A fan blade fatigue life prediction method, device, equipment and program product

Abstract

This invention relates to the field of wind turbine blade technology, and discloses a method, device, equipment, and program product for predicting the fatigue life of wind turbine blades. Based on a three-dimensional geometric model, this invention analyzes the stress distribution of the wind turbine blade under different wind speeds using transient dynamics, and generates a finite element model of the target wind turbine blade composite material containing different simulated damages, achieving accurate stress calculation under different operating conditions. By using modal analysis and cubic spline interpolation, two indicators are obtained: the strain modal change rate and direct indicators for damage location, improving the sensitivity and accuracy of identifying and locating minor damages. By using these two indicators combined with rainflow counting to generate a stress spectrum, the load characteristics of critical nodes can be accurately extracted. By combining a skewed semi-trapezoidal membership function and a low-amplitude load strengthening function constructed based on fuzzy mathematics theory, the calculated initial total fatigue damage value is corrected, eliminating stress ambiguity and load influence, significantly improving the accuracy of life prediction.

Description

Technical Field

[0001] This invention relates to the field of wind turbine blade technology, specifically to a method, device, equipment, and program product for predicting the fatigue life of wind turbine blades. Background Technology

[0002] Damage diagnosis of wind turbine blades can be achieved by arranging strain gauges, accelerometers, or fiber Bragg grating (FBG) sensors to collect signals such as strain and vibration, and by combining vibration modal analysis to monitor changes in parameters such as natural frequency and damping ratio to determine damage.

[0003] Traditional nondestructive testing techniques, such as ultrasonic testing and acoustic emission testing, are not sensitive enough to identify early micro-damage (such as cracks within 10% of the chord length) in composite blades, and rely on manual prediction of damage location, making it difficult to achieve global detection. Damage indicators based on displacement modes or natural frequencies do not respond significantly to changes in local blade stiffness, and are prone to misjudgment and missed detection. Summary of the Invention

[0004] This invention provides a method, device, equipment, and program product for predicting the fatigue life of wind turbine blades, in order to solve the problems of insufficient sensitivity of traditional non-destructive testing technology, reliance on manual prediction of damage location, difficulty in achieving global detection, and insignificant response to local stiffness changes of blades based on displacement modes or natural frequencies, which easily leads to misjudgment and missed detection.

[0005] In a first aspect, the present invention provides a method for predicting the fatigue life of wind turbine blades, the method comprising: A three-dimensional geometric model of the wind turbine blade to be predicted is obtained. Based on the three-dimensional geometric model, the stress distribution of the wind turbine blade under different wind speeds is analyzed using transient dynamics, and a finite element model of the target wind turbine blade composite material containing different simulated damages is generated. Based on the finite element model of the target wind turbine blade composite material, the strain modal change rate and direct indicators of damage location are obtained through modal analysis and cubic spline interpolation. Based on the strain modal change rate and direct indicators of damage location, the stress time history of different simulated damage hazard nodes in the finite element model of the target wind turbine blade composite material is processed using the rainflow counting method, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated. Based on the multi-condition comprehensive stress spectrum, the initial total fatigue damage value of the wind turbine blade to be predicted under different stress levels is calculated using the stress-life curve of fiberglass composite material and the Miner linear cumulative damage criterion. The initial total fatigue damage value is corrected using the target membership function and the low-amplitude load strengthening function, and the fatigue life prediction result of the wind turbine blade to be predicted is determined. The target membership function is a slanted semi-trapezoidal membership function constructed based on fuzzy mathematics theory.

[0006] The wind turbine blade fatigue life prediction method provided by this invention is based on a three-dimensional geometric model. It analyzes the stress distribution of the wind turbine blade under different wind speeds using transient dynamics and generates a finite element model of the target wind turbine blade composite material containing different simulated damages. This enables accurate stress calculation under different operating conditions and establishes a foundation for damage simulation. Furthermore, by using modal analysis and cubic spline interpolation, two indicators—the strain modal change rate and direct damage location indicators—are obtained, improving the sensitivity and accuracy of identifying and locating minor damages. Furthermore, by generating a stress spectrum using dual indicators combined with rainflow counting, the load characteristics of critical nodes can be accurately extracted, helping to support reliable damage calculation. Furthermore, by combining a skewed semi-trapezoidal membership function and a low-amplitude load strengthening function constructed based on fuzzy mathematics theory, the calculated initial total fatigue damage value is corrected, eliminating stress ambiguity and load influence, significantly improving the accuracy of life prediction. Therefore, by implementing this invention, a fully integrated calculation process from modeling and damage identification to life prediction is achieved, enabling accurate damage identification and significantly reducing prediction errors, making it suitable for practical engineering applications.

[0007] In one optional implementation, based on a three-dimensional geometric model, transient dynamics are used to analyze the stress distribution of the wind turbine blade under different wind speeds, and a finite element model of the target wind turbine blade composite material containing different simulated damages is generated, including: Based on the three-dimensional geometric model, the composite material of the wind turbine blade to be predicted is designed with layup and meshed to obtain the initial finite element model of the composite material of the wind turbine blade to be predicted; the stress distribution of the wind turbine blade to be predicted under different wind speeds is analyzed using transient dynamics, and multiple dangerous areas are identified in the initial finite element model of the composite material of the wind turbine blade to be predicted; different simulated damages are set in each dangerous area, and the target finite element model of the composite material of the wind turbine blade to be predicted containing different simulated damages is obtained.

[0008] The wind turbine blade fatigue life prediction method provided by this invention establishes a high-precision initial finite element model that closely matches the actual structure by performing layup design and mesh generation on the composite material of the wind turbine blade to be predicted. Furthermore, by analyzing the stress distribution of the wind turbine blade under different wind speeds through transient dynamics and identifying multiple critical areas, it is possible to accurately locate high-stress vulnerable parts and focus on key monitoring and analysis areas. Moreover, by setting different simulated damages in the critical areas, a realistic damage scenario is constructed, which helps support damage identification and life assessment.

[0009] In one optional implementation, based on the finite element model of the target wind turbine blade composite material, modal analysis and cubic spline interpolation are used to obtain the strain modal change rate and direct indicators of damage location, including: Displacement and strain modes were analyzed on the finite element model of the target wind turbine blade composite material, and the strain mode curve and strain mode change rate were determined. The strain mode curve was differentially calculated using cubic spline interpolation, and a direct index for damage localization was constructed.

[0010] The wind turbine blade fatigue life prediction method provided by this invention enhances the local strain abrupt response and improves the identification of minor damage by using displacement mode and strain mode analysis combined with cubic spline interpolation for differential calculation. Therefore, by implementing this invention and forming a dual-index damage identification system, the problem of traditional methods being insensitive to early minor damage is solved, and false positives and false negatives are reduced.

[0011] In one optional implementation, based on the strain modal change rate and direct indicators of damage location, the rainflow counting method is used to process the stress time history of different simulated damage-prone nodes in the finite element model of the target wind turbine blade composite material, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated, including: Based on the strain modal change rate and direct indicators of damage location, the damage location and degree of different simulated damages in the finite element model of the composite material of the wind turbine blade to be predicted are identified; multiple simulated damage hazard nodes are determined according to the damage location; the stress time history of multiple simulated damage hazard nodes is processed by the rainflow counting method, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated.

[0012] The wind turbine blade fatigue life prediction method provided by this invention identifies the damage location and degree of different simulated damages by combining strain modal change rate and direct damage location indicators, achieving precise damage localization and quantitative assessment. Furthermore, it can accurately pinpoint multiple simulated damage hazard nodes based on the damage location. Further, it processes the stress time history of multiple simulated damage hazard nodes using the rainflow counting method and generates a multi-condition comprehensive stress spectrum for the wind turbine blade to be predicted, achieving standardized processing of the load time history and providing accurate input for subsequent fatigue calculations. Therefore, by implementing this invention, combining damage identification results with stress load analysis, it provides accurate and reliable stress data support for life prediction.

[0013] In one optional implementation, based on a multi-condition comprehensive stress spectrum, the initial total fatigue damage value of the wind turbine blade under different stress levels is calculated using the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion, including: Based on the multi-condition comprehensive stress spectrum, the stress-life curve of fiberglass composite material and Miner's linear cumulative damage criterion are used to calculate multiple basic fatigue damage values ​​of the wind turbine blade under different stress levels; based on the multiple basic fatigue damage values, the initial total fatigue damage value of the wind turbine blade is determined.

[0014] The wind turbine blade fatigue life prediction method provided by this invention can obtain the component damage contribution under each stress level by calculating the basic fatigue damage value. Furthermore, by summing multiple basic fatigue damage values, an initial total fatigue damage value is obtained, realizing a preliminary assessment of the uncorrected overall damage.

[0015] In one optional implementation, the initial total fatigue damage value is corrected using the target membership function and the low-amplitude load strengthening function, and the fatigue life prediction result of the wind turbine blade to be predicted is determined, including: Based on the initial total fatigue damage value, the total damage degree membership value of the wind turbine blade to be predicted is obtained after processing with the target membership function; based on the total damage degree membership value, the low-amplitude load strengthening function and the load interaction effect, the corrected target total fatigue damage value is obtained after processing with the fuzzy damage accumulation model; based on the target total fatigue damage value, the fatigue life prediction result of the wind turbine blade to be predicted is determined.

[0016] The wind turbine blade fatigue life prediction method provided by this invention obtains the total membership value through the target membership function, quantifies the fuzzy damage under low-amplitude loads, and characterizes the fuzzy characteristics of the damage. Furthermore, the initial total fatigue damage value is corrected by strengthening functions, load interaction effects, and a fuzzy damage accumulation model, compensating for the shortcomings of traditional criteria, improving the accuracy of damage calculation, and ultimately outputting high-precision life results that conform to actual working conditions.

[0017] In a second aspect, the present invention provides a device for predicting the fatigue life of wind turbine blades, the device comprising: The system comprises three modules: an acquisition module for acquiring a three-dimensional geometric model of the wind turbine blade to be predicted; an analysis module for analyzing the stress distribution of the wind turbine blade under different wind speeds using transient dynamics based on the three-dimensional geometric model, and generating a finite element model of the target wind turbine blade composite material containing different simulated damages; a first processing module for obtaining the strain modal change rate and direct damage location indicators based on the finite element model of the target wind turbine blade composite material through modal analysis and cubic spline interpolation; and a second processing module for analyzing the target wind turbine blade composite material using rainflow counting based on the strain modal change rate and direct damage location indicators. The stress-time history of different simulated damage-prone nodes in the finite element model of the material is processed to generate a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted. The calculation module is used to calculate the initial total fatigue damage value of the wind turbine blade to be predicted under different stress levels based on the multi-condition comprehensive stress spectrum, using the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion. The correction module is used to correct the initial total fatigue damage value using the target membership function and the low-amplitude load strengthening function, and to determine the fatigue life prediction result of the wind turbine blade to be predicted. The target membership function is a slanted semi-trapezoidal membership function constructed based on fuzzy mathematics theory.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine blade fatigue life prediction method of the first aspect or any corresponding embodiment described above.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the wind turbine blade fatigue life prediction method of the first aspect or any corresponding embodiment described above.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the wind turbine blade fatigue life prediction method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0021] 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.

[0022] 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 predicting the fatigue life of wind turbine blades according to an embodiment of the present invention. Figure 3 This is a structural block diagram of a wind turbine blade fatigue life prediction device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] 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.

[0026] As an optional application scenario of this invention, considering the specific application environment architecture or specific hardware architecture upon which the wind turbine blade fatigue life prediction method depends, the specific application environment architecture or specific hardware architecture is described here. 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.

[0027] 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.

[0028] This invention provides a method for predicting the fatigue life of wind turbine blades. By integrating the entire process from modeling and damage identification to life prediction, it achieves accurate damage identification and significantly reduces prediction errors, making it suitable for practical engineering applications.

[0029] According to an embodiment of the present invention, a method for predicting the fatigue life of 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.

[0030] 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 2 This is a flowchart of a wind turbine blade fatigue life prediction method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the three-dimensional geometric model of the wind turbine blade to be predicted.

[0031] In one optional embodiment, the three-dimensional geometric model represents a three-dimensional solid geometric model created using SolidWorks that is completely consistent with the shape, size, and structural form of the actual large wind turbine blade. It is used to fully characterize the overall outline, root, maximum chord length, leading / trailing edge, tip, and other real geometric structural features of the wind turbine blade.

[0032] Step S202: Based on the three-dimensional geometric model, the stress distribution of the wind turbine blade to be predicted under different wind speeds is analyzed using transient dynamics, and a finite element model of the target wind turbine blade composite material containing different simulated damage is generated.

[0033] In one optional embodiment, transient dynamics representation (also known as time history analysis or time-domain dynamics analysis) represents a mechanical analysis method for studying the dynamic response (such as displacement, velocity, acceleration, stress, strain, etc.) of a structure under time-varying loads. In this embodiment, it is used to analyze the dynamic structural response of wind turbine blades under time-varying aerodynamic loads.

[0034] In one optional embodiment, different simulated damages represent various structural damages that may occur in actual engineering, set at vulnerable parts of the blade, and are used to simulate the structural state of the blade after damage.

[0035] In one optional embodiment, the target wind turbine blade composite material finite element model is represented by a high-precision finite element model based on a three-dimensional geometric model, after completing composite material layup, mesh generation, dangerous area definition, and simulated damage embedding.

[0036] In one optional embodiment, based on the real geometric and composite material structural characteristics represented by the three-dimensional geometric model of the wind turbine blade, the actual stress state of the blade under variable wind speed load is reconstructed through transient dynamic response calculation, and high-stress danger areas are identified. Furthermore, by embedding various simulated damages into the high-stress danger areas to equivalently deteriorate the real structure, a finite element model of the target wind turbine blade composite material that accurately reflects the health and damage states can ultimately be constructed.

[0037] Step S203: Based on the finite element model of the target wind turbine blade composite material, modal analysis and cubic spline interpolation are used to obtain the strain modal change rate and direct indicators of damage location.

[0038] In an optional embodiment, modal analysis refers to the vibration characteristic analysis of a finite element model of a damaged wind turbine blade, which can obtain displacement modes and strain modes, and thus characterize changes in structural stiffness and modal characteristics caused by damage.

[0039] In one alternative embodiment, cubic spline interpolation represents a smooth curve passing through a series of shape points. Mathematically, it is the process of obtaining a set of curve functions by solving a system of three bending moment equations. In this embodiment, it is used to perform smooth fitting processing on discrete strain modal data and generate continuous and smooth strain modal curves.

[0040] In an optional embodiment, the strain mode change rate represents a damage sensitivity index formed by comparing the difference in strain mode amplitudes between the healthy state and the damaged state, which is used to reflect the degree of influence of damage on the structural modes.

[0041] In one alternative embodiment, the Strain Mode Shape Difference (ISMSD) represents a structural damage localization method based on the principle of strain mode difference. It can identify possible damage locations in a structure by using only strain mode data after damage, without relying on prior information from the intact state of the structure.

[0042] In one alternative embodiment, a composite material finite element model containing simulated damage is used as the object. By utilizing the property that damage changes the strain mode distribution of the structure, strain mode features are extracted through modal analysis.

[0043] Furthermore, discrete data errors are eliminated through cubic spline interpolation, and local strain mutation signals are enhanced through differential interpolation, ultimately forming a highly sensitive dual-index system, namely strain modal change rate and direct damage location index, thus achieving accurate characterization of damage.

[0044] Step S204: Based on the strain modal change rate and direct indicators of damage location, the stress time history of different simulated damage-prone nodes in the finite element model of the target wind turbine blade composite material is processed using the rainflow counting method, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated.

[0045] 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.

[0046] In one alternative embodiment, different simulated damage hazard nodes represent key calculation nodes of the finite element model located within the simulated damage region, which have high stress levels and are prone to fatigue failure.

[0047] In one alternative embodiment, the stress time history represents a complete response data sequence of stress continuously changing over time at a critical node under different wind speeds and dynamic loads.

[0048] In one optional embodiment, the multi-condition integrated stress spectrum represents a complete stress load dataset for fatigue calculation, formed by uniformly statistically and weightedly integrating the stress cycle characteristics under various wind speed conditions.

[0049] In one alternative embodiment, damage can significantly alter the local stress distribution and response patterns. Therefore, based on the dual indicators of damage identification, stress time series data of critical nodes are extracted, and key fatigue load features are extracted using the rainflow counting method. Then, the stress spectrum can be weighted and fused according to the proportion of actual working conditions, which can ultimately form a stress spectrum that can truly reflect the service load state of the blade, i.e., a multi-working-condition comprehensive stress spectrum.

[0050] Step S205: Based on the multi-condition comprehensive stress spectrum, the initial total fatigue damage value of the wind turbine blade to be predicted under different stress levels is calculated using the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion.

[0051] In an optional embodiment, the stress-life curve (FRP SN curve) of the FRP composite material represents the characteristic curve of the relationship between the FRP composite material used in the wind turbine blade under different stress levels S and the corresponding fatigue life N.

[0052] 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.

[0053] In one optional embodiment, the multi-condition comprehensive stress spectrum is used as input. Based on the fatigue life corresponding to different stress levels, the stress life curve of fiberglass composite material and Miner linear cumulative damage criterion are used to accumulate the damage caused by each stress cycle, thereby obtaining the overall initial fatigue damage degree of the blade, i.e. the initial total fatigue damage value.

[0054] Step S206: The initial total fatigue damage value is corrected using the target membership function and the low-amplitude load strengthening function, and the fatigue life prediction result of the wind turbine blade to be predicted is determined.

[0055] In an optional embodiment, the target membership function is a slanted semi-trapezoidal membership function constructed based on fuzzy mathematics theory, which is used to quantify the stress fuzziness caused by low-amplitude loads, thereby objectively characterizing the fuzzy properties of damage.

[0056] In one optional embodiment, the low-amplitude load strengthening function represents a correction function constructed based on the cyclic strengthening characteristics of the material under low-amplitude loads. It is used to compensate for the low-amplitude load strengthening effect ignored by the traditional Miner criterion and to correct the damage calculation deviation under low stress levels.

[0057] In one optional embodiment, based on the initial total fatigue damage value, fuzzy mathematics is used to process the fuzziness of stress and damage. The traditional linear accumulation rule is corrected by low-amplitude load strengthening and load interaction effect, which can eliminate the systematic error of the original model and make the fatigue damage calculation more in line with the actual service state, thereby outputting high-precision fatigue life prediction results for wind turbine blades.

[0058] The wind turbine blade fatigue life prediction method provided in this embodiment is based on a three-dimensional geometric model. It analyzes the stress distribution of the wind turbine blade under different wind speeds using transient dynamics and generates a finite element model of the target wind turbine blade composite material containing different simulated damages. This enables accurate stress calculation under different operating conditions and establishes a foundation for damage simulation. Furthermore, by using modal analysis and cubic spline interpolation, two indicators—the strain modal change rate and direct damage location indicators—are obtained, improving the sensitivity and accuracy of identifying minute damages. Furthermore, by generating a stress spectrum using dual indicators combined with rainflow counting, the load characteristics of critical nodes can be accurately extracted, helping to support reliable damage calculation. Furthermore, by combining a skewed semi-trapezoidal membership function and a low-amplitude load strengthening function constructed based on fuzzy mathematics theory, the calculated initial total fatigue damage value is corrected, eliminating stress ambiguity and load influence, significantly improving the accuracy of life prediction. Therefore, by implementing this invention, a fully integrated calculation process from modeling and damage identification to life prediction is achieved, enabling accurate damage identification and significantly reducing prediction errors, making it suitable for practical engineering applications.

[0059] In some optional implementations, step S202 above includes: Step S2021: Based on the three-dimensional geometric model, the composite material of the wind turbine blade to be predicted is designed with layup and meshed to obtain the initial finite element model of the composite material of the wind turbine blade to be predicted.

[0060] In one optional embodiment, based on the three-dimensional geometric model of the wind turbine blade, the composite material layup is defined and the mesh is discretized according to the actual blade design parameters, thereby enabling the construction of a non-destructive, high-precision initial finite element model of the wind turbine blade composite material that can be used for mechanical simulation.

[0061] The continuous blade structure is discretized into finite elements, and the mechanical properties of the composite material are restored by matching the actual ply angle, ply number, and material properties. Furthermore, by reasonably controlling the mesh density, the accuracy of strain and stress calculations can be guaranteed, and the smoothing of minor damage signals can be avoided, thereby ensuring that the structural response is consistent with reality.

[0062] For example, a three-dimensional geometric model can be imported into ANSYS Workbench. Then, composite material layup design is performed, with the layup angle and number of layups strictly matching the actual blade design to ensure that the strain mode distribution is not distorted.

[0063] Furthermore, mesh generation is performed. Specifically, the mesh can be refined in the damaged area to prevent strain abrupt changes at microcracks from being smoothed out; a reasonable mesh density is used in non-critical areas to balance computational efficiency and accuracy.

[0064] Furthermore, the material properties and element types are defined, and an initial finite element model of the wind turbine blade composite material without any simulated damage is generated.

[0065] Step S2022: Analyze the stress distribution of the wind turbine blade under different wind speeds using transient dynamics, and identify multiple hazardous areas in the initial finite element model of the composite material of the wind turbine blade.

[0066] In one optional embodiment, the wind turbine blades exhibit a time-varying dynamic response under varying wind speed aerodynamic loads, and transient dynamics can accurately capture stress peaks and time-history changes. Therefore, based on the initial finite element model, the transient dynamics method is used to calculate the dynamic stress distribution of the blades under different wind speed conditions, thereby locating dangerous areas with high stress, stress concentration, and susceptibility to fatigue failure.

[0067] For example, firstly, boundary conditions are set: the blade root connection is fixed (e.g., displacement and rotation are constrained) to simulate the actual installation state; air damping or structural damping is set to avoid dynamic response divergence. Air damping is the viscous damping generated by the relative motion between the blade and the air, such as air friction on the skin surface and airflow separation damping; structural damping is the core damping source for all wind turbine blades in the simulation. In this embodiment, Rayleigh Damping is used, controlled by the mass damping coefficient and stiffness damping coefficient.

[0068] Furthermore, by constraining all degrees of freedom at the blade root connection, the rigid bolt connection between the blade and the hub is simulated to ensure that the load transfer path is consistent with reality.

[0069] Then, loads are applied, that is, the corresponding aerodynamic load time histories are applied to the blade model according to different wind speeds and operating conditions. The operating conditions can include: low wind speed 6-8m / s stable load with no obvious fluctuations; rated wind speed 10-12m / s with small fluctuations of ±5%; high wind speed 14-16m / s with large fluctuations of ±10%.

[0070] Furthermore, if the location where the load is applied does not match the actual area of ​​action of the aerodynamic load, it may lead to a decrease in structural performance, failure, or other potential problems. Therefore, during the design or application process, the applied loads (whether external or aerodynamic loads) should be applied precisely to the areas of the structure intended to bear these loads; that is, the location of the load application must be consistent with the actual area of ​​action of the aerodynamic load.

[0071] Furthermore, a transient dynamics solver is selected and the corresponding solution parameters are set for solving the problem. These solution parameters may include: setting an appropriate time step, which must be less than 1 / 10 of the load variation period to ensure the capture of dynamic peak values; and defining the solution termination time, i.e., covering the complete cycle of the load time history.

[0072] Furthermore, after the solution is completed, the Mises equivalent stress and principal stress distribution are extracted to generate stress cloud diagrams and critical path stress time history curves. Based on the magnitude and concentration of stress, dangerous areas such as blade root transition fillets, leading / trailing edge reinforcement zones, bolt connection hole perimeters, aerodynamic load concentration zones in the blade, and the location of the maximum chord length are located.

[0073] Step S2023: Set different simulated damages in each hazardous area and obtain a finite element model of the target wind turbine blade composite material containing different simulated damages.

[0074] In one optional embodiment, by simulating damage equivalent to the structural degradation state of a real blade, the local stiffness and strain mode distribution can be altered, allowing the finite element model to reproduce the mechanical properties after damage. Therefore, in this embodiment, within the defined hazardous area, by setting different forms and degrees of simulated damage equivalent to the structural degradation state of a real blade, the local stiffness and strain mode distribution can be altered, thereby enabling the constructed final target wind turbine blade composite material finite element model to reproduce the mechanical properties after damage.

[0075] In an optional embodiment, simulated damages, such as transverse cracks and delamination, are set in each hazardous area, covering different early minor damage and multi-damage conditions within 10% of the chord length.

[0076] Furthermore, the node numbers, mesh generation, and modal orders of the damage model and the healthy state model are strictly matched to prevent calculation errors caused by spatial misalignment or modal order misalignment. This completes the parameter definition and model assembly for the simulated damage, and retains all boundary conditions, load settings, and material properties consistent with the initial model. Ultimately, this generates a finite element model of the target wind turbine blade composite material containing different simulated damages, which can be directly used for displacement modal analysis and strain modal analysis.

[0077] In some optional implementations, step S203 above includes: Step S2031: Perform displacement mode and strain mode analysis on the finite element model of the target wind turbine blade composite material, and determine the strain mode curve and strain mode change rate.

[0078] In one optional embodiment, displacement mode and strain mode analysis are performed on the target finite element model containing simulated damage, and strain mode is converted by derivative of displacement mode to obtain strain mode curves. Then, by comparing the amplitudes of strain modes of the same order and at the same location under healthy and damaged states, the strain mode change rate SR, which is used to quantify the impact of damage on structural modes, can be obtained.

[0079] Damage alters local stiffness, which in turn changes the strain mode distribution. Therefore, when the strain modes cannot be directly output, they can be obtained by differentiating the displacement modes with respect to position.

[0080] For example, displacement modal analysis and strain modal analysis are performed on the target finite element model, and then data alignment is performed to ensure that the node numbers, mesh generation, and modal orders of the healthy model and the damaged model are strictly consistent, thus avoiding misalignment errors.

[0081] Furthermore, if the strain mode is output directly, it can be extracted directly; if only the displacement mode is output, the strain mode can be converted from the displacement mode according to the following relationship (1): (1) In the formula: Indicates the first First strain mode at position The amplitude at that point; Indicates the first First strain mode at position The spanwise displacement amplitude at that location.

[0082] Furthermore, strain modal curves can be generated based on the conversion / extraction results.

[0083] Furthermore, by comparing the strain mode amplitudes at the same order and location under healthy and damaged conditions... It is possible to calculate the corresponding strain mode change rate SR.

[0084] Step S2032: Perform differential calculations on the strain modal curves using cubic spline interpolation and construct direct indicators for damage localization.

[0085] In one alternative embodiment, discrete measurement points cannot capture abrupt changes in local strain; cubic spline interpolation can encrypt data and smooth curves; and differencing can amplify changes in modal curvature. Therefore, by performing cubic spline interpolation to encrypt and smooth the discrete strain modal curves, and then performing first- or second-order differencing, the direct damage localization index ISMSD can be finally constructed and calculated.

[0086] For example, based on strain modal curves, strain modal data of healthy and damaged states are obtained and arranged according to the same measurement point location, while abnormal noise points are removed to ensure data correspondence.

[0087] Furthermore, a cubic spline interpolation function is constructed with the measurement point location as the independent variable and the strain mode value as the dependent variable, generating dense interpolation points between adjacent measurement points to obtain a smooth and continuous strain mode curve.

[0088] Furthermore, the interpolated strain modal curves are subjected to first-order difference. or second-order difference The difference sequence between the healthy state and the damaged state is obtained. The difference step size is consistent with the interpolation point interval.

[0089] Furthermore, the difference sequences between the damaged state and the healthy state are processed by difference, and the absolute value is taken and normalized to obtain the ISMSD index, as shown in the following relationship (2): (2) In the formula: Indicates position The direct indicator value for locating the damage at the site, with a value range of [value range missing]. The larger the value, the higher the probability of damage at that location; the peak value indicates the location of the damage. The coordinates of the measuring points along the spanwise (or chordwise) direction of the wind turbine blade are used to characterize different monitoring points along the blade length direction. This indicates the modal order involved in the calculation, typically the first 8-12 orders, to balance sensitivity and anti-interference capability; Indicates the first The weighting coefficients of each mode are allocated according to the proportion of strain energy in the mode. The greater the strain energy, the higher the weight. For example, the weight of the bending mode is higher than that of the torsional mode, which is used to highlight the contribution of the mode that is more sensitive to damage. Indicates the first Position in the first mode The strain mode difference sequence at the location is obtained by performing difference operations on the strain mode curve after cubic spline interpolation, and is used to amplify the strain mode anomaly caused by a sudden change in local stiffness. Indicates the first Position in the first mode The strain mode change rate at the point is obtained by comparing the strain mode amplitudes in the healthy state and the damaged state, and is used to quantify the degree of influence of damage on the structural modes. Indicates the first Position in the first mode The product of the differential strain and the strain modal change rate at the point integrates the dual damage characteristics of local strain abrupt change and modal amplitude change; Indicates the first Under the first mode, the positions of all measurement points The maximum value is used for normalization to make The value range is [0, 1], which facilitates weighted summation of multiple modes.

[0090] In some optional implementations, step S204 above includes: Step S2041: Based on the strain modal change rate and direct indicators of damage location, identify the damage location and degree of different simulated damages in the finite element model of the composite material of the target wind turbine blade to be predicted.

[0091] In one alternative embodiment, damage can cause a decrease in the local stiffness of the structure, resulting in a significant abrupt change in the strain mode amplitude and curve curvature. The strain mode change rate (SR) reflects the degree of influence of the damage on the mode amplitude, while the ISMSD amplifies the signal of the abrupt change in local strain. Therefore, by using a dual-index system consisting of the strain mode change rate (SR) and the direct indicator of damage location (ISMSD), damage can be determined in a finite element model containing simulated damage. This can identify the specific location and severity of various simulated damages in the model, thereby enabling accurate identification of early minor damage and multi-damage conditions.

[0092] For example, the strain mode change rate (SR) can be used as a criterion for determining the degree of damage. Specifically, the amplitudes of the same strain mode at the same location are compared between healthy and damaged states. The greater the amplitude change, the higher the SR value, and the more severe the corresponding damage.

[0093] Furthermore, the direct indicator of damage location, ISMSD, is used as the basis for determining the damage location. Specifically, the distribution curve of ISMSD along the blade position is plotted, and the coordinate position corresponding to the peak of the curve is the damage location.

[0094] Furthermore, a threshold of 1.5 to 2 times the maximum ISMSD value under healthy conditions is used as the judgment threshold. Signals above the threshold are considered true damage signals, while those below the threshold are considered noise or spurious signals and are excluded. Further, the SR and ISMSD results are combined, and the location coordinates and damage level / degree of each simulated injury are output.

[0095] Step S2042: Determine multiple simulated damage hazard nodes based on the damage location.

[0096] In one optional embodiment, the damage location corresponds to the region with the highest stress level and the largest stress gradient in the finite element model. Nodes in this region are most prone to fatigue damage. Therefore, selecting the critical node with the damage location as the core can ensure that the stress time history data is the most representative, thereby making the fatigue calculation more consistent with the actual damage and failure law.

[0097] For example, based on all identified damage locations, the coordinate regions in the finite element model of the target wind turbine blade composite material are matched. Then, in the element where each damage location is located and the surrounding stress concentration region, stress peak nodes, curvature abrupt change nodes, and mesh critical nodes are selected as candidate hazardous nodes.

[0098] Furthermore, invalid nodes with stable stress and no load response can be eliminated, while nodes with significant stress changes and sensitivity to damage can be retained, thus forming multiple corresponding simulated damage-prone nodes.

[0099] Furthermore, the number, coordinates, and unit to which each dangerous node belongs can be recorded to ensure complete alignment with the stress-time history data of the finite element model of the target wind turbine blade composite material, thus preparing for subsequent extraction of stress-time history.

[0100] Step S2043: The stress time history of multiple simulated damage-prone nodes is processed using the rainflow counting method, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated.

[0101] In one optional embodiment, the stress-time history data of each critical node under different wind speed conditions are statistically analyzed using the rainflow counting method, and fatigue load characteristics such as stress amplitude, average stress, and number of cycles are extracted. Then, the data are weighted and fused according to the proportion of each working condition to obtain a multi-condition comprehensive stress spectrum that can be directly used for fatigue damage calculation.

[0102] For example, stress time-series data for each critical node under three working conditions—low wind speed (6-8 m / s), rated wind speed (10-12 m / s), and high wind speed (14-16 m / s)—can be derived from the finite element results. This data may include time, node number, and stress value. Furthermore, all peak and valley values ​​in the stress time history are extracted and arranged chronologically to form a peak-valley stress sequence. .

[0103] Furthermore, starting from the first peak of the sequence, the stress is traversed sequentially from high stress to low stress, and the stress amplitude and average stress are calculated, as shown in the following equations (3) and (4): (3) (4) In the formula: It represents the stress amplitude (alternating stress amplitude), which is the half-amplitude of stress change in one fatigue stress cycle. It is an indicator for measuring the degree of load alternation. It represents the average stress, which is the central level of stress in a fatigue stress cycle and reflects the static stress components of the cyclic load. This represents the peak stress, which is the maximum stress value in a complete stress cycle (positive for tensile stress and negative for compressive stress). This represents the stress valley value, which is the minimum stress value in a complete stress cycle (positive for tensile stress and negative for compressive stress).

[0104] Furthermore, the integrity of the cycle is determined: if the subsequent peak value is less than the previous initial peak value, it is determined to be a complete cycle, and the stress amplitude, average stress, and cycle number N=1 are recorded; otherwise, it is an incomplete cycle, which is temporarily stored and merged with the subsequent sequence.

[0105] Furthermore, remove the already counted intermediate points and repeat the above logic until all peak-valley data have been counted. Then, classify by stress amplitude and count the total number of cycles corresponding to each stress level under each working condition. Furthermore, by weighting the stress spectrum according to the actual operating time proportion of each wind speed condition, and summing the number of cycles for the same stress amplitude under different conditions, a multi-condition comprehensive stress spectrum is obtained, which can include stress amplitude. Mean stress Total number of loops .

[0106] In one example, taking the stress time history of a critical node as an example, the peak-valley sequence is extracted as follows: .in, ; ; ; ; ; .

[0107] Furthermore, select the first peak of the sequence. Used as the starting point for counting.

[0108] Furthermore, from Flow direction Calculate stress amplitude With average stress; .

[0109] Furthermore, the flow to ,because Then determine - - To complete the loop, and record the results: , Number of loops .

[0110] Furthermore, remove the intermediate point. Update the sequence to: .

[0111] Furthermore, from Flow direction Calculate stress amplitude With average stress; .

[0112] Furthermore, the flow to ,because If the current segment does not form a complete loop, the flow continues to the next segment. and calculate With average stress; .

[0113] Furthermore, because Then - - - Disassembled into: 1. Complete loop: - - And record: , Number of loops ; 2. Semi-circulation - It is then merged with adjacent sequences.

[0114] Furthermore, repeat the above process until all peak-valley sequences have been counted, ultimately obtaining all complete cycles. Mean stress Total number of loops .

[0115] In some optional implementations, step S205 above includes: Step S2051: Based on the multi-condition comprehensive stress spectrum, the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion are used to calculate the basic fatigue damage values ​​of the wind turbine blade to be predicted under different stress levels.

[0116] In one optional embodiment, the fatigue life corresponding to each stress amplitude is determined by using the multi-condition comprehensive stress spectrum as input and the SN stress-life curve of the fiberglass composite material. Then, according to the Miner linear cumulative damage criterion, the basic fatigue damage value caused by each stress level is calculated separately, thereby obtaining the component contribution of each stress level to the fatigue damage of the blade.

[0117] For example, read the comprehensive stress spectrum under multiple working conditions to obtain the stress amplitude corresponding to each stress level. Total number of loops Then, for each stress amplitude You can look up the corresponding fatigue life using the SN stress-life curve of fiberglass composite materials. .

[0118] Furthermore, using the Miner linear cumulative damage criterion, the basic fatigue damage values ​​of the wind turbine blades to be predicted under different stress levels are calculated, as shown in the following equation (5): (5) In the formula: Indicates the first The fatigue damage value of the foundation corresponding to the stress level.

[0119] Step S2052: Determine the initial total fatigue damage value of the wind turbine blade to be predicted based on multiple basic fatigue damage values.

[0120] In an optional embodiment, all basic damage values ​​are summed according to Miner's linear accumulation rule to obtain the initial total fatigue damage value. The following relation (6) is shown: (6) In the formula: This indicates the total number of stress levels in the multi-condition integrated stress spectrum.

[0121] In some optional implementations, step S206 above includes: Step S2061: Based on the initial total fatigue damage value, the total damage degree membership value of the wind turbine blade to be predicted is obtained after processing by the target membership function.

[0122] In one optional embodiment, the actual fatigue damage is fuzzy, and traditional deterministic calculations cannot describe the fuzzy boundaries of slight, moderate, and severe damage. In this embodiment, the deterministic total damage can be transformed into the membership degree of the interval [0, 1] by using a slanted semi-trapezoidal membership function, which can then objectively reflect the fuzzy characteristics of the severity of the damage.

[0123] In an alternative embodiment, using the uncorrected initial total fatigue damage value Using fuzzy mathematics as input, a semi-large semi-trapezoidal membership function is constructed for mapping calculation, and the total damage degree membership value is output, thereby realizing the quantitative characterization of fuzzy damage caused by low amplitude load.

[0124] For example, firstly, a blur damage threshold parameter can be set, which may include: minimum damage. That is, the lower limit of damage with a membership degree of 0; critical damage. That is, the intermediate threshold with a membership degree of 0.5; failure damage That is, the lower limit of failure with a membership degree of 1, which is usually taken as .

[0125] Furthermore, the membership value of the total damage degree is calculated using a skewed semi-trapezoidal membership function, as shown in the following equation (7): (7) In the formula: This represents the membership value of the total degree of damage.

[0126] Step S2062: Based on the total damage degree membership value, the low-amplitude load strengthening function, and the load interaction effect, the corrected target total fatigue damage value is obtained after processing by the fuzzy damage accumulation model.

[0127] In an optional embodiment, the load interaction effect refers to the phenomenon that when wind turbine blades are subjected to alternating high and low stresses with random amplitude fatigue loads during service, the loading sequence of different stress levels and the interaction between adjacent stresses will lead to a significant difference between the actual fatigue damage degree and the traditional Miner linear superposition result. This can include two typical cases: 1. High stress amplitude → Low stress amplitude: Low-amplitude loads will be affected by the damage caused by previous high-amplitude loads, and fatigue damage will be aggravated. 2. Low stress amplitude → high stress amplitude: Low-amplitude loads will have a certain strengthening effect on materials, thus suppressing fatigue damage caused by subsequent high-amplitude loads.

[0128] In one alternative embodiment, the traditional Miner ignores low-amplitude load enhancement, load interaction, and stress ambiguity. Therefore, in this embodiment, the damage weight of the low-stress segment is corrected by the low-amplitude load enhancement function, the influence of loading sequence is corrected by the load interaction factor, and the fuzzy membership degree is combined to achieve more realistic damage accumulation calculation, which greatly reduces the prediction error.

[0129] In an optional embodiment, after inputting the total damage degree membership value, the low-amplitude load strengthening function, and the load interaction effect into the fuzzy damage accumulation model, the model quantifies the fuzziness of stress and damage with a skewed semi-trapezoidal membership function, compensates for the material strengthening characteristics with a low-amplitude load strengthening function, and considers the influence of the high and low stress loading sequence with a load interaction factor. The model performs nonlinear, non-independent, and fuzzy weighted accumulation of fatigue damage at each stress level, and finally obtains a total fatigue damage value that is more consistent with the actual service state of the wind turbine blade.

[0130] For example, by introducing the material strengthening coefficient and the attenuation coefficient, and constructing a low-amplitude load strengthening function, the following relationship (8) is shown: (8) In the formula: This represents the value of the low-amplitude load strengthening function, used to correct the fatigue damage weight under low stress levels. This indicates a low-amplitude stress level, referring to the lower stress amplitude that occurs during the operation of the wind turbine blades; This indicates the number of cycles corresponding to the low-amplitude stress, that is, the number of load cycles at this low stress level; It represents the material strengthening coefficient, which is related to the low-amplitude stress level and reflects the degree of strengthening of the material under low-amplitude loads; This represents the attenuation coefficient, which controls the rate at which the enhancement effect decays with increasing cycle number and is determined experimentally.

[0131] Furthermore, a load interaction factor is introduced. To characterize the effect of high and low stress loading sequence: (1) High amplitude followed by low amplitude: The damage worsened; (2) Low amplitude followed by high amplitude: This strengthens the inhibition of damage.

[0132] Furthermore, the membership value of the total damage degree Low-amplitude load strengthening function value and load interaction effect Input the fuzzy damage accumulation model, calculate the stress-corrected damage at each level, and sum them to obtain the corrected target total fatigue damage value, as shown in the following relationship (9): (9) In the formula: This represents the corrected target total fatigue damage value; Indicates the first Fatigue life of fiberglass composite materials under stress amplitude; This represents the initial total fatigue damage value.

[0133] Step S2063: Based on the target total fatigue damage value, determine the fatigue life prediction result of the wind turbine blade to be predicted.

[0134] In an optional embodiment, the corrected target total fatigue damage value has eliminated the errors caused by low-amplitude strengthening, load interaction, and stress ambiguity. Therefore, when the target total fatigue damage value reaches the failure threshold (usually 1), the corresponding service time is the fatigue life of the blade.

[0135] In one optional embodiment, based on the corrected target total fatigue damage value, and combined with material fatigue characteristics and load conditions, the final fatigue life prediction result of the wind turbine blade can be obtained by inversion calculation.

[0136] For example, the criterion for judging blade fatigue failure is set as follows: It is determined at this time that the fatigue life has been reached.

[0137] Furthermore, by combining stress spectrum under multiple operating conditions, wind speed distribution, and operating time ratio, the remaining service time / total fatigue life of the blade from the initial state to the damage threshold can be calculated. Combined with wind turbine operating parameters such as wind speed, rotational speed, and operating time, the final fatigue life prediction result can be obtained.

[0138] Furthermore, the prediction accuracy can be dynamically adjusted based on wind field environmental parameters such as turbulence intensity and wind speed distribution, and a lifespan conclusion adapted to the actual engineering situation can be formed.

[0139] 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.

[0140] This embodiment provides a device for predicting the fatigue life of wind turbine blades, such as... Figure 3 As shown, the device includes: The acquisition module 301 is used to acquire the three-dimensional geometric model of the wind turbine blade to be predicted.

[0141] Analysis module 302 is used to analyze the stress distribution of the wind turbine blade under different wind speeds based on a three-dimensional geometric model and transient dynamics, and to generate a finite element model of the target wind turbine blade composite material containing different simulated damages.

[0142] The first processing module 303 is used to obtain the strain modal change rate and direct indicators of damage location based on the finite element model of the target wind turbine blade composite material, through modal analysis and cubic spline interpolation.

[0143] The second processing module 304 is used to process the stress time history of different simulated damage-prone nodes in the finite element model of the target wind turbine blade composite material based on the strain modal change rate and direct indicators of damage location, and to generate a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted.

[0144] The calculation module 305 is used to calculate the initial total fatigue damage value of the wind turbine blade under different stress levels based on the multi-condition comprehensive stress spectrum, the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion.

[0145] The correction module 306 is used to correct the initial total fatigue damage value using the target membership function and the low-amplitude load strengthening function, and to determine the fatigue life prediction result of the wind turbine blade to be predicted. The target membership function is a semi-large trapezoidal membership function constructed based on fuzzy mathematics theory.

[0146] In some alternative implementations, the analysis module 302 includes: The element division is used to perform layup design and mesh generation of the composite material of the wind turbine blade to be predicted based on the three-dimensional geometric model, so as to obtain the initial finite element model of the composite material of the wind turbine blade to be predicted.

[0147] The first analysis unit is used to analyze the stress distribution of the wind turbine blade under different wind speeds using transient dynamics, and to identify multiple dangerous areas in the initial finite element model of the composite material of the wind turbine blade.

[0148] The setting unit is used to set different simulated damages in each hazardous area and obtain a finite element model of the target wind turbine blade composite material containing different simulated damages.

[0149] In some alternative implementations, the first processing module 303 includes: The second analysis unit is used to perform displacement mode and strain mode analysis on the finite element model of the target wind turbine blade composite material, and to determine the strain mode curve and strain mode change rate.

[0150] The computation unit is used to perform differential calculations on strain modal curves using cubic spline interpolation and to construct direct indicators for damage localization.

[0151] In some alternative implementations, the second processing module 304 includes: The identification unit is used to identify the damage location and degree of different simulated damages in the finite element model of the composite material of the target wind turbine blade based on the strain modal change rate and direct indicators of damage location.

[0152] The first determining unit is used to determine multiple simulated damage-prone nodes based on the damage location.

[0153] The first processing unit is used to process the stress time history of multiple simulated damage-prone nodes using the rainflow counting method, and generate a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted.

[0154] In some alternative implementations, the computing module 305 includes: The calculation unit is used to calculate multiple basic fatigue damage values ​​of the wind turbine blade under different stress levels based on the comprehensive stress spectrum under multiple working conditions, using the stress-life curve of fiberglass composite material and Miner's linear cumulative damage criterion.

[0155] The second determining unit is used to determine the initial total fatigue damage value of the wind turbine blade to be predicted based on multiple basic fatigue damage values.

[0156] In some alternative implementations, the correction module 306 includes: The second processing unit is used to obtain the membership value of the total damage degree of the wind turbine blade to be predicted by processing the target membership function based on the initial total fatigue damage value.

[0157] The third processing unit is used to obtain the corrected target total fatigue damage value based on the total damage degree membership value, the low-amplitude load strengthening function, and the load interaction effect through the fuzzy damage accumulation model.

[0158] The third determining unit is used to determine the fatigue life prediction result of the wind turbine blade to be predicted based on the target total fatigue damage value.

[0159] 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 for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0160] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0161] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable 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.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0162] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 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.

[0163] 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 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the wind turbine blade fatigue life prediction method of the embodiments of the present invention.

[0164] Figure 4 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.

[0165] 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 method shown in the above embodiments is implemented.

[0166] 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.

[0167] 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 predicting the fatigue life of wind turbine blades, characterized in that, The method includes: Obtain the three-dimensional geometric model of the wind turbine blade to be predicted; Based on the three-dimensional geometric model, the stress distribution of the wind turbine blade under different wind speeds is analyzed using transient dynamics, and a finite element model of the target wind turbine blade composite material containing different simulated damage is generated. Based on the finite element model of the target wind turbine blade composite material, the strain mode change rate and direct indicators of damage location are obtained through modal analysis and cubic spline interpolation. Based on the strain modal change rate and the direct index of damage location, the stress time history of different simulated damage hazard nodes in the finite element model of the target wind turbine blade composite material is processed by the rainflow counting method, and the multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated. Based on the multi-condition comprehensive stress spectrum, the initial total fatigue damage value of the wind turbine blade under different stress levels is calculated using the stress life curve of the fiberglass composite material and the Miner linear cumulative damage criterion. The initial total fatigue damage value is corrected using the target membership function and the low-amplitude load strengthening function, and the fatigue life prediction result of the wind turbine blade to be predicted is determined. The target membership function is a semi-large semi-trapezoidal membership function constructed based on fuzzy mathematics theory.

2. The method according to claim 1, characterized in that, Based on the aforementioned three-dimensional geometric model, transient dynamics is used to analyze the stress distribution of the wind turbine blade under different wind speeds, and a finite element model of the target wind turbine blade composite material containing different simulated damages is generated, including: Based on the three-dimensional geometric model, the composite material of the wind turbine blade to be predicted is designed with layup and meshed to obtain the initial finite element model of the composite material of the wind turbine blade to be predicted. The stress distribution of the wind turbine blade to be predicted under different wind speeds was analyzed using transient dynamics, and multiple dangerous areas were identified in the initial finite element model of the composite material of the wind turbine blade to be predicted. Different simulated damages were set in each hazardous area, and a finite element model of the target wind turbine blade composite material containing different simulated damages was obtained.

3. The method according to claim 1, characterized in that, Based on the finite element model of the target wind turbine blade composite material, modal analysis and cubic spline interpolation were performed to obtain the strain modal change rate and direct indicators of damage location, including: Displacement mode and strain mode analysis were performed on the finite element model of the target wind turbine blade composite material, and the strain mode curve and the strain mode change rate were determined. The strain modal curves are subjected to differential calculations using the cubic spline interpolation method, and the direct index for damage localization is constructed.

4. The method according to claim 1, characterized in that, Based on the strain modal change rate and the direct damage location index, the stress time history of different simulated damage-prone nodes in the finite element model of the target wind turbine blade composite material is processed using the rainflow counting method, and a multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated, including: Based on the strain modal change rate and the direct damage location index, the damage location and damage degree of different simulated damages in the finite element model of the target wind turbine blade composite material are identified. Multiple simulated damage-prone nodes were determined based on the damage location; The stress time history of the multiple simulated damage-prone nodes is processed using the rainflow counting method, and the multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted is generated.

5. The method according to claim 1, characterized in that, Based on the comprehensive stress spectrum under the aforementioned multi-condition conditions, the initial total fatigue damage value of the wind turbine blade under different stress levels is calculated using the stress-life curve of the fiberglass composite material and the Miner linear cumulative damage criterion, including: Based on the multi-condition comprehensive stress spectrum, the stress life curve of the fiberglass composite material and the Miner linear cumulative damage criterion are used to calculate multiple basic fatigue damage values ​​of the wind turbine blade to be predicted under different stress levels. Based on the multiple basic fatigue damage values, the initial total fatigue damage value of the wind turbine blade to be predicted is determined.

6. The method according to claim 1, characterized in that, The initial total fatigue damage value is corrected using the target membership function and the low-amplitude load strengthening function, and the fatigue life prediction result of the wind turbine blade to be predicted is determined, including: Based on the initial total fatigue damage value, the total damage degree membership value of the wind turbine blade to be predicted is obtained after processing by the target membership function. Based on the total damage degree membership value, the low-amplitude load strengthening function, and the load interaction effect, the corrected target total fatigue damage value is obtained after processing by the fuzzy damage accumulation model. Based on the target total fatigue damage value, the fatigue life prediction result of the wind turbine blade to be predicted is determined.

7. A device for predicting the fatigue life of wind turbine blades, characterized in that, The device includes: The acquisition module is used to acquire the three-dimensional geometric model of the wind turbine blade to be predicted; The analysis module is used to analyze the stress distribution of the wind turbine blade under different wind speeds based on the three-dimensional geometric model and using transient dynamics, and to generate a finite element model of the target wind turbine blade composite material containing different simulated damages. The first processing module is used to obtain the strain mode change rate and direct indicators of damage location based on the finite element model of the target wind turbine blade composite material, through modal analysis and cubic spline interpolation. The second processing module is used to process the stress time history of different simulated damage risk nodes in the finite element model of the target wind turbine blade composite material based on the strain modal change rate and the direct damage location index, using the rainflow counting method, and to generate the multi-condition comprehensive stress spectrum of the wind turbine blade to be predicted. The calculation module is used to calculate the initial total fatigue damage value of the wind turbine blade under different stress levels based on the multi-condition comprehensive stress spectrum, using the stress life curve of the fiberglass composite material and the Miner linear cumulative damage criterion. The correction module is used to correct the initial total fatigue damage value using the target membership function and the low-amplitude load strengthening function, and to determine the fatigue life prediction result of the wind turbine blade to be predicted, wherein the target membership function is a skewed semi-trapezoidal membership function constructed based on fuzzy mathematics theory.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine blade fatigue life prediction method according to any one of claims 1 to 6.

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 method according to any one of claims 1 to 6.

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

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