically comprises:

[0031] Invalid stress distribution points in the single-channel time-stress load spectrum are removed to obtain an effective stress random load.

[0032] The effective stress random load is converted into a random asymmetric cyclic load by using a rain flow counting method.

[0033] A wind turbine blade fatigue life prediction system is applied to the wind turbine blade fatigue life prediction method, and the system comprises:

[0034] A construction module is configured to construct a wind turbine blade life prediction model.

[0035] An establishment module is configured to establish a numerical calculation model of a wind turbine blade whose fatigue life is to be predicted.

[0036] A load spectrum determination module is configured to determine a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted.

[0037] A preprocessing module is configured to preprocess the single-channel time-stress load spectrum, and establish a two-dimensional joint probability density function model by using double statistical parameters.

[0038] A parameter determination module is configured to determine parameters in the wind turbine blade life prediction model by fatigue tests of a composite material under a multi-stress ratio according to the two-dimensional joint probability density function model.

[0039] a result prediction module configured to input the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade with the fatigue life to be predicted.

[0040] Optionally, the establishing module comprises:

[0041] a three-dimensional model establishing submodule configured to establish a three-dimensional model of the wind turbine blade with the fatigue life to be predicted;

[0042] a calculation model establishing submodule configured to perform finite element analysis on the three-dimensional model to establish a numerical calculation model of the wind turbine blade with the fatigue life to be predicted.

[0043] An electronic device comprises a memory configured to store a computer program and a processor configured to execute the computer program to enable the electronic device to perform the wind turbine blade fatigue life prediction method.

[0044] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the wind turbine blade fatigue life prediction method.

[0045] According to the specific embodiments of the present application, the following technical effects are achieved:

[0046] The wind turbine blade fatigue life prediction method comprises the following steps: constructing a wind turbine blade life prediction model; establishing a numerical calculation model of the wind turbine blade with the fatigue life to be predicted; determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade with the fatigue life to be predicted; preprocessing the single-channel time-stress load spectrum, using double statistical parameters to establish a two-dimensional joint probability density function model; determining parameters in the wind turbine blade life prediction model according to the two-dimensional joint probability density function model through a fatigue test under a multi-stress ratio of a composite material; inputting the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade with the fatigue life to be predicted. The present application uses a segmented function to separately describe the difference between compression and tensile properties, combines an isofatigue fatigue limit diagram described by early, middle and late fatigue damage accumulation rules, and combines a linear fatigue damage accumulation rule and a two-dimensional joint probability distribution to derive a fatigue life prediction mathematical model. Parameters in the isofatigue fatigue limit model are determined according to fatigue test data under a multi-stress ratio of a composite material, so that the fatigue life prediction precision of the fatigue performance of a composite material is improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0048] Figure 1 A flow chart of the wind turbine blade fatigue life prediction method provided by the present application is provided.

[0049] Figure 2 A flow chart of the fatigue life prediction of one specific embodiment provided by the present application is provided.

[0050] Figure 3 A schematic diagram of the single-channel time-stress typical load spectrum output by the wind turbine blade provided by the present application is provided. Figure 3 (a) in the figure is a schematic diagram of the equal-life fatigue limit curve, Figure 3 (b) in the figure is a schematic diagram of the time-stress level curve collected;

[0051] Figure 4 A schematic diagram of the damage accumulation curve of the wind turbine blade provided by the present application is provided.

[0052] Figure 5 A module diagram of the wind turbine blade fatigue life prediction system provided by the present application is provided.

[0053] Symbol explanation:

[0054] 1-Construction module, 2-Construction module, 3-Load spectrum determination module, 4-Preprocessing module, 5-Parameter determination module, 6-Result prediction module. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] The purpose of the present application is to provide a wind turbine blade fatigue life prediction method, system, device and medium, which can improve the life prediction accuracy.

[0057] The application discloses a wind turbine blade fatigue life prediction method based on an equal life model.The application establishes a double exponential parameter equal life fatigue limit model in accordance with the material characteristics of the wind turbine blade, and establishes a double parameter damage life model based on the model.

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0059] Embodiment one

[0060] As shown in the formula (1), the application provides a wind turbine blade fatigue life prediction method, which comprises the following steps: Figure 1 Step S1: constructing a wind turbine blade life prediction model.

[0061]

[0062]

[0063] n=n1+n2 (2);

[0064]

[0065]

[0066] N *Fatigue life of the blade under n times of random load, Damage of the blade under n1 times of random stress S a , m in the tensile stage of the material, Damage of the blade under n2 times of random stress S a , m in the compression stage of the material, n1 is the number of cycles of the random stress acting in the tensile region, n2 is the number of cycles of the random stress acting in the compression region, S a is the cyclic stress amplitude, S m is the mean stress of the cyclic stress, A is the conversion coefficient of the blade, f(S a , m ) is the joint probability density function of the mean stress and the stress amplitude conforming to the normal distribution, in general, the probability density functions of the stress amplitude and the mean stress conform to the normal distribution, the lognormal distribution or the Weibull distribution, C t is the constant parameter in the tensile stage, C c is the constant parameter in the compression stage, S t is the ultimate tensile strength of the material, S c is the ultimate compressive strength of the material, D(S a , m ) is the damage function, S i,-1 is the i-th symmetrical cyclic load, m, C is the material performance parameter. Formula (1) is solved according to formula (2), formula (3) and formula (4).

[0067] Step S2: establishing a numerical calculation model of the wind turbine blade whose fatigue life is to be predicted.

[0068] S2 specifically includes:

[0069] Step S21: establishing a three-dimensional model of the wind turbine blade whose fatigue life is to be predicted.

[0070] Step S22: performing finite element analysis on the three-dimensional model to establish a numerical calculation model of the wind turbine blade whose fatigue life is to be predicted.

[0071] Step S3: determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted. Specifically, the stress distribution of the critical dangerous position of the wind turbine blade whose fatigue life is to be predicted is calculated according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted, so as to obtain a single-channel time-stress load spectrum; the critical dangerous position includes the root of the wind turbine blade and the one-third position of the wind turbine blade.

[0072] Step S4: preprocessing the single-channel time-stress load spectrum, using double statistical parameters to establish a two-dimensional joint probability density function model.

[0073] S4 specifically includes:

[0074] Step S41: preprocessing the single-channel time-stress load spectrum to determine random asymmetric cyclic load.

[0075] S41 specifically includes:

[0076] Step S411: removing invalid stress distribution points in the single-channel time-stress load spectrum to obtain effective stress random load.

[0077] Step S412: applying rainflow counting method to convert the effective stress random load into random asymmetric cyclic load.

[0078] Step S42: using double statistical parameter analysis to analyze the distribution of mean stress and stress amplitude in the random asymmetric cyclic load, and establishing a two-dimensional joint probability density function model.

[0079] Step S5: according to the two-dimensional joint probability density function model, determining the parameters in the wind turbine blade life prediction model through fatigue test under multiple stress ratios of composite materials. The parameters include the number of cycles of random stress acting on the compression region, the number of cycles of random stress acting on the tension region, the constant parameters of the tension stage, the constant parameters of the compression stage, and the i-th level of symmetric cyclic load. Specifically, through fatigue test, the directly obtained parameters include the number of cycles n1 of random stress acting on the compression region, the number of cycles n2 of random stress acting on the tension region, the constant parameters C t and C c , the exponential parameters θ 1，t , θ 2，t , θ 1，c , θ 2，c , the two-dimensional probability density function mean μ a , μ m , σ a , σ m ; according to the constant parameters C t and C c , the exponential parameters θ 1，t , θ 2，t , θ 1，c , θ 2，c , the following can be obtained

[0080] Step S6: inputting the parameters into the wind turbine blade life prediction model to obtain the fatigue life prediction result of the wind turbine blade to be predicted.

[0081] The wind turbine blade life prediction model provided by the application is constructed as follows:

[0082] The damage evolution mechanism of composite materials under alternating load is very complex, and the relationship between load and life is generally considered by using phenomenological model in engineering, and the general S-N curve expression is shown in formula (5):

[0083]

[0084] Wherein, S -1 is the symmetric cyclic load stress level, N is the load cycle number under symmetric cyclic load, and m and C are material performance parameters.

[0085] From the general S-N curve expression, it can be seen that the input of load and the output of life all depend on the symmetric cyclic load, and the damage caused by the symmetric cyclic load and the random load has different cumulative ways, and the random load is usually quantitatively described by using the average stress and the stress amplitude to describe the load characteristics, and the linear Goodman model expression is shown in formula (6):

[0086]

[0087] From the mathematical model of formula (6), it can be seen that the composite material in the tensile and compression stages has a consistent linear symmetric damage accumulation trend, and the interaction force between the fiber and the fiber layer is not considered in the compression stage. The interaction force between the fiber and the fiber layer does not cause the material to have a damage accumulation condition similar to that in the tensile stage, and the gradual occurrence of this condition is shown in the equal-life fatigue limit diagram as a nonlinear and asymmetric bell shape. Therefore, the damage effect in the compression stage of the material is relatively conservative compared with the Goodman model. However, in the tensile stage of the material, the tensile resistance of the material is mainly due to the tensile strength of the fiber itself, and in the later stage of material damage, due to the large amount of damage accumulation, the remaining carrying capacity of the material decreases to the next load peak, directly leading to material failure, and therefore, the damage in the tensile stage of the material is relatively large compared with the Goodman model. In summary, the Goodman model is not suitable for describing the fatigue performance of composite materials.

[0088] Therefore, the present application proposes an equal-life fatigue limit model considering the fatigue performance of composite materials, and the expression of the model is shown in formula (7):

[0089]

[0090] Wherein, a, k are normalized stress amplitude and symmetric cyclic stress level, and θ 1,t , θ 2,t , θ 1,c , θ2,c , C t , C c , C , C

[0091] The equal-life fatigue limit model provided by the application is higher in accuracy in the equal-life fatigue limit graph, and is more in line with the tensile and compressive characteristics of the composite material, and has two characteristics: 1. The average stress and the stress amplitude are used to describe any random load; and 2. The symmetric cyclic load S -1 is replaced; since the blade is subjected to random load, the average stress and the stress amplitude can be used to represent each random load, and the damage caused by each random load can be represented by substituting the damage function expression.

[0092] The random load characteristics can introduce the average stress and the stress amplitude to describe the random load level, and the expression is shown in formula (8):

[0093]

[0094] wherein S max is the maximum stress of a single load, and S min is the minimum stress of a single load.

[0095] As a typical case of the random load, the symmetric cyclic load is introduced to describe the symmetric cyclic load by using the average stress and the stress amplitude, and the symmetric cyclic load in formula (5) is converted into a double-parameter function, and the expression is shown in formula (9):

[0096]

[0097] wherein N(S a ,S m ) is the fatigue life under the asymmetric cyclic load S(S a ,S m ).

[0098] Therefore, formula (9) is the result of substituting formula (7) into formula (5).

[0099] For the complex random load, the material life or the failure is usually calculated or judged according to the relationship between the stress and the life of the material by using the damage accumulation rule, and the Miner linear damage theory is widely used in engineering, and the expression is:

[0100]

[0101] wherein n i is the number of stress cycles of the i-th level.i This represents the number of cycles corresponding to the i-th stress level.

[0102] Equation (9) can be transformed to obtain N(S) for the tensile or compressive stage. a ,S m In fact, the reciprocal of N is the damage, thus yielding equation (11). Therefore, the damage caused by the i-th stress acting once is shown in equation (11):

[0103]

[0104] Among them, D i,1 (S a ,S m ) represents the i-th level load S i,1 (S a ,S m Damage caused by a single action, N i (S a ,S m S represents the number of cycles under the i-th level load. i,-1 Let be the i-th level symmetrical cyclic load, and m and C be the material property parameters. Equation (4) is a simplified expression of Equation (11).

[0105] Equation (11) represents the damage caused by a single-cycle random load. Since a load spectrum has a large number of load cycles as shown in the figure below, it is necessary to integrate to calculate the damage of the load spectrum block. Equation (11) is D(S) in the integrand of the double integral of Equation (1). a ,S m ).

[0106] When a random variable load is applied for n cycles, material failure occurs. According to the linear damage accumulation law, when the random stress S(S) a ,S m The damage to the blade after n cycles is shown in formula (3). Therefore, the blade life prediction model expression is shown in formula (1).

[0107] Substituting the random load probability density function in the above formula into the life prediction model, we can obtain the fatigue damage and life of the key parts of the blade. When the cumulative damage value reaches 1, the material is considered to have failed. The denominator of formula (1) represents the damage caused by a typical load spectrum block in the tensile and compressive stages.

[0108] The fatigue life prediction method proposed in this invention establishes an equal-life fatigue limit mathematical model considering the compressive and tensile fatigue properties of composite materials. Utilizing the linear damage accumulation rule and taking into account the statistical laws of the two parameters, mean stress and stress amplitude, a blade fatigue life prediction model is established. The equal-life fatigue damage model provided by this invention offers higher prediction accuracy for composite materials and can mitigate the risk of failure within reliability limits to a certain extent.

[0109] Taking a wind turbine blade made of GRP laminate with double 0° unidirectional layup and two stitched ±45° layups as an example, the fatigue life prediction process is as follows: Figure 2 As shown, the ultimate tensile strength of this material is 139.12 MPa, and the ultimate compressive strength is 106.4 MPa. A numerical simulation model of the blade was established to solve for the stress distribution in key critical areas, outputting a single-channel time-stress load spectrum. Invalid stress distribution points were first removed from the compiled load spectrum. The random load was then converted into a random asymmetric cyclic load using the rainflow counting method. A total of 2435 data points were obtained through numerical simulation, as shown below. Figure 3 As shown in (b), 852 cyclic loads were obtained after rainflow counting; 3. Using dual statistical parameters, the variable distribution of average stress and stress amplitude in the load spectrum after rainflow counting was analyzed, and a two-dimensional joint probability density function model was established. The average stress and stress amplitude after statistical counting both conform to a Gaussian distribution, and their probability density function is: By fitting a distribution, the parameter in which is σ a =80.253MPa, σ m =72.126MPa, μ a =40.082MPa, μ m =52.733MPa; The parameter values ​​in the SN curve were determined based on the composite material fatigue test, where the material property parameters m = 18.129 and C = 6.176 × 10^36; Figure 3 (a) and Figure 4 As shown, the parameters in the equal-life fatigue limit model are determined based on fatigue test data of composite materials under multiple stress ratios. Specifically, under symmetrical cyclic stress of 36 MPa, at a cycle count of 10^8, the parameter θ corresponding to the equal-life fatigue limit curve in the tensile region is... 1,t =0.3652, θ 2,t =1.1882, C t =5.5318×10 -8 The parameter θ corresponds to the fatigue limit curve of the same life in the compression region. 1,c =1.3068, θ 2,c =1.7445, C c =5.4136×10 -9Determine the load type. When the load is a tensile load, substitute the above parameters into the equation. In the calculation formula, the random stress S(S) is obtained. a ,S m Damage to the blade caused by n1 loads during the tensile stage of the material; when the load is during the compressive stage, the above parameters are substituted into... In the calculation formula, the random stress S(S) is obtained. a ,S m The damage caused by the blade during the material compression stage (n2 times) is calculated by summing the losses from the two stages, and the total fatigue damage D is obtained based on the total fatigue damage. The blade life can be predicted based on the total fatigue damage.

[0110] Example 2

[0111] To implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a wind turbine blade fatigue life prediction system is provided below, such as... Figure 5 As shown, the system includes:

[0112] Module 1 is used to build a wind turbine blade life prediction model.

[0113] Module 2 is established to create a numerical calculation model for the wind turbine blade whose fatigue life is to be predicted.

[0114] The load spectrum determination module 3 is used to determine the single-channel time-stress load spectrum based on the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted.

[0115] Preprocessing module 4 is used to preprocess the single-channel time-stress load spectrum and establish a two-dimensional joint probability density function model using dual statistical parameters.

[0116] The parameter determination module 5 is used to determine the parameters in the wind turbine blade life prediction model based on the two-dimensional joint probability density function model and fatigue tests under multiple stress ratios of composite materials.

[0117] The result prediction module 6 is used to input the parameters into the wind turbine blade life prediction model to obtain the fatigue life prediction result of the wind turbine blade whose fatigue life is to be predicted.

[0118] The establishment module 2 includes:

[0119] The 3D model building submodule is used to build a 3D model of the wind turbine blade whose fatigue life is to be predicted.

[0120] The computational model establishment submodule is used to perform finite element analysis on the three-dimensional model and establish a numerical computational model for the wind turbine blade whose fatigue life is to be predicted.

[0121] Example 3

[0122] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the wind turbine blade fatigue life prediction method of Embodiment 1.

[0123] Alternatively, the aforementioned electronic device may be a server.

[0124] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wind turbine blade fatigue life prediction method of Embodiment 1.

[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0126] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method of predicting fatigue life of a wind turbine blade, characterized by, The method comprises: constructing a wind turbine blade life prediction model; The wind turbine blade life prediction model is: n = n1 + n2; where N * is the fatigue life of the blade under random loading, is the damage of the blade under random stress S(S a ,S m ) in the tensile stage, is the damage of the blade under random stress S(S a ,S m ) in the compression stage, n1 is the cycle number of random stress in the tensile stage, n2 is the cycle number of random stress in the compression stage, S a is the cycle stress amplitude, S m is the mean stress of cycle stress, A is the whole blade conversion coefficient, f(S a ,S m ) is the joint probability density function of mean stress and stress amplitude, C t is the constant parameter in the tensile stage, C c is the constant parameter in the compression stage, S t is the ultimate tensile strength of the material, S c is the ultimate compressive strength of the material, D(S a ,S m ) is the damage function, S i,-1 is the i-th level of symmetric cyclic loading, m, and C is the material performance parameter. establishing a numerical calculation model of the wind turbine blade whose fatigue life is to be predicted; determining a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted; preprocessing the single-channel time-stress load spectrum, using double statistical parameters, and establishing a two-dimensional joint probability density function model; determining parameters in the wind turbine blade life prediction model according to the two-dimensional joint probability density function model and through a fatigue test under a multi-stress ratio of a composite material; the parameters include a cycle number of a random stress acting on a compression region, a cycle number of a random stress acting on a tension region, a constant parameter in a tension stage, a constant parameter in a compression stage, and an i-th symmetric cyclic load; inputting the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade whose fatigue life is to be predicted.

2. The wind turbine blade fatigue life prediction method according to claim 1, characterized in that, The method further comprises: establishing a three-dimensional model of the wind turbine blade whose fatigue life is to be predicted; performing finite element analysis on the three-dimensional model to establish the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted.

3. The wind turbine blade fatigue life prediction method of claim 1, wherein, The method further comprises: calculating stress distribution of a key dangerous position of the wind turbine blade whose fatigue life is to be predicted according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted to obtain the single-channel time-stress load spectrum; the key dangerous position includes a root of the wind turbine blade and one-third of the wind turbine blade.

4. The wind turbine blade fatigue life prediction method of claim 1, wherein, The method further comprises: preprocessing the single-channel time-stress load spectrum to determine a random asymmetric cyclic load; analyzing distribution of average stress and stress amplitude in the random asymmetric cyclic load by using double statistical parameters to establish the two-dimensional joint probability density function model.

5. A wind turbine blade fatigue life prediction method according to claim 4, characterised in that, The method further comprises: removing invalid stress distribution points in the single-channel time-stress load spectrum to obtain effective stress random load; applying a rain flow counting method to convert the effective stress random load into the random asymmetric cyclic load.

6. A wind turbine blade fatigue life prediction system, characterised in that, The system comprises: a construction module configured to construct a wind turbine blade life prediction model; The wind turbine blade life prediction model is: n = n1 + n2; where N * is the fatigue life of the blade under random loading n times, is the damage of the blade under random stress S(S a ,S m ) n1 times in the tensile stage, is the damage of the blade under random stress S(S a ,S m ) n2 times in the compression stage, n1 is the cycle number of random stress in the tensile stage, n2 is the cycle number of random stress in the compression stage, S a is the cycle stress amplitude, S m is the average stress of the cycle stress, A is the conversion coefficient of the blade, f(S a ,S m ) is the joint probability density function of the average stress and the stress amplitude conforming to the normal distribution, C t is the constant parameter in the tensile stage, C c is the constant parameter in the compression stage, S t is the ultimate tensile strength of the material, S c is the ultimate compressive strength of the material, D(S a ,S m ) is the damage function, S i,-1 is the i-th level of symmetric cyclic loading, m, and C is the material performance parameter. a establishing module configured to establish a numerical calculation model of a wind turbine blade whose fatigue life is to be predicted; a load spectrum determining module configured to determine a single-channel time-stress load spectrum according to the numerical calculation model of the wind turbine blade whose fatigue life is to be predicted; a preprocessing module configured to preprocess the single-channel time-stress load spectrum, use double statistical parameters, and establish a two-dimensional joint probability density function model; A parameter determination module is configured to determine parameters in the wind turbine blade life prediction model according to the two-dimensional joint probability density function model and through fatigue tests under a multi-stress ratio of the composite material. A result prediction module is configured to input the parameters into the wind turbine blade life prediction model to obtain a fatigue life prediction result of the wind turbine blade with the fatigue life to be predicted.

7. A wind turbine blade fatigue life prediction system according to claim 6, characterised in that, The establishing module comprises: A three-dimensional model establishing submodule is configured to establish a three-dimensional model of the wind turbine blade with the fatigue life to be predicted. A calculation model establishing submodule is configured to perform finite element analysis on the three-dimensional model to establish a numerical calculation model of the wind turbine blade with the fatigue life to be predicted.

8. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to run the computer program to enable the electronic device to perform the wind turbine blade fatigue life prediction method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to implement the wind turbine blade fatigue life prediction method according to any one of claims 1 to 5.

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