Wind Turbine Blades Research Articles

Bend-twist adaptive control for flexible wind turbine blades: Principles and experimental validation

This study proposes a new methodology for optimizing the power curve of a wind turbine at low wind speeds. The principles of bend-twist coupling and the mechanism of energy exchange between the structure and inflow are analyzed. For the blade’s geometric nonlinearity, the virtual displacements and strain fields are described using the Green-Lagrange strain theorem, retaining third-order terms in the energy expressions. The equations of motion are derived using Hamilton’s principle. The bend-twist coupling effects and large deformations of the blade are analyzed using the Updated Lagrange method. Notably, the angle of attack for a single blade section is influenced by bend-twist deformation, causing variations in the rotor’s maximum power coefficient from its optimal value. Additionally, the projection length of the blade, influenced by centrifugal forces, also affects the bend-twist deformation. Based on these findings, an aero-elastic coupling control strategy, termed “Bend-twist Adaptive Control”, is proposed and validated through experiments. The results demonstrate that the proposed control strategy could increase annual power production by 2.3 %. These conclusions offer a promising outlook for future wind turbine blade design and power optimization.

Null projection screen test for aerodynamic surfaces on wind turbine blades

This work proposes a novel method, to our knowledge, to qualitatively test aerodynamic surfaces by projecting a null screen. The main idea is, by using ray tracing equations in a model with two pinhole systems, to map an ordered predefined array of fringes formed by points on the detection plane, defined as a CMOS sensor, to the projection plane of a commercial projector, passing through the surface under test whose geometry must be known in advance. Once the coordinates on the projection plane are calculated, the pattern is projected onto the aerodynamic surface. The proposed predefined array will be observed by the camera if the surface and the alignment of the optical systems are perfect; otherwise, a distorted array will be detected, indicating misalignment or that the surface has defects that can be observed qualitatively. The mathematical expressions found to perform the direct and inverse ray tracing in 3D are applied to design a null test by projection on a reference plane and on an aerodynamic surface based on the NACA 0012 airfoil.

Evaluation of Manufacturing Small Wind Turbine (500 W) Blades via Injection Molding and Selection of Optimal Gate Position

Evaluation of Manufacturing Small Wind Turbine (500 W) Blades via Injection Molding and Selection of Optimal Gate Position

DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%.

Acoustic emission data analytics on delamination growth in a wind turbine blade under full-scale cyclic testing

This study investigates Acoustic Emission (AE) measurement data collected in an experiment where fatigue loading tests were carried out on a 14.3-meter full-scale composite wind turbine blade in a test facility. Artificial defects were embedded in the blade and these defects initiate damage growth in four different areas of the blade. Statistical algorithms and techniques were applied to the acoustic emission data to understand the growth of the damages and to identify possible fracture mechanisms. In composite materials, the different types of failure, such as micro-cracking, delamination, matrix cracking, etc. will release AE waveforms with a continuum of frequencies in different ranges. These frequency ranges will be used in the interpretation of the AE data. The AE parameters, i.e., energy and frequency, are used to show a steady damage growth and structural failure of the blade. These parameters, combined with AE amplitude probability distributions, can be used to monitor the structural health of wind turbine blades. This study provides new insights into the acoustic emission characteristics of a composite structure rather than material coupons.

Aerodynamic characteristics of wind turbines considering the inhomogeneity and periodic incentive of wake effects

In wind farms, the wake can lead to a loss of output power, and the uneven characteristics of wake can also exacerbate the fatigue load of downstream wind turbine (WT), affecting their operating life. To explore the impact of wake effects on downstream WT, this article proposes a new method for calculating the load of WT wake wind conditions and verifies it. Establishing a 3DJG wake model coupled with improved BEM for wind turbine aerodynamic performance calculation method. The influence of downstream WT operation phase angle and positions of 3D wake on the time-varying aerodynamic characteristics of downstream WTs was discussed from three perspectives: Blade elements on the spanwise, single blade and wind rotor. Results shown that: (1) For the blade element, calculated the shear force in the flapwise direction and the edgewise direction on each blade segment. The overall shear force of blade element in the vertical upward direction of WT blades is significantly greater than that in other directions. The non-uniformity of the wake is not sufficient to change the position of the maximum shear force on the blade. The position of the maximum shear force at each wake position is in the rear segments of the blade; (2) For the single blade, the most severe load fluctuation occurs when it is in the partial wake condition. The period of this fluctuation is 360°. When in full wake condition, the load fluctuation is relatively small, and due to the symmetry of the wake, the load fluctuation period is only 180°. The load standard deviation (SD) of the partial wake condition increases by 51 % compared to the full wake condition. (3) For the wind rotor, different wake positions have a weighty impact on the power loss of the WT rotor. Compared to the incoming shear wind, the power loss at the wake center can reach up to 54.22 %. At the wake boundary, the power loss of the WT is only 5.6 %. The non-uniformity of wake also has a periodic impact on the power output of the entire WT rotor, with the fluctuation period is 120°, and the fluctuation amplitude of no more than 2 %. This article provides a more intuitive quantitative analysis of the impact of wake on the time-varying aerodynamic characteristics of downstream WTs. It has certain reference value for the design strength verification of WTs, as well as the yaw strategy and layout design of wind farms.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Drone-based inspection of wind turbine blades: a comparative study of deep learning models

Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection. This paper offers a comparative study of five advanced deep learning models: Residual Networks (ResNet), Inception Networks (InceptionV3), YOLO (You Only Look Once), EfficientNet, and Vision Transformers (ViTs) applied to the DTU Drone Inspection Images of Wind Turbine dataset. The objective is to determine the most effective model for detecting defects in wind turbine blades, which is critical for ensuring the efficiency and safety of wind energy systems. The models were assessed using key performance metrics, such as accuracy, precision, recall, F1-score, and inference time. EfficientNet emerged as the top performer with the highest accuracy and balanced efficiency, while YOLO demonstrated the fastest inference time, making it ideal for real-time applications. ResNet and InceptionV3 also performed well, particularly in detecting subtle defects, whereas ViTs showed strong capabilities in capturing complex global features despite longer processing times. The findings provide valuable insights for selecting appropriate deep learning models in drone-based wind turbine inspections, contributing to more effective and reliable maintenance practices.

Fatigue damage mechanics approach to predict the end of incubation and breakthrough of leading edge protection coatings for wind turbine blades

The state of the surface of the leading edge of wind turbine blades is important for optimal aerodynamic performance. Rain erosion leads to damage and roughness on the leading edge. In this study, we present an approach to predict the level of roughness of the leading edge based on fatigue damage accumulation due to impacts of rain droplets. Impact simulations of droplets on the protective coating layer are coupled with rain erosion test data and fatigue S-N curves. Fatigue damage values are then related to surface roughness levels. An approach to extract S-N curve parameters from a rain erosion test and then make predictions for other coatings based only on the output of impact simulations is presented and tested against test data. Both the end of incubation and coating breakthrough times are predicted with this approach. Stress, strain and energy density values were used as fatigue damage indicators and the respective predictions were compared to each other. It was found that the maximum principal strain gave the best predictions and matched the experimental trends.

Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications.

Improved Lift for Thick Flatback Airfoils in the Inboard Blades of Large Wind Turbines

Improved Lift for Thick Flatback Airfoils in the Inboard Blades of Large Wind Turbines

Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network

Due to the global commitment to mitigate climate warming by replacing fossil fuels, the global wind power installed capacity continues to increase. The reliability of wind turbines is a vital factor in ensuring their safety and profitability. Early fault detection can significantly reduce the maintenance costs of wind farms. This paper constructs a Conv1d-GRU-MHA model for detecting blade structural damage in floating offshore wind turbines (FOWTs) operating under complex conditions. This model combines a one-dimensional convolutional neural network (Conv1d), gated recurrent units (GRU), and multi-head attention mechanism (MHA) to provide accurate detection of blade structural damage. This amalgamation enables the model to comprehend diverse scales of sequence data, effectively capturing intricate patterns and abstract features. The NREL OpenFAST software was used to model a 5 MW wind turbine, where structural damage was introduced on the blades. Acceleration data was collected from blades and tower in different directions and under different wind turbulence intensities (WTI). The Conv1d-GRU-MHA network predicts blade health with an accuracy of up to 97.58 % under the 5 % turbulence level background, with both precision and recall being superior to the comparative models.

Analyzing dynamic stall on tubercle mounted VAWT blades: A simplistic experimental approach using an oscillating rig

Leading-edge tubercles, inspired by the flippers of humpback whales, are widely adopted passive flow control devices to enhance the aerodynamic performance of various lifting surfaces. This experimental study investigates the implementation of sinusoidal and triangular tubercles on H-type Vertical Axis Wind Turbine blades to analyze their effects on dynamic stall characteristics. Experimental tests were conducted using a specially designed oscillating rig to replicate blade motion at different reduced frequencies. The results reveal that tubercle blades exhibit a lower stall angle and maximum normal force compared to the baseline configuration. Moreover, the dynamic stall characteristics of tubercle blades are notably smoother, leading to reduced hysteresis losses. A variation in the tubercle amplitude-wavelength ratio further decreases hysteresis, albeit at the cost of reduced normal force generation. At the highest tested reduced frequency of 0.065, tubercles reduce hysteresis by up to 38%. Despite the reduction in normal force, tubercles effectively mitigate the effects of dynamic stall vortices, resulting in smoother stall behavior. The observed reduction in hysteresis can contribute to enhancing the turbine’s lifespan and increasing power production efficiency. This experimental approach provides a cost-effective alternative to more expensive methods for studying dynamic stall characteristics.

Influence of surface curvature on the impact force of water droplet

Although the global market for wind energy is growing rapidly, leading-edge erosion is a critical issue hindering the development of wind power. The impact force of a droplet colliding with flat surfaces has been investigated in previous studies. However, the impact force exerted on curved surfaces, such as that experienced by eroded wind turbine blades, is not well understood. This study discusses the relationship between the impact force generated on a solid surface by a water droplet and the radius of curvature of the impacting surface. The impact force by a droplet was measured using a force sensor mounted on semi-cylindrical caps with different radii of curvature. The measurement results showed that the impact force decreased as the radius of curvature decreased. A computational fluid dynamics model solving incompressible flows showed that, unlike the case of a curved surface, the initial momentum of the droplet was mostly transferred to the flat surface. This resulted in a high impulse for an impact with a flat surface. The falling droplet was blocked by the surface, and the lateral jet was accelerated sideward. This acceleration was moderate for curved surfaces. When colliding with a flat surface, a higher impact force was generated owing to the wider area of the excited surface pressure compared with that of the curved surface. Finally, the relationship between the peak of the impact force and the surface curvature was derived, suggesting that the force peak is inversely proportional to the curvature.

Aerodynamics of a three-dimensionally deformed rigid wing

A flexible wing with a large aspect ratio emerges in many modern engineering applications (e.g. solar planes and super large wind turbine blades) and its interaction with incident flow differs markedly from conventional fluid–structure interactions (FSI), exhibiting frequently a large three-dimensional (3D) deformation. Yet, there is little information on how such deformation may change wing aerodynamics. This work investigates the aerodynamic performance of deformed and cantilever-supported NACA0012 rigid wings with an aspect ratio of 9, using ANSYS Fluent with the SST-κ-ω turbulence model at a chord-length-based Reynolds number Rec of 1.5 × 105. Numerical simulation is validated experimentally. The wing tip bending displacement is up to 4.14c, and the maximum twisted angle is up to 7°. The angle α of attack varies from 0° to 20° at mid span of the wing. It has been found that the torsional deformation can significantly advance the local flow separation, reattachment, bubble length, and transition from laminar to turbulence, resulting in a drop in the critical angle αcr of attack, at which the separation bubble size reaches the maximum. Accordingly, the lift and drag coefficients increase, as well as the bending and pitching-up moments, though the stall is advanced due to a change in local α. The tip vortex is also enhanced, inducing strong downwash postponing the separation bubble on the wing and resulting in redistributed force near the wing tip that increases markedly the local bending moment but decrease the local pitching-up moment. On the other hand, the bending deformation tends to produce an effect opposite to the torsion on the flow structure and causing little change in the lift coefficient, though reducing the induced drag and moments appreciably. With both deformations in place, the torsion overwhelms the bending in general in terms of its impact upon aerodynamics and flow structures, the latter acting to cancel at least partially the effect of the former.

Numerical investigation of the effect of winglet configurations with multiple cant angles on the aerodynamic performance of wind turbine blade

ABSTRACT A numerical investigation of the effects of winglets with multiple cant angles on the power output of the blade of a horizontal axis wind turbine (HAWT) is conducted. Several winglet configurations with single and multiple cant angles are designed and their performance is evaluated. Numerical solutions obtained by RANS equations and moving reference frame method were compared with the experiments and demonstrated that they concur well with the experimental data. In the torque and thrust comparison, the ones with multiple cant angles present better power output enhancement than those with a single cant angle at a relatively high wind speed. The configurations of multiple cant angles are effective in reducing the influence of wing tip vortices, resulting in a higher torque while maintaining the same winglet length and height.

Optimizing Aerofoil Design: A Comprehensive Analysis of Aerodynamic Efficiency through CFD Simulations and Wind Tunnel Experiments

This study explores the aerodynamic properties of different aerofoil shapes and their performance under varying flow conditions to identify the most efficient design based on lift-to-drag ratio, stall behaviour, and overall aerodynamic efficiency. Using Computational Fluid Dynamics (CFD) simulations, several aerofoil profiles were analysed at different angles of attack and flow speeds. These simulations were validated through wind tunnel experiments, offering a comprehensive understanding of aerofoil performance in real-world scenarios. The combination of CFD analysis and wind tunnel testing enabled a thorough assessment of each aerofoil shape, leading to the discovery of a specific aerofoil with a high lift-to-drag ratio and stable performance at high angles of attack. These results have significant implications for the design of wings and blades in aerospace and aeronautical applications, improving fuel efficiency and performance in both aviation and wind energy sectors. Additionally, dynamic roughness shows potential in reducing separation bubbles, but further investigation is needed to assess its effectiveness at higher angles of attack and elevated Reynolds numbers. Understanding the scalability and practical application of dynamic roughness in real-world scenarios is essential. Current research on surface modifications like dimples and riblets lacks optimized configurations for varying conditions. More research is needed to understand the interaction between surface geometries and the boundary layer, particularly at higher angles of attack and Reynolds numbers. Combining experimental and numerical methods can provide a comprehensive understanding of flow control techniques. The limited research on applying flow control strategies to wind turbine blades indicates a significant opportunity to improve wind energy efficiency. Future studies should focus on optimizing multiple techniques and addressing practical challenges, such as durability, cost-effectiveness, and integration into existing systems. Investigating the cost-effectiveness and durability of these modifications for long-term use will be vital for their successful adoption in the industry. Expanding research to include the effects of environmental factors like temperature and humidity will offer a more complete understanding of flow control in various operating conditions. By addressing these gaps, advancements in aerodynamic performance can be achieved, benefiting the aerospace and wind energy sectors.

Analyzing the Strelitzia Juncea Cellulosic Fibers Mechanical Properties’ Experimental Data Using Various Statistical Methods

ABSTRACT Manufacturing composites using natural fibers support the development of more ecologically friendly applications in many industrial areas, including sports equipment, wind turbine blades, construction materials, packaging, medical devices, and prosthetics. To investigate the tests’ number (N) impact on the fibers’ tensile behavior, newly developed fibers from the Strelitzia juncea (SJ) plant are being studied for their mechanical properties using various statistical laws. This study aims to assess the SJ fibers’ performance with a gauge length of 50 mm under quasi-static stress conditions. 150 SJ fibers were tensile tested in five groups (30, 60, 90, 120, and 150) to determine the N impact on the fibers’ tensile strength, break deformation, and Young’s modulus. Weibull and log-normal distributions were used to study statistically the mechanical properties results, and their dispersion was investigated and quantified using least squares and maximum likelihood prediction model with a confidence level of 95%.

Sensitivity Analysis of Fatigue Life for Cracked Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model under Distribution Parameter Uncertainty

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy.

Impact of wind turbine nominal power limitation over wind turbine blade remaining useful life and its economic consequences

The present paper investigates the effects of wind turbine nominal power limitation on the remaining useful life of turbine blades. It also looks at the economic impact of this limitation. In this context, the paper provides wind turbine owners and operators with an overview of how to potentially extend the remaining useful life of wind turbine blades and lays out the economic benefits that can be achieved via the modulation of nominal power. In investigating wind turbine blade damage, prior studies focused mainly on predictive models based on the 10 minutes SCADA data wind speed history, without however, trying to protract the remaining useful life of the blades. Only a handful of papers have explored the possibility of increasing the remaining useful life by adjusting the start-up and shutdown procedures with poor results. It would appear that wind turbine blade fatigue damage mainly increases when the wind turbine is in a power production regime, and the mechanical stresses associated with this regime are a function of the nominal power of the wind turbine. The present work therefore investigates the impacts of nominal power changes on both the remaining useful life of wind turbine blades and the economic value of the wind turbine in a bid to identify an optimal control mode. The wind turbine blade damage evaluation is based on 10 minutes SCADA data and the FAST simulation tool with the ultimate goal of providing wind turbine operators with an easy application. The damage evaluation is then applied considering different nominal power levels for the same wind turbine model in order to see the resulting impact on the remaining useful life. This project therefore takes a pioneering approach by proposing a remaining useful life optimization tool to wind turbine operators, in effect, a decision-making tool regarding which exploitation strategy to adopt.