Fan blade fatigue life prediction model construction method and fatigue life prediction method

Abstract

The application relates to the technical field of fan blades, and discloses a fan blade fatigue life prediction model construction method and a fatigue life prediction method. The application constructs a target two-dimensional thermoelastic stress analysis correction model suitable for isotropic and anisotropic materials through a thermoelastic stress analysis theory and a thermoelastic temperature dissipation effect, and realizes accurate adaptation of different material characteristics of the blade. Linkage of the target two-dimensional thermoelastic stress analysis correction model and a thermal structure coupling finite element model can convert the abstract thermoelastic temperature dissipation effect into quantifiable numerical results. By establishing a fatigue life curve, the direct correlation between the thermoelastic characteristics and the fatigue life is realized. By calculating a target total fatigue damage value and establishing a fatigue life prediction model, the accurate quantification of damage under random load is realized by fusing multiple criteria, the randomness of wind fields and loads is considered, the transformation from damage quantification to the prediction model is completed, and the achievement of offline modeling is realized.

Description

Technical Field

[0001] This invention relates to the field of wind turbine blade technology, specifically to a method for constructing a fatigue life prediction model and a fatigue life prediction method for wind turbine blades. Background Technology

[0002] In the current field of wind turbine blade fatigue life prediction, life estimation is performed using traditional SN curves combined with the Miner linear cumulative damage criterion, with load acquisition relying on simplified models or local sensor data. Furthermore, the load acquisition dimension is singular, often focusing only on aerodynamic loads and neglecting the coupling effects of gravity, centrifugal loads, and environmental loads (turbulence, icing, etc.), leading to significant deviations between load input and actual operating conditions. In addition, existing technologies do not adequately consider the differences in temperature response between isotropic and anisotropic composite materials, and have not established targeted correction models, further affecting the accuracy and applicability of life prediction. Summary of the Invention

[0003] This invention provides a method for constructing a fatigue life prediction model and a fatigue life prediction method for wind turbine blades, in order to solve the problems of existing technologies neglecting the coupling effect of gravity, centrifugal load and environmental load, resulting in a large deviation between load input and actual operating conditions, and the lack of a targeted correction model, which further affects the accuracy and applicability of life prediction.

[0004] In a first aspect, the present invention provides a method for constructing a fatigue life prediction model for wind turbine blades, the method comprising: This study acquires wind speed statistics data for the target wind field, as well as a load time history database, historical measured surface temperature dataset, and a thermo-structural coupled finite element model (TEM) of the wind turbine blades throughout their entire lifespan. The load time history database includes aerodynamic, gravitational, centrifugal, and environmental loads on the wind turbine blades. The TEM model achieves mechanical and thermal continuity between the metal and composite materials through binding constraints. Based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, a target two-dimensional thermoelastic stress analysis correction model for isotropic and anisotropic materials of the wind turbine blades is constructed using the load time history database and historical measured surface temperature dataset. The thermoelastic temperature change on the wind turbine blade surface is calculated using the target two-dimensional thermoelastic stress analysis correction model and the TEM model. Based on the energy dissipation principle, a fatigue life curve characterizing the relationship between the thermoelastic temperature change and the number of cycles is established. Based on the Weibull distribution function and Miner linear cumulative damage criterion, the target total fatigue damage value of the wind turbine blades is calculated using the wind speed statistics data, load time history database, and fatigue life curve. Based on the target total fatigue damage value, a fatigue life prediction model for wind turbine blades under random loads is established.

[0005] The wind turbine blade fatigue life prediction model construction method provided by this invention provides comprehensive and multi-type basic support by acquiring wind speed statistical data sets, load time history databases, historical measured surface temperature datasets, and a thermal-structure coupled finite element model. This achieves complete pre-construction data and engineering model, solving the problems of single data and lack of linkage between environmental, load, and measured data in traditional methods. Furthermore, based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, a target two-dimensional thermoelastic stress analysis correction model adapted to isotropic and anisotropic materials is constructed, achieving precise adaptation to different material properties of the blade and improving the material specificity and model adaptability of thermoelastic analysis. Furthermore, by utilizing the linkage between the target two-dimensional thermoelastic stress analysis correction model and the thermal-structure coupled finite element model, the abstract thermoelastic temperature dissipation effect is transformed into quantifiable numerical results, thereby accurately outputting the thermoelastic temperature change on the blade surface, solving the problem of large temperature calculation deviations caused by the lack of coupling of thermal dissipation effect in traditional models. Furthermore, by establishing a fatigue life curve, a direct correlation between thermoelastic characteristics and fatigue life is achieved, constructing a dedicated life determination reference standard and improving the accuracy of life characterization. Furthermore, by calculating the total fatigue damage value of the target, multiple criteria are integrated to achieve accurate quantification of damage under random loads, taking into account the randomness of wind field and load, thus improving the comprehensiveness of damage calculation. Furthermore, by establishing a fatigue life prediction model, the transformation from damage quantification to prediction model is completed, thereby forming a reusable, full-dimensional life prediction platform and realizing the application of offline modeling results. Therefore, by implementing this invention, the problems of traditional modeling—single data, poor model adaptability, and insufficient consideration of random factors—are solved, improving the accuracy, adaptability, and engineering practicality of fatigue life prediction models.

[0006] In one optional implementation, based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, and utilizing load time history databases and historical measured surface temperature datasets, a target two-dimensional thermoelastic stress analysis correction model adapted to isotropic and anisotropic materials of wind turbine blades is constructed, including: Based on the thermoelastic stress analysis theory and the thermoelastic temperature dissipation effect, an initial two-dimensional thermoelastic stress analysis correction model is constructed using a load time history database. The parameters of the initial two-dimensional thermoelastic stress analysis correction model are calibrated using a historical measured surface temperature dataset to obtain the target two-dimensional thermoelastic stress analysis correction model.

[0007] The method for constructing a fatigue life prediction model for wind turbine blades provided by this invention establishes a two-dimensional theoretical model framework by building an initial two-dimensional thermoelastic stress analysis correction model, clarifying the theoretical and data foundation of the model. Furthermore, the model parameters are optimized using measured data, eliminating the deviation between the theoretical model and actual operating conditions, and improving the model's calculation accuracy and reliability. Therefore, by implementing this invention, the accuracy and practical adaptability of the two-dimensional thermoelastic stress analysis correction model are improved.

[0008] In one optional implementation, based on the principle of energy dissipation, a fatigue life curve characterizing the relationship between thermoelastic temperature change and the number of cycles is established, including: Based on the principle of energy dissipation, a quantitative relationship between viscous dissipation energy and elastic energy during the cyclic loading process of wind turbine blades is established. Based on the quantitative relationship, a mapping model between thermoelastic temperature change and blade fatigue damage is constructed. Based on the mapping model, the characteristic parameters of the curve are determined through blade fatigue tests under different load levels, and a fatigue life curve characterizing the relationship between thermoelastic temperature change and the number of cycles is established.

[0009] The wind turbine blade fatigue life prediction model construction method provided by this invention quantifies the energy distribution law under cyclic loading by establishing a quantitative relationship between viscous dissipation energy and elastic energy, clarifying the core energy source of fatigue damage, and providing a theoretical basis for damage mapping. Furthermore, by constructing a mapping model between thermoelastic temperature change and fatigue damage, a direct conversion from temperature characteristics to fatigue damage is achieved, simplifying the damage calculation logic and improving the efficiency of damage analysis. Furthermore, by experimentally fitting curve parameters and constructing a life curve adapted to blade operating conditions, the relevance and accuracy of the life curve are improved. Therefore, by implementing this invention, a precise correlation between thermoelastic temperature change and fatigue life is achieved, solving the problems of poor adaptability and lack of thermoelastic characteristic correlation in traditional life curves, and providing a reliable reference standard for fatigue damage calculation.

[0010] In one optional implementation, based on the Weibull distribution function and the Miner linear cumulative damage criterion, the target total fatigue damage value of the wind turbine blade is calculated using wind speed statistics datasets, load time history databases, and fatigue life curves, including: Based on the Weibull distribution function, a random load spectrum for wind turbine blades is established using wind speed statistics and load time history databases. Based on the Miner linear cumulative damage criterion, multiple fatigue lives corresponding to multiple stress amplitudes in the random load spectrum are determined using fatigue life curves. Based on multiple fatigue lives and multiple cycle counts in the random load spectrum, the fatigue damage contribution of the wind turbine blades in each wind speed range is calculated. Based on the fatigue damage contribution of the wind turbine blades in each wind speed range, the initial total fatigue damage value of the wind turbine blades is determined. The probability density function in the Weibull distribution function is used to describe the uncertainty of the random loads in the random load spectrum, and the initial total fatigue damage value is corrected to obtain the target total fatigue damage value.

[0011] The wind turbine blade fatigue life prediction model construction method provided by this invention recreates the actual random load state of the blade during service by establishing a random load spectrum, providing a realistic load input for damage calculation and improving the authenticity of the load input. Furthermore, by determining the fatigue life corresponding to the stress amplitude, it achieves rapid matching of load characteristics and life indicators, providing basic data support for damage contribution calculation and improving the efficiency of data matching. Furthermore, by calculating the fatigue damage contribution of each wind speed range, it can finely decompose damage according to the wind speed dimension, clarifying the damage proportion of different operating conditions and improving the precision of damage calculation. Furthermore, by determining the initial total fatigue damage value, it achieves the accumulation of damage across multiple ranges and loads, recreating the damage accumulation process throughout the entire life cycle. Furthermore, by correcting the initial value to obtain the target total fatigue damage value, it quantifies the uncertainty impact of random loads, optimizes the damage calculation results, and improves the accuracy of the total fatigue damage value. Therefore, by implementing this invention, accurate quantification of the total fatigue damage of the blade under random loads is achieved, solving the problems of single-factor consideration and low precision in traditional damage calculation.

[0012] In one optional implementation, based on the Weibull distribution function, a random load spectrum for the wind turbine blades is established using wind speed statistics data sets and load time history databases, including: By fitting the wind speed statistical data set with the Weibull distribution function, the wind speed distribution characteristic parameter set of the target wind field is obtained. Based on the wind speed distribution characteristic parameter set and the load time history database, multiple stress amplitudes and multiple cycle numbers are obtained through rainflow counting method. Based on the multiple stress amplitudes and multiple cycle numbers, the random load spectrum of the wind turbine blade is established.

[0013] The wind turbine blade fatigue life prediction model construction method provided by this invention fits the wind speed statistical data set using the Weibull distribution function to obtain a set of wind speed distribution characteristic parameters. This accurately describes the wind speed distribution law of the target wind field, providing environmental characteristic basis for load spectrum construction and improving wind field adaptability. Furthermore, by processing the stress amplitude and cycle number using the rainflow counting method, structured statistics and key feature extraction of the load time history are achieved, providing core quantitative data for load spectrum construction. Furthermore, by establishing a random load spectrum, a standardized and normalized random load input carrier can be formed, which can truly reflect the actual load state of the blade and improve the input accuracy of subsequent damage calculations. Therefore, by implementing this invention, by constructing a standardized random load spectrum adapted to the characteristics of the target wind field, the problems of non-standardized and unsuitable traditional load spectrum construction are solved, providing accurate and reliable load input for fatigue damage calculation.

[0014] Secondly, the present invention provides a method for predicting the fatigue life of wind turbine blades, the method comprising: The real-time surface temperature dataset and real-time dynamic load dataset of the wind turbine blade to be predicted are obtained; based on the real-time surface temperature dataset and real-time dynamic load dataset, the predicted life of the wind turbine blade is obtained by processing the wind turbine blade fatigue life prediction model, wherein the wind turbine blade fatigue life prediction model is obtained by the wind turbine blade fatigue life prediction model construction method of the first aspect above or any of its corresponding embodiments.

[0015] The wind turbine blade fatigue life prediction method provided by this invention transforms offline historical data into real-time dynamic data by collecting real-time operating status data of the blades, providing real-time input for online prediction. Furthermore, by combining real-time data with a precise offline model, real-time dynamic prediction of lifespan is achieved, completing the transition from offline modeling to online application. Therefore, by implementing this invention, the problems of traditional methods being limited to offline prediction and lacking real-time capability are solved, enabling dynamic monitoring and accurate prediction of blade fatigue life, and improving the intelligence and timeliness of wind turbine blade operation and maintenance.

[0016] Thirdly, the present invention provides a device for constructing a fatigue life prediction model for wind turbine blades, the device comprising: The first acquisition module is used to acquire wind speed statistics data set of the target wind field, as well as the load time history database, historical measured surface temperature dataset, and thermal-structure coupled finite element model of the wind turbine blades throughout their entire life cycle. The load time history database includes aerodynamic, gravity, centrifugal, and environmental loads on the wind turbine blades. The thermal-structure coupled finite element model achieves mechanical and thermal continuity between the metal and composite materials through binding constraints. The second module is used to construct a target two-dimensional thermoelastic stress analysis correction based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, using the load time history database and historical measured surface temperature dataset. This correction is adapted to the isotropic and anisotropic materials of the wind turbine blades. The model comprises: a first calculation module, used to calculate the thermoelastic temperature change on the surface of the wind turbine blade using a target two-dimensional thermoelastic stress analysis correction model and a thermal-structure coupled finite element model; a first establishment module, used to establish a fatigue life curve characterizing the relationship between the thermoelastic temperature change and the number of cycles based on the energy dissipation principle; a second calculation module, used to calculate the target total fatigue damage value of the wind turbine blade using the Weibull distribution function and Miner linear cumulative damage criterion, utilizing wind speed statistical data sets, load time history databases, and fatigue life curves; and a second establishment module, used to establish a fatigue life prediction model for the wind turbine blade under random loads based on the target total fatigue damage value.

[0017] Fourthly, the present invention provides a device for predicting the fatigue life of wind turbine blades, the device comprising: The second acquisition module is used to acquire the real-time surface temperature dataset and the real-time dynamic load dataset of the wind turbine blade to be predicted; the prediction module is used to obtain the predicted life of the wind turbine blade based on the real-time surface temperature dataset and the real-time dynamic load dataset through the wind turbine blade fatigue life prediction model, wherein the wind turbine blade fatigue life prediction model is obtained by the wind turbine blade fatigue life prediction model construction method of the first aspect or any of its corresponding embodiments.

[0018] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the wind turbine blade fatigue life prediction model construction method of the first aspect or any corresponding embodiment thereof, or the wind turbine blade fatigue life prediction method provided in the second aspect.

[0019] In a sixth aspect, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the wind turbine blade fatigue life prediction model construction method of the first aspect or any corresponding embodiment thereof, or the wind turbine blade fatigue life prediction method provided in the second aspect. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the method for constructing a wind turbine blade fatigue life prediction model according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the method for predicting the fatigue life of wind turbine blades according to an embodiment of the present invention. Figure 4 This is a structural block diagram of a wind turbine blade fatigue life prediction model construction device according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a wind turbine blade fatigue life prediction device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0024] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0025] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the wind turbine blade fatigue life prediction model construction method or the execution of the wind turbine blade fatigue life prediction method depends is described herein. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0026] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0027] This invention provides a method for constructing a fatigue life prediction model for wind turbine blades, which solves the problems of traditional modeling data being singular, poor model adaptability, and insufficient consideration of random factors, thereby improving the accuracy, adaptability, and engineering practicality of the fatigue life prediction model.

[0028] According to an embodiment of the present invention, a method for constructing a fatigue life prediction model for wind turbine blades is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for constructing a fatigue life prediction model for wind turbine blades, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 2 This is a flowchart of a method for constructing a wind turbine blade fatigue life prediction model according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the wind speed statistics data set of the target wind field, as well as the load time history database of the wind turbine blades in the target wind field throughout their entire life cycle, the historical measured surface temperature dataset, and the thermal structure coupled finite element model.

[0030] In one optional embodiment, the target wind field refers to the wind farm where the wind turbine blades are actually in service, which is the specific site where the blades bear aerodynamic and environmental loads; the wind speed statistics set refers to the long-term wind speed related statistics collected by the local meteorological station or wind field monitoring equipment of the target wind field, which may include data such as the frequency of occurrence, distribution range, and duration of different wind speed values.

[0031] In one alternative embodiment, the full life cycle refers to the entire operating cycle of a wind turbine blade from the time it is designed and manufactured and put into service until it reaches its designed fatigue life or suffers irreparable fatigue damage.

[0032] In one optional embodiment, the load time history database represents a time-series integrated set of multi-field coupled load data for the entire life cycle of wind turbine blades, which may include aerodynamic loads, gravity loads, centrifugal inertial loads, and environmental load data such as turbulence intensity, wind shear index, and icing thickness. The aerodynamic loads may include lift, drag, and bending moment of the blade element section at different wind speeds, calculated using GH BLADED software.

[0033] In one optional embodiment, the historical measured surface temperature dataset represents the measured surface temperature data of the wind turbine blades that are collected and stored in real time by an infrared thermal imager (installed on the top of the nacelle, with a sampling frequency of 30Hz) during the entire life cycle of the wind turbine blades. It can include the temperature values ​​and change patterns of each node on the blade surface under different operating conditions and different operating stages.

[0034] In one optional embodiment, the thermal-structural coupling finite element model represents a finite element simulation model built based on the structural features and material properties of wind turbine blades. It can simultaneously realize the coupled calculation of thermal field and structural field, and serves as an engineering carrier for the implementation of the two-dimensional thermoelastic stress analysis correction model. It can accurately capture the stress and strain, temperature conduction, and coupling relationship between the two of the blades, balancing calculation accuracy and efficiency.

[0035] Furthermore, the thermal-structure coupled finite element model achieves mechanical and thermal continuity between metals and composite materials through binding constraints.

[0036] For example, based on the material distribution characteristics of the wind turbine blades, a hybrid element modeling approach is adopted to construct sub-models for both the metal components and the composite material body. Specifically, solid element modeling is used for the isotropic metal components of the wind turbine blades (blade root connection section, metal embedded parts, connectors, etc.) to accurately capture the three-dimensional stress-strain and temperature conduction patterns of the metal components. Furthermore, shell element modeling is used for the anisotropic composite material body of the wind turbine blades (blade body, skin, main beam, etc.) to efficiently simulate the thin film / bending effect and heat transfer process of the composite material.

[0037] Furthermore, to address the interface connection issue between the metallic solid element and the composite shell element, a bonded constraint is used to achieve both mechanical and thermal continuity, ensuring uninterrupted transfer of load and temperature at the interface. Specifically, after establishing the metallic solid element model and the composite shell element model respectively, a bonded constraint is added, simultaneously satisfying both mechanical bonding (transferring load and stress) and thermal bonding (transferring temperature and heat flow).

[0038] Furthermore, discretized load data from the load time-history database is imported into the model. Simultaneously, thermal boundary conditions tailored to the actual operation of the blade are defined, and specific thermophysical and mechanical parameters are assigned to different materials. Specifically, the multi-field coupled load time-history data throughout the entire lifecycle is discretized and imported into the finite element model as the load input for structural field calculations. Then, taking the entire outer surface of the wind turbine blade (the surface of the metal components and the outer surface of the composite shell) as the boundary object, the convective heat transfer coefficient is set segmentally along the spanwise direction. At the same time, define the ambient temperature. It is the basic thermal boundary.

[0039] in, This indicates the actual wind speed of the target wind field, in m / s; further, the blade tip... The largest, and the leaves and roots are the smallest.

[0040] Furthermore, default parameters or precise experimental parameters can be set for metallic materials; based on the actual design parameters of the wind turbine blades, ply angles, number of layers, and corresponding thermophysical and mechanical parameters such as thermal conductivity, specific heat capacity, and elastic modulus can be set for composite materials.

[0041] Furthermore, based on the requirements of thermal-structural coupling calculations, a suitable dedicated solver is selected, and the final construction of the thermal-structural coupling finite element model is completed, ensuring that the model can achieve coupled solution of temperature and stress.

[0042] For example, a professional simulation solver that supports thermal-structural coupling can be selected, such as ABAQUS's Standard / Explicit solver or ANSYS's Transient Structural+Thermal coupled solver.

[0043] Step S202: Based on the thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, a target two-dimensional thermoelastic stress analysis correction model for isotropic and anisotropic materials adapted to wind turbine blades is constructed using a load time history database and historical measured surface temperature dataset.

[0044] In one optional embodiment, thermoelastic stress analysis (TSA) theory represents a non-contact optical measurement technique based on the thermo-mechanical coupling effect, used to visualize and quantitatively analyze the stress distribution across the entire field by detecting the minute temperature changes on the surface of a material or structure caused by stress changes when it is subjected to cyclic elastic loads.

[0045] In an optional embodiment, the thermoelastic temperature dissipation effect refers to the phenomenon that the stress-induced thermoelastic temperature change of the wind turbine blade under low-frequency cyclic load (f≤10Hz) will dissipate heat through three forms: heat conduction, heat convection, and heat radiation. It can include the heat conduction, heat convection, and heat radiation temperature dissipation effects under low-frequency cyclic load.

[0046] In one optional embodiment, the isotropic material of the wind turbine blade refers to the metallic material (such as A3 steel or 6061 aluminum alloy) applied to the wind turbine blade. Its mechanical and thermophysical properties are consistent in all directions and have no direction dependence. It is mainly used in key load-bearing connection parts such as blade root connection section and metal embedded parts.

[0047] Furthermore, the anisotropic material of wind turbine blades refers to the fiber-reinforced composite material applied to the main body of wind turbine blades. Its mechanical and thermophysical properties have obvious direction dependence, that is, the performance along the fiber layup direction is significantly different from that in the vertical direction. It can adapt to the aerodynamic force requirements of the blade by designing the layup angle / number of layers. It is mainly used in core structural parts such as blade body, skin, and main beam.

[0048] In one optional embodiment, based on the thermoelastic stress analysis theory, considering the heat conduction, heat convection and heat radiation temperature dissipation effects under low-frequency cyclic loading, the load time history database is used as the load input basis, and the model parameters are calibrated by combining the historical measured surface temperature dataset. In this way, a target two-dimensional thermoelastic stress analysis correction model that is suitable for both isotropic and anisotropic materials of wind turbine blades can be finally constructed.

[0049] Step S203: Calculate the thermoelastic temperature change on the surface of the wind turbine blade using the target two-dimensional thermoelastic stress analysis correction model and the thermal structure coupled finite element model.

[0050] In an optional embodiment, the thermoelastic temperature change represents the absolute change in blade surface temperature caused by the thermoelastic effect when the wind turbine blade material is subjected to stress by multi-field coupled loads.

[0051] In an optional embodiment, the thermoelastic temperature change represents the actual temperature change under low-frequency cyclic loading after compensation for thermoelastic temperature dissipation effect and correction by a two-dimensional model.

[0052] In one optional embodiment, the target two-dimensional thermoelastic stress analysis correction model is combined with the thermo-structural coupled finite element model that fits the actual structure of the blade. Then, through the collaborative calculation of the two models, the thermoelastic temperature change of each node on the surface of the wind turbine blade can be accurately solved.

[0053] For example, multi-field coupled load time history data from the load time history database are synchronously input into the target two-dimensional thermoelastic stress analysis correction model and the thermo-structural coupled finite element model. Then, the coupled solver in the thermo-structural coupled finite element model is used to perform thermo-structural coupled numerical calculations on the wind turbine blades, which can accurately solve and extract the stress data of each node on the blade surface, including the three-dimensional normal stress of isotropic metal components. , And the stress tensor of the corresponding layup direction of the anisotropic composite material.

[0054] Furthermore, after receiving the stress data transmitted from the finite element model, the target two-dimensional thermoelastic stress analysis correction model can calculate the thermoelastic temperature change at each node based on the material type (isotropic / anisotropic) of each part of the wind turbine blade and in conjunction with the thermoelastic temperature dissipation effect compensation logic. The following relation (1) is shown: (1) In the formula: Indicates the coefficient of thermal expansion; Indicates ambient temperature; Indicates density; This indicates the specific heat capacity at constant pressure. This indicates that the first invariant of the stress tensor must be considered in the two-dimensional case of normal stress.

[0055] Furthermore, in a two-dimensional scenario, the above relation (1) can be extended to the following relation (2): (2) Furthermore, based on the above relationships (1) and (2), and combined with the refined correction of the thermophysical parameters of the composite material layup direction, while fully coupling the temperature dissipation effects of heat conduction, heat convection, and heat radiation, the calculation results of the above relationships (1) and (2) are compensated for dissipation to obtain the thermoelastic temperature change of each node in the composite material. .

[0056] In an optional embodiment, the calculation results can also be verified for accuracy to ensure that the error with the measured value is controlled within 5%. Then, dangerous nodes on the surface of the wind turbine blades and key parts of the entire structure are extracted. Values ​​are used to generate cloud maps of temperature changes on the blade surface and nodes. The time-history data is ultimately integrated and output as a standardized dataset of thermoelastic temperature changes on the surface of wind turbine blades.

[0057] Step S204: Based on the principle of energy dissipation, establish a fatigue life curve that characterizes the relationship between the thermoelastic temperature change and the number of cycles.

[0058] In one optional embodiment, the energy dissipation principle states that under low-frequency cyclic loads, the mechanical work input by the wind turbine blades will not be completely stored in a recoverable elastic form, but will undergo energy conversion and dissipation. Some of the energy will be converted into unrecoverable energy, leading to material performance degradation and fatigue damage.

[0059] In an optional embodiment, the number of cycles represents the number of times the blade material undergoes periodic stress / strain changes when subjected to multi-field coupled cyclic loads during the operation of the wind turbine blade in the target wind farm.

[0060] In one optional embodiment, based on the principle of energy dissipation, the intrinsic relationship between thermoelastic temperature change and fatigue damage is established by quantifying the energy conversion relationship during the cyclic loading process of the blade. This ultimately enables the construction of a characterizing thermoelastic temperature change. With load cycle number The fatigue life curves corresponding to the relationship.

[0061] Step S205: Based on the Weibull distribution function and Miner linear cumulative damage criterion, the target total fatigue damage value of the wind turbine blade is calculated using wind speed statistics data set, load time history database and fatigue life curve.

[0062] In an optional embodiment, the Weibull distribution function represents a continuous probability distribution. In this embodiment, it is a two-parameter probability distribution function used to accurately describe the probability distribution of wind speed in a target wind field. It may include a probability density function (PDF) and a cumulative distribution function (CDF, ​​representing wind speed). The probabilities of , are shown in the following relationships (3) and (4): (3) (4) In the formula: Indicates wind speed; This represents a shape parameter, has no unit, and is used to characterize the concentration of wind speed distribution. At that time, the wind speed distribution approximates a normal distribution; At that time, the wind speed distribution is more concentrated and the fluctuations are smaller; At that time, wind speed distribution is more dispersed and fluctuates more greatly; This represents the scale parameter, in m / s, and is positively correlated with the mean wind speed. It is approximately 1.2 times the most probable wind speed (the wind speed with the highest probability of occurrence) of the target wind field and is used to determine the scale range of the distribution. This indicates that the wind speed falls within the range. The probability within a certain range of wind speeds; Indicates wind speed The cumulative probability, i.e., wind speed The sum of probabilities is used to calculate the cumulative probability of occurrence within a certain wind speed range.

[0063] Furthermore, the annual working hours = total annual hours (8760h) × probability of the wind speed range, which represents the effective operating time of the target wind field in a certain wind speed range each year.

[0064] In one optional embodiment, the Miner linear cumulative damage criterion is a classic criterion for fatigue damage calculation. Its core principle is that under the action of multiple load spectra, the fatigue damage of the material has a linear cumulative trend with the number of load cycles. When the cumulative damage value under each stress level reaches 1, the material fails due to fatigue.

[0065] In one optional embodiment, based on the Weibull distribution function and Miner linear cumulative damage criterion, and combined with the wind speed statistics set of the target wind field, the load time history database of the entire blade life cycle, and the constructed thermoelastic temperature change-cycle fatigue life curve, the initial total fatigue damage value of the blade is first calculated through multi-criteria fusion and multi-data linkage, and then the random load uncertainty is corrected for it, so that the accurate target total fatigue damage value of the wind turbine blade can be finally obtained.

[0066] Step S206: Based on the target total fatigue damage value, establish a fatigue life prediction model for wind turbine blades under random loads.

[0067] In one optional embodiment, random load action refers to the stress state of the wind turbine blades during actual operation in the target wind field, characterized by multiple coupled loads with no fixed pattern and exhibiting random dynamic changes. Furthermore, this state is caused by the random distribution of wind speed in the wind field (turbulence, wind shear, wind speed fluctuations, etc.), which leads to the magnitude, frequency, and mode of action of aerodynamic, centrifugal, and environmental loads on the blades changing in real time with the wind speed, and the stress amplitude and number of load cycles have no fixed period.

[0068] In one optional embodiment, by using the target total fatigue damage value as a quantitative basis and combining it with the operating characteristics of the wind turbine blade throughout its entire life cycle, a wind turbine blade fatigue life prediction model that can adapt to the random load conditions of the target wind farm can be established.

[0069] For example, the core basic parameters of the model can be calibrated based on the actual operating characteristics of the target wind farm, that is, the target total fatigue damage value can be defined as the cumulative fatigue damage value of the wind turbine blades per unit time (1 year) in the target wind farm. At the same time, the fatigue failure judgment threshold was determined to be... That is, when the cumulative damage value reaches 1, the blade will fail due to fatigue.

[0070] Furthermore, based on the damage-life inverse correlation principle and the assumption of uniform accumulation of damage throughout the entire life cycle, the formula for calculating the design life of the new blade is derived, as shown in the following equation (5): (5) In the formula: This indicates the design life of the wind turbine blades, that is, the lifespan of the wind turbine blades from brand new (damaged) to fully developed. From failure state (damage) The cumulative running time of ).

[0071] Furthermore, for blades that have been in service for years, their cumulative damage is calculated using the following formula (6): (6) Furthermore, its remaining lifetime can be calculated using the following relationship (7): (7) In the formula: Indicates remaining lifespan.

[0072] Furthermore, the main framework of the wind turbine blade fatigue life prediction model is constructed, and the above-mentioned design life and remaining life calculation formulas are integrated into the model. At the same time, the probability density function used to correct the target total fatigue damage value and the Weibull distribution characteristic parameters of the wind field speed distribution are integrated into the model, and finally the corresponding wind turbine blade fatigue life prediction model is constructed.

[0073] The wind turbine blade fatigue life prediction model construction method provided in this embodiment solves the problems of traditional modeling data being singular, poor model adaptability, and insufficient consideration of random factors, thereby improving the accuracy, adaptability, and engineering practicality of the fatigue life prediction model.

[0074] In some optional implementations, step S202 above includes: Step S2021: Based on the thermoelastic stress analysis theory and the thermoelastic temperature dissipation effect, an initial two-dimensional thermoelastic stress analysis correction model is constructed using a load time history database.

[0075] In one optional embodiment, based on the thermoelastic stress analysis (TSA) theory, considering the heat conduction, heat convection, and heat radiation temperature dissipation effects under low-frequency cyclic loads on wind turbine blades, and using the full life cycle load time history database as the only load input, appropriate thermoelastic calculation logic and parameter systems are designed for the differences in thermophysical properties between isotropic metallic materials and anisotropic composite materials of wind turbine blades, thereby enabling the construction of an initial two-dimensional thermoelastic stress analysis correction model that has not been calibrated with measured data.

[0076] For example, a framework for a two-dimensional thermoelastic stress analysis correction model is constructed, and the model is divided into an isotropic material calculation dimension and an anisotropic material calculation dimension according to the material type of the wind turbine blade. Furthermore, the two dimensions are independent of each other and can achieve unified data output, corresponding to the thermoelastic calculation of the metal components of the blade (blade root, embedded parts, etc.) and the composite material body (blade body, skin, main beam, etc.), respectively.

[0077] Furthermore, the thermoelastic stress analysis theory is embedded into the core algorithm of the model, and a compensation module for heat conduction, heat convection, and heat radiation temperature dissipation effects under low-frequency cyclic loading is integrated. A quantitative correlation between heat dissipation and load frequency and environmental parameters is established.

[0078] Furthermore, corresponding thermoelastic calculation equations, namely the above-mentioned relations (1) and (2), are configured for the isotropic material dimension, and the initial values ​​of the basic parameters are completed. At the same time, for the anisotropic material dimension, based on the above-mentioned relations (1) and (2), a composite material layup direction correction factor is added, and the basic thermophysical parameters of the composite material (glass fiber / carbon fiber epoxy resin) and the design layup angle and number of layers are initially integrated, and the layup direction fine calculation logic is enabled.

[0079] Furthermore, the time-history database of the wind turbine blade's entire life cycle load is integrated into the model. The model automatically extracts multi-field coupled load data under different wind speeds and operating conditions, and converts the load data into normal stresses at various parts of the blade through stress calculation. , The input of equal stress enables the automated conversion of load into stress, providing a stress data source for thermoelastic temperature calculation.

[0080] Furthermore, the model is configured with dual-precision calculation logic for both fast estimation and precise analysis modes, and an initial dual-dimensional thermoelastic stress analysis correction model is output, which enables dual-dimensional thermoelastic temperature calculation.

[0081] In the rapid estimation mode, thermal radiation is ignored for isotropic materials and thermal conductivity is simplified for anisotropic materials; in the precise analysis mode, both types of materials are fully coupled with the three major temperature dissipation effects, and fine calculation of the layup direction is enabled for anisotropic materials.

[0082] Step S2022: Use historical measured surface temperature dataset to calibrate the parameters of the initial two-dimensional thermoelastic stress analysis correction model to obtain the target two-dimensional thermoelastic stress analysis correction model.

[0083] In one optional embodiment, the historical measured surface temperature dataset of the wind turbine blade throughout its entire life cycle is used as the calibration basis. The theoretically calculated temperature value of the initial two-dimensional thermoelastic stress analysis correction model is compared with the measured blade surface temperature value by the infrared thermal imager. Then, the key thermophysical parameters in the model are corrected and optimized by numerical fitting method to eliminate the deviation between the theoretical model and the actual thermoelastic response characteristics of the blade. Finally, the target two-dimensional thermoelastic stress analysis correction model with calculation accuracy that meets the engineering requirements can be obtained.

[0084] For example, measured temperature data of key nodes on the blade surface under different operating conditions collected by infrared thermal imagers can be extracted from historical measured surface temperature datasets and classified and organized according to time series and operating condition type to form a standardized measured dataset.

[0085] Furthermore, the operating load data corresponding to the standardized measured dataset is input into the initial two-dimensional TSA correction model, and the model is run to obtain the theoretical calculated values ​​of thermoelastic temperature under the same node and operating conditions on the blade surface, thereby forming a theoretical calculation dataset that corresponds one-to-one with the measured dataset.

[0086] Furthermore, the theoretical calculation dataset and the measured dataset are compared node by node and operating condition by condition, and the temperature calculation deviation rate of each node is calculated to generate a cloud map of the blade surface temperature calculation deviation distribution. Then, the key parts with large deviations (such as the blade tip and blade root connection section) are located, and the core reasons for the deviation are analyzed, such as the deviation of thermal conductivity assignment and unreasonable setting of convective heat transfer coefficient.

[0087] Furthermore, the least squares method can be used to iteratively correct the key thermophysical parameters in the model. Specifically, thermal conductivity and convective heat transfer coefficient can be extracted from the isotropic and anisotropic calculation dimensions, respectively. Coefficient of thermal expansion Key parameters requiring calibration include the convective heat transfer coefficient. Calibration is performed by segmenting the leaf blade along its span (leaf tip, leaf shaft, leaf root).

[0088] Furthermore, the parameters are substituted into the model to recalculate the temperature values, and the mean square error between the calculated and measured values ​​is compared. If the error is greater than 5%, the parameters are adjusted until the error meets the accuracy requirements. Additionally, for anisotropic materials, the thermal conductivity correction factor in the layup direction is calibrated to ensure the calculation accuracy of the composite material components.

[0089] Furthermore, a validation dataset (i.e., uncalibrated measured data) from the historical measured surface temperature dataset is selected, and the model with corrected input parameters is used to calculate the temperature. This verifies whether the deviation between the calculated and measured values ​​is controlled within 5%. If the validation passes, all calibration parameters in the model are solidified, model optimization is completed, and the corresponding optimized target two-dimensional thermoelastic stress analysis correction model is obtained.

[0090] In some optional implementations, step S204 above includes: Step S2041: Based on the principle of energy dissipation, establish the quantitative relationship between viscous dissipation energy and elastic energy during the cyclic loading process of wind turbine blades.

[0091] In one optional embodiment, based on the principle of energy dissipation, the stress characteristics of wind turbine blades under low-frequency cyclic load (f≤10Hz) are quantified and decomposed into the energy conversion form of external mechanical work input, and the proportion and mutual quantitative relationship of elastic strain energy (recoverable) and viscous dissipation energy (non-recoverable, the core source of fatigue damage) are determined, thereby establishing a direct correlation between viscous dissipation energy and thermoelastic temperature change.

[0092] For example, based on the principle of energy dissipation, the total external input mechanical work of the wind turbine blades under low-frequency cyclic load is determined. The method of energy decomposition is shown in the following relationship (8): (8) In the formula: It represents elastic strain energy, which can be recovered after unloading, and is related to the material's elastic modulus and stress-strain. This indicates viscous dissipated energy, which is irreversible and is converted into heat energy due to internal friction of the material, microcrack propagation, etc. This represents the heat dissipation energy from heat conduction, convection, and radiation. It accounts for 15% at low frequencies and can be corrected using the TSA model.

[0093] Furthermore, the dissipation ratio is defined. and will Substitute the above relationship (8) and establish the viscous dissipation energy With elasticity The quantitative relationship.

[0094] Furthermore, material samples (isotropic metals and anisotropic composite materials) from the core load-bearing components of the wind turbine blades were selected and subjected to low-frequency cyclic loading tests at different load levels. Data were collected for each load cycle. , , and corresponding The values ​​are obtained, and corresponding energy-temperature datasets are generated under multiple load levels.

[0095] Furthermore, based on energy-temperature datasets under multiple load levels, linear fitting was used to obtain... The relationship between load and material, and verification. The linearity of the energy dissipation, and thus the viscous energy dissipation is ultimately achieved. With elasticity The quantitative relationship is established.

[0096] Step S2042: Based on the quantification relationship, construct a mapping model between the thermoelastic temperature change and the fatigue damage of the blade.

[0097] In an alternative embodiment, based on viscous dissipation energy With elasticity The quantitative relationship and the principle of energy dissipation are combined to determine the dissipation ratio. With fatigue damage The following exponential relationship is satisfied: (9) (9) In the formula: Indicates the initial dissipation ratio ( (When the material has no inherent dissipation ratio without damage). , The degree of maturity of a material is related to its type and microstructure, and can be determined experimentally. It reflects the extent to which the dissipation ratio increases with damage. It reflects the rate at which the dissipation ratio increases with damage. This represents the normalized fatigue loss value, with a range of [0, 1]. Indicates no damage; This indicates that the material has experienced fatigue failure.

[0098] Furthermore, Substitute the thermoelastic temperature change The quantification formula, and combined with The relationship with stress can ultimately yield the thermoelastic temperature change. Leaf fatigue damage The mapping model is shown in the following relation (10): (10) In the formula: Indicates the density of the blade material; This indicates the specific heat capacity at constant pressure of the blade material.

[0099] Step S2043: Based on the mapping model, the characteristic parameters of the curve are determined through blade fatigue tests under different load levels, and a fatigue life curve characterizing the relationship between the thermoelastic temperature change and the number of cycles is established.

[0100] In an optional embodiment, the Miner linear cumulative damage criterion is used to define fatigue damage. The following relation (11) is shown: (11) In the formula: Indicates the current loop count; This indicates the fatigue life of the material at this load level; at failure, .

[0101] Furthermore, by substituting the above relation (11) into the mapping model shown in the above relation (10), the thermoelastic temperature change can be obtained. With the number of loops The relationship between them is shown in the following relation (12): (12) Furthermore, multiple sets of data were collected through fatigue tests at different load levels. and The data is used to fit the curve characteristic parameters using the above relation (10). , , , Then, substituting these values ​​into the above relationship (10), we can construct the corresponding fatigue life curve covering the entire load range, i.e. curve.

[0102] In some optional implementations, step S205 above includes: Step S2051: Based on the Weibull distribution function, a random load spectrum of the wind turbine blades is established using wind speed statistics data set and load time history database.

[0103] In an optional embodiment, the random load spectrum represents a standardized load dataset that reflects the actual service stress state of the blade after statistical processing, which combines the randomly varying wind speed of the target wind field with the multi-field coupled loads throughout the life cycle of the wind turbine blade. It may include a series of stress amplitudes and corresponding cycle numbers.

[0104] In one alternative embodiment, the wind speed pattern of the wind field is fitted with a Weibull distribution, combined with a load time history database, and the stress amplitude and cycle number are extracted by rainflow counting method, which can ultimately form a random load spectrum that can be used for fatigue calculation.

[0105] Specifically, step S2051 includes: Step a1: Fit the wind speed statistics data set using the Weibull distribution function to obtain the wind speed distribution characteristic parameter set of the target wind field.

[0106] Step a2: Based on the wind speed distribution characteristic parameter set and load time history database, multiple stress amplitudes and multiple cycle numbers are obtained through rainflow counting method.

[0107] Step a3: Establish the random load spectrum of the wind turbine blades based on multiple stress amplitudes and multiple cycle numbers.

[0108] In an optional embodiment, the rain flow counting method represents a fatigue load statistical method that decomposes the measured load time history into several load cycles for component fatigue life analysis and test load spectrum compilation. Its principle is based on the two-parameter method that simultaneously considers stress amplitude and mean.

[0109] In one optional embodiment, the wind speed in the natural wind field conforms to a two-parameter Weibull distribution, which, after fitting, can accurately describe the probability of each wind speed occurring. Therefore, by fitting the wind speed statistical data set using the Weibull distribution function, the shape parameters can be calculated. Scale parameters Isotropic wind speed distribution characteristic parameters.

[0110] Furthermore, the annual operating hours for each wind speed interval are calculated based on the wind speed distribution, and the load cycle number at each wind speed is obtained by combining this with the blade rotation speed. Further, the load time history can be segmented and mapped to each wind speed interval according to the wind speed distribution characteristics. Then, rainflow counting is performed on each load time history segment, and the stress amplitude and cycle number corresponding to each cycle are extracted.

[0111] Furthermore, stress amplitudes and cycle counts can be categorized according to wind speed ranges. Then, stress amplitudes are arranged in ascending order, and the corresponding cycle counts and wind speed ranges are labeled to generate a complete random load spectrum for wind turbine blades.

[0112] Step S2052: Based on the Miner linear cumulative damage criterion, the fatigue life corresponding to multiple stress amplitudes in the random load spectrum is determined using the fatigue life curve.

[0113] In an alternative embodiment, all stress amplitudes are read from the random load spectrum and then... Using the curve as a reference, the fatigue life corresponding to each stress amplitude can be obtained.

[0114] Step S2053: Calculate the fatigue damage contribution of the wind turbine blades in each wind speed range based on multiple fatigue lives and multiple cycles in the random load spectrum.

[0115] In one optional embodiment, the actual number of cycles is obtained for each wind speed range. and corresponding fatigue life Then, according to the Miner's criterion, divide by the number of iterations. To correspond to fatigue life This allows us to obtain the damage contribution for each wind speed range.

[0116] Step S2054: Determine the initial total fatigue damage value of the wind turbine blades based on the fatigue damage contribution of the wind turbine blades in each wind speed range.

[0117] In one optional embodiment, the fatigue damage contribution of all wind speed ranges is summed to obtain the initial total fatigue damage of the blade, i.e., the initial total fatigue damage value.

[0118] Step S2055: The probability density function in the Weibull distribution function is used to describe the uncertainty of the random load in the random load spectrum, and the initial total fatigue damage value is corrected to obtain the target total fatigue damage value.

[0119] In one optional embodiment, the Weibull probability density function is used to describe the random fluctuations of load and wind speed, and then the initial total damage is substituted into the correction model to complete the uncertainty compensation, and finally output the target total fatigue damage value after probability correction.

[0120] This embodiment provides a method for predicting the fatigue life of wind turbine blades, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 3 This is a flowchart of a wind turbine blade fatigue life prediction method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain the real-time surface temperature dataset and real-time dynamic load dataset of the wind turbine blades to be predicted.

[0121] In one optional embodiment, the real-time surface temperature dataset represents the set of blade surface temperature data collected in real time at the current operating moment of the wind turbine blade by an infrared thermal imager (sampling frequency is usually 30Hz) installed on the top of the nacelle. It may include the instantaneous temperature values ​​and short-term temperature change trends of each key monitoring node on the blade surface (such as blade tip, main beam, blade root connection section, etc.) under the current operating conditions.

[0122] In one optional embodiment, the real-time dynamic load dataset represents a set of multi-field coupled dynamic load data obtained by the wind turbine SCADA system, load sensor or aerodynamic load real-time calculation module at the current operating moment of the wind turbine blades. It may include the instantaneous values ​​and short-term change histories of aerodynamic loads, gravity loads, centrifugal loads and environmental loads (such as turbulence, wind shear, etc.) under the current operating conditions.

[0123] Step S302: Based on the real-time surface temperature dataset and the real-time dynamic load dataset, the predicted life of the wind turbine blade is obtained through the wind turbine blade fatigue life prediction model.

[0124] The wind turbine blade fatigue life prediction model is obtained through the wind turbine blade fatigue life prediction model construction method provided in the above embodiments of the present invention.

[0125] In an optional embodiment, the acquired real-time surface temperature dataset and real-time dynamic load dataset are input into the fatigue life prediction model, which can output the predicted life of the wind turbine blades. For details, please refer to [reference needed]. Figure 1 Step S206 of the illustrated embodiment will not be described again here.

[0126] This embodiment also provides a wind turbine blade fatigue life prediction model construction device and a wind turbine blade fatigue life prediction device. These devices are used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0127] This embodiment provides a device for constructing a fatigue life prediction model for wind turbine blades, such as... Figure 4 As shown, the device includes: The first acquisition module 401 is used to acquire the wind speed statistics data set of the target wind field, as well as the load time history database of the wind turbine blades in the target wind field throughout their entire life cycle, the historical measured surface temperature dataset, and the thermal structure coupled finite element model. The load time history database includes the aerodynamic, gravity, centrifugal, and environmental loads of the wind turbine blades. The thermal structure coupled finite element model achieves mechanical and thermal continuity between the metal and composite materials through binding constraints.

[0128] Module 402 is used to construct a target two-dimensional thermoelastic stress analysis correction model for isotropic and anisotropic materials of wind turbine blades, based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, and utilizing load time history database and historical measured surface temperature dataset.

[0129] The first calculation module 403 is used to calculate the thermoelastic temperature change on the surface of the wind turbine blade by using the target two-dimensional thermoelastic stress analysis correction model and the thermal structure coupled finite element model.

[0130] The first module 404 is used to establish a fatigue life curve characterizing the relationship between the thermoelastic temperature change and the number of cycles, based on the principle of energy dissipation.

[0131] The second calculation module 405 is used to calculate the target total fatigue damage value of the wind turbine blade based on the Weibull distribution function, probability density function, and Miner linear cumulative damage criterion, using wind speed statistical data set, load time history database, and fatigue life curve.

[0132] The second module 406 is used to establish a wind turbine blade fatigue life prediction model under random load based on the target total fatigue damage value.

[0133] The wind turbine blade fatigue life prediction model construction device provided in this embodiment of the invention can execute the wind turbine blade fatigue life prediction model construction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules are the same as in the corresponding embodiments described above, and will not be repeated here.

[0134] This embodiment also provides a wind turbine blade fatigue life prediction device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0135] This embodiment provides a device for predicting the fatigue life of wind turbine blades, such as... Figure 5 As shown, the device includes: The second acquisition module 501 is used to acquire the real-time surface temperature dataset and the real-time dynamic load dataset of the wind turbine blade to be predicted.

[0136] The prediction module 502 is used to obtain the predicted life of the wind turbine blade based on the real-time surface temperature dataset and the real-time dynamic load dataset through the wind turbine blade fatigue life prediction model. The wind turbine blade fatigue life prediction model is obtained by the wind turbine blade fatigue life prediction model construction method of the above embodiment of the present invention.

[0137] The wind turbine blade fatigue life prediction device provided in this embodiment of the invention can execute the wind turbine blade fatigue life prediction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules are the same as in the corresponding embodiments described above, and will not be repeated here.

[0138] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0139] The following is a detailed reference. Figure 6This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0140] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0141] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the wind turbine blade fatigue life prediction model construction method or the wind turbine blade fatigue life prediction method of the embodiments of the present invention.

[0142] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0143] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the wind turbine blade fatigue life prediction model construction method or wind turbine blade fatigue life prediction method shown in the above embodiments is implemented.

[0144] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0145] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a fatigue life prediction model for wind turbine blades, characterized in that, The method includes: The system acquires a set of wind speed statistics data for the target wind field, as well as a load time history database, historical measured surface temperature dataset, and a thermal-structure coupled finite element model of the wind turbine blades throughout their entire life cycle. The load time history database includes aerodynamic, gravity, centrifugal, and environmental loads on the wind turbine blades. The thermal-structure coupled finite element model achieves mechanical and thermal continuity between the metal and composite materials through binding constraints. Based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, using the load time history database and the historical measured surface temperature dataset, a target two-dimensional thermoelastic stress analysis correction model adapted to the isotropic and anisotropic materials of the wind turbine blades is constructed. The thermoelastic temperature change of the wind turbine blade surface is calculated using the target two-dimensional thermoelastic stress analysis correction model and the thermal structure coupled finite element model. Based on the principle of energy dissipation, a fatigue life curve characterizing the relationship between thermoelastic temperature change and the number of cycles is established. Based on the Weibull distribution function and the Miner linear cumulative damage criterion, the target total fatigue damage value of the wind turbine blade is calculated using the wind speed statistics data set, the load time history database, and the fatigue life curve. Based on the target total fatigue damage value, a fatigue life prediction model for wind turbine blades under random loads is established.

2. The method according to claim 1, characterized in that, Based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, and utilizing the load time history database and the historical measured surface temperature dataset, a target two-dimensional thermoelastic stress analysis correction model adapted to the isotropic and anisotropic materials of the wind turbine blades is constructed, including: Based on the thermoelastic stress analysis theory and the thermoelastic temperature dissipation effect, an initial two-dimensional thermoelastic stress analysis correction model is constructed using the load time history database. The parameters of the initial two-dimensional thermoelastic stress analysis correction model are calibrated using the historical measured surface temperature dataset to obtain the target two-dimensional thermoelastic stress analysis correction model.

3. The method according to claim 1, characterized in that, Based on the principle of energy dissipation, a fatigue life curve characterizing the relationship between thermoelastic temperature change and the number of cycles is established, including: Based on the aforementioned energy dissipation principle, a quantitative relationship between viscous dissipation energy and elastic energy during the cyclic loading process of the wind turbine blades is established. Based on the quantification relationship, a mapping model between the thermoelastic temperature change and blade fatigue damage is constructed. Based on the mapping model, the characteristic parameters of the curve are determined by blade fatigue tests under different load levels, and the fatigue life curve characterizing the relationship between the thermoelastic temperature change and the number of cycles is established.

4. The method according to claim 1, characterized in that, Based on the Weibull distribution function and Miner's linear cumulative damage criterion, the target total fatigue damage value of the wind turbine blade is calculated using the wind speed statistics dataset, the load time history database, and the fatigue life curve, including: Based on the Weibull distribution function, and using the wind speed statistics data set and the load time history database, a random load spectrum for the wind turbine blades is established. Based on the Miner linear cumulative damage criterion, the fatigue life curve is used to determine multiple fatigue lives corresponding to multiple stress amplitudes in the random load spectrum. Based on the multiple fatigue lives and the multiple cycle counts in the random load spectrum, the fatigue damage contribution of the wind turbine blades in each wind speed range is calculated. The initial total fatigue damage value of the wind turbine blade is determined based on the fatigue damage contribution of the wind turbine blade in each wind speed range. The uncertainty of the random load in the random load spectrum is described by the probability density function in the Weibull distribution function, and the initial total fatigue damage value is corrected to obtain the target total fatigue damage value.

5. The method according to claim 4, characterized in that, Based on the Weibull distribution function, and utilizing the wind speed statistics dataset and the load time history database, a random load spectrum for the wind turbine blades is established, including: By fitting the wind speed statistics data set with the Weibull distribution function, the wind speed distribution characteristic parameter set of the target wind field is obtained; Based on the wind speed distribution characteristic parameter set and the load time history database, multiple stress amplitudes and multiple cycle numbers are obtained through rainflow counting method. Based on the multiple stress amplitudes and the multiple cycle numbers, a random load spectrum for the wind turbine blades is established.

6. A method for predicting the fatigue life of wind turbine blades, characterized in that, The method includes: Obtain the real-time surface temperature dataset and real-time dynamic load dataset of the wind turbine blade to be predicted; Based on the real-time surface temperature dataset and the real-time dynamic load dataset, the predicted life of the wind turbine blade is obtained through the wind turbine blade fatigue life prediction model. The wind turbine blade fatigue life prediction model is obtained by the wind turbine blade fatigue life prediction model construction method according to any one of claims 1 to 5.

7. A device for constructing a fatigue life prediction model for wind turbine blades, characterized in that, The device includes: The first acquisition module is used to acquire the wind speed statistics data set of the target wind field, as well as the load time history database, historical measured surface temperature dataset, and thermal structure coupled finite element model of the wind turbine blades in the target wind field throughout their entire life cycle. The load time history database includes the aerodynamic, gravity, centrifugal, and environmental loads of the wind turbine blades. The thermal structure coupled finite element model achieves mechanical and thermal continuity between the metal and composite materials through binding constraints. A construction module is used to construct a target two-dimensional thermoelastic stress analysis correction model adapted to the isotropic and anisotropic materials of the wind turbine blades, based on thermoelastic stress analysis theory and thermoelastic temperature dissipation effect, using the load time history database and the historical measured surface temperature dataset. The first calculation module is used to calculate the thermoelastic temperature change of the wind turbine blade surface using the target two-dimensional thermoelastic stress analysis correction model and the thermal structure coupled finite element model. The first module is used to establish a fatigue life curve that characterizes the relationship between the thermoelastic temperature change and the number of cycles, based on the principle of energy dissipation. The second calculation module is used to calculate the target total fatigue damage value of the wind turbine blade based on the Weibull distribution function, probability density function, and Miner linear cumulative damage criterion, using the wind speed statistics data set, the load time history database, and the fatigue life curve. The second module is used to establish a fatigue life prediction model for wind turbine blades under random loads based on the target total fatigue damage value.

8. A device for predicting the fatigue life of wind turbine blades, characterized in that, The device includes: The second acquisition module is used to acquire the real-time surface temperature dataset and the real-time dynamic load dataset of the wind turbine blade to be predicted. The prediction module is used to obtain the predicted life of the wind turbine blades by processing the real-time surface temperature dataset and the real-time dynamic load dataset through the wind turbine blade fatigue life prediction model, wherein the wind turbine blade fatigue life prediction model is obtained by the wind turbine blade fatigue life prediction model construction method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the wind turbine blade fatigue life prediction model construction method according to any one of claims 1 to 5, or the wind turbine blade fatigue life prediction method according to claim 6.

10. A computer program product, characterized in that, The method includes computer instructions for causing a computer to execute the wind turbine blade fatigue life prediction model construction method according to any one of claims 1 to 5, or the wind turbine blade fatigue life prediction method according to claim 6.

Images