Large Wind Turbine Blades Research Articles

Extended scaling approach for droplet flow and glaze ice accretion on a rotating wind turbine blade

This paper presents and analyzes a non-dimensional model of the flow field and droplet trajectories with glaze ice accretion on a rotating wind turbine blade. The formulation leads to similitude relationships for glaze ice accretion to evaluate new scaling parameters corresponding to the rotation of the blade. New scaling parameters are developed to determine ice scaling conditions based on the geometry scale factor of a wind turbine blade. The scaling methodology can be used to determine alternative test conditions and predict glaze icing conditions on a full-scale wind turbine blade. Numerical CFD icing simulations are performed using ANSYS FENSAP ICE software. A wind blade model is developed using blade element momentum theory (BEM). Each blade size is tested at specific flow conditions using the scaling equations. Scaled conditions for velocity (streamwise and rotational), droplet size, and icing time are tested. CFD solutions for the flow field are obtained for comparisons of the velocity, droplet trajectories, pressure coefficient distributions, ice thickness, and ice shapes. Recommended parameters to be used for glaze ice scaling on a rotating blade are presented and numerical results are reported to support these recommendations. The paper provides new insights into modeling of glaze ice accretion on large wind turbine blades based on smaller scaled blade sections tested in a laboratory setting.

The Impact of Wood Fibers in Composite Panels Made from Recycled Fiberglass Wind Turbine Blades

The utilization of wind power has increased almost exponentially during the last decade, leading to the growth in both the number and size of wind turbines. Modern turbine blades are typically made of fiberglass sandwiched around a balsa wood (BW) core. With an increase in the use of wind energy a collateral issue of what to do with the large and voluminous wind turbine blades (WTB) that reached end-of-life has arisen. Through previous research, we developed a recycling process that will keep these turbine blades out of the landfill by considering recycled wind turbine blades as a feedstock for second generation composite panels. The objective of this research is to further the research by understanding the effect that different compositions of wood and fiberglass have on the properties of these panels. Through testing, it was found that the addition of balsa wood increases the modulus of rupture (MOR) and the modulus of elasticity (MOE). The internal bonding (IB) strength values are well above that of typical particle board. All panels tested were visibly more resistant to water sorption than typical particle board. The thermal conductivity for all samples was within the typical range for particleboard. Fire test result indicates that the addition of balsa wood increases the heat and visible smoke release rate of composites.

Defect detection of large wind turbine blades based on image stitching and improved Unet network

Aiming at the problem that the existing computer vision detection technology is difficult to comprehensively and carefully detect the damage status of large wind turbine blades due to the limitation of the field of view, this paper proposes a refined and multi-scale detection method for large-scale wind turbine blades by combining an image stitching algorithm and a deep learning network. First of all, combining the image stitching algorithm with image weighted fusion, images of large wind turbine blades shot in close range are stitched together, so as to realize the clear restoration of the full size and defects of the blades. On this basis, an improved Unet network VGG16Unet is proposed. Combined with transfer learning, the classification and detection of various defects on wind turbine blades under the condition of small dataset training are realized. Finally, by the aid of the combination of the image stitching algorithm and the semantic segmentation network, the refined damage detection of the overall structure of large wind turbine blades is implemented. The research shows that the mean pixel accuracy and the mean intersection over union of the VGG16Unet model are 95.33% and 85.20%, respectively, which is better than the classical semantic segmentation models, fully convolutional neural network model and Unet model. The combination of the VGG16Unet model and the image stitching algorithm not only realizes the global detection of the entire structure but also ensures the detailed detection of each local area, which makes the detection of large wind turbine blades more comprehensive and refined.

Scaling formulation of multiphase flow and droplet trajectories with rime ice accretion on a rotating wind turbine blade

In this paper, scaling and similitude are investigated for ice accretion on a rotating wind turbine blade. Numerical CFD icing simulations are performed using FENSAP ICE software to test the scaling methods and verify the results. A small blade section is scaled six times, and each case is tested at specific flow conditions. Scaled conditions for velocity (streamwise and rotational), droplet size, and icing time are examined. CFD solutions for the flow field (air and droplet) are obtained in terms of the velocity, droplet trajectories, pressure coefficient distributions, ice thickness, and ice shapes, by quantifying the significant parameters involved in the icing process. Recommendations for parameters to be used for rime ice scaling on a rotating blade are presented, and numerical results are reported to support those recommendations. Numerical results and test conditions are obtained at sea level in wind tunnel facilities for experimental investigation. This paper presents the formulation of non-dimensionlized governing equations for scaling of the flow field, droplet trajectories, and ice accretion on a rotating blade model. It derives a set of non-dimensional groups which govern the flow parameters for a rotating blade, including the Strouhal number, Froude number, drag coefficient of droplets and droplet based Reynolds number, droplet inertia parameter, and two other new parameters. The scaling methodology can also be utilized to determine alternative test conditions to predict rime ice conditions on a rotating blade. The study provides valuable insight to predict ice accretion on large wind turbine blade sections in the field based on scaled smaller blade sections tested in a laboratory setting.

Vibration Control of Large Wind Turbine Blades with Unidirectional Cable Pendulum Damper

A unidirectional cable pendulum damper is proposed to mitigate vibration responses of large-scale wind turbine blades in the edgewise direction, and the effectiveness of the proposed device for vibration response control is investigated. First, the configuration and working principles of the vibration damper are introduced. Second, the Euler–Lagrange equation of the single blade vibration damping system with the vibration damper is derived based on the Hamilton’s principle. At last, considering the effect of blade rotation, the wind load that applied on the blade was calculated using the rotating sample spectrum. The dynamic responses of blades are also calculated using the MATLAB/Simulink platform. The peak value and standard deviation of blade responses were taken as evaluation indexes to evaluate the vibration control efficiency of the device with varying the parameters such as the mass ball pendulum length, the fixed pulley block installation position, the stiffness and the pre-stress of the flexible cable. The optimal parameters of the vibration damper are investigated and obtained based on the assumption that no pounding happens between the mass ball of the vibration damper and the inner wall of the blade. Results show that the length of the mass ball pendulum and the installation position of the fixed pulley block, the values of which are within a reasonable range, can ensure the optimal performance of the vibration damper. The displacement of the mass sphere can be effectively reduced by increasing the stiffness of the flexible cable, and the pre-stress shows marginal beneficial effect on the damping performance and the displacement control of the mass sphere. Using the optimum parameters, the vibration damper can keep the mass ball within a safe running distance. The reduction of the peak value and the standard deviation of the blade displacement can reach 89.36% and 89.83%, respectively. The proposed device space can effectively reduce the blade tip response of the wind turbine by occupying less space. Moreover, the size of the device installation space can be changed by adjusting the weight ball swing length, which is suitable for the vibration control of the wind turbine blade edgewise direction.

Interfacial and tensile properties improvement of composites using a modified matrix with epoxy‐terminated hyperbranched polyether sulfone

AbstractThe carbon fiber/epoxy resin (CF/EP) composites have been prepared using CF and the diglycidyl ether of bisphenol‐A (DGEBA) matrix, modified by a new epoxy‐terminated hyperbranched polyether sulfone (EHPES). Compared with CF/DGEBA composite, the modified CF/EHPES composites achieve excellent performances. The tensile strength, modulus, and elongation at break are increased by 19.1%, 7.9%, and 66.7%, respectively. The results are mainly caused by the strong interfacial properties, the interfacial shear strength (IFSS) of CF/7% EHPES composite is increased by 20.7%. Many crosslinking points enhance the mechanical interlocking between the DGEBA/EHPES matrix and CF surface. The strengthened chemical bonding promotes the formation of a higher density crosslinking network. This study provides a strategy for the domestication of large diameter wind turbine blades.

Nonlinear dynamic analysis of parked large wind turbine blade considering harmonic inertial excitation using continuum mathematical model

In this paper, an investigation on the nonlinear dynamics of parked large wind turbine blades considering harmonic inertial excitation is presented. The investigation is based on the continuum mathematical model which is derived using the extended Hamilton principle. Employing the Euler–Bernoulli beam model, the equations of motion are derived by considering the flap-wise and edge-wise deformations as well as the cubic nonlinearity. The proposed model is validated through comparisons with both published results and numerical simulations. The nonlinear dynamics of the NREL 5-MW wind turbine blade is investigated numerically using a bifurcation toolbox MATCONT. The steady state primary resonances of the first three modes are compared and analyzed with varying excitation frequency and excitation direction. Results show that for parked blades the aerodynamic damping increases with the increase of inflow wind speed, and the response under low wind speed becomes more unfavorable than high wind speed. The blade response can suddenly jump up or down when the excitation direction varies slightly because of the saddle node bifurcation. The 3:1 internal resonance between the 1st and 3rd modes is also observed in this study, suggesting the possible interaction of the two modes during large amplitude vibration. Quasi-periodic motions can also be induced because of the Hopf bifurcation in the case of negative aerodynamic damping.

Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM.

Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.

Infusible thermoplastic resin based sandwich structures for wind blade applications and the influence of scrim on facesheet to core interface debonding

Infusible thermoplastic Elium® family of resins from Arkema have garnered much attention in recent years as a possible replacement for thermoset resins in laminate and sandwich composite manufacturing for wind blade applications due to its ease of recyclability and the ability to utilize existing manufacturing processes without imposing complicated variations. However, physical and mechanical properties of the proposed Elium® based thermoplastic composites must be comparable to existing epoxy (thermoset) based composites using manufacturing processes relevant for large wind turbine blades. A 13-meter-long demonstration blade was manufactured for that purpose and sandwich samples were obtained from that project for a detailed study. This paper details three-point flexural properties of unidirectional E-glass fiber reinforced acrylic and epoxy based sandwich panels with identical balsa wood core materials. In addition, to evaluate the relative merit considering debond failure mode, the interfacial critical strain energy release rate, predominantly in mode-1, was compared via single cantilever beam testing. In sandwich composites constructed with balsa wood core material, resin uptake by the balsa core is traditionally impeded via the insertion of a scrim material at the facesheet to core interface. Results revealed that inclusion of scrim mesh layer at the facesheet to core interface reduced flexural properties and strain energy release rates in panels infused with acrylic resin but did not significantly reduce these properties in epoxy infused facesheets.

An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics

The establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with enhanced predictive capabilities. This is particularly relevant for wind energy infrastructures, where the definition of remaining useful life is a main driver for assessing the efficacy of these systems. In order to ensure a proper representation of the physical structure, the monitored response of the Digital Twin should match the one experienced by the actual system throughout the complete spectrum of its operational conditions. In most typical SHM configurations, it is only possible to rely on output-only measurements, acquired from finite positions within a structure, which naturally raises the challenge of recovering the full-field operational response, including unmeasured locations. This problem, also known as Virtual Sensing (VS), has been treated using different schemes, including Bayesian filtering and Modal Expansion (ME). In this paper, the Augmented Kalman Filter (AKF) is exploited to this end; a tool which allows for simultaneous full-field response and unmeasured input prediction. The common issue of Bayesian filtering relies on calibration of the filters defining parameters, namely the assumed measurement and process noise covariance levels. While the first is directly related to the accuracy of the employed physical sensors, the latter often acts as a tuning parameter for improving the reliability of the prediction. The process noise covariance adjustment is often performed in an offline fashion, either by making use of regularization methods, e.g., the L-curve method, or via trial and error. In this work, we propose a methodology for automated process noise covariance adaptation, relying on response estimates recovered by means of an improved ME approach. The method is validated on experimental data from a large scale research Wind Turbine (WT) blade made of glass fiber reinforced plastics.

State-of-the-art review of product stewardship strategies for large composite wind turbine blades

Is predicted that around 42 million tonnes of composite waste from wind turbine blades will need to be recycled annually worldwide by 2050. This poses a potential environmental crisis that must be timely mitigated. Therefore, this study proposes an integrated multilevel product stewardship to address the environmental impact of wind turbine blade waste. This product stewardship integrates circular economy, cleaner production, eco design, and industry 4.0 technologies. To tailor the proposed product stewardship, a systematic literature review that extracted a total of 267 studies and industry reports was performed. A large variety of technologies were identified under seven different potential pathways that can be taken and combined to address the environmental impact of wind turbine blades. Moreover, a technology roadmap and a project strategy plan composed of 5 milestones, to be achieved by 2050, were presented envisioning the maturation and adoption of the proposed solutions.

Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades

With global warming and the depletion of fossil energy sources, renewable energy is gradually replacing non-renewable energy as the main energy in the future. As one of the fastest growing renewable energy sources, the safety and reliability of wind energy have been paid more and more attention. The size of modern wind turbines is becoming larger and larger. As the main component of wind turbines to capture energy, the blade is often damaged by various complex environments and irregular loads. Therefore, the health monitoring and damage identification of wind turbine blades have become a main research focus. At present, in addition to the overview of various detection methods of wind turbine blades, there is a lack of comprehensive classifications and overviews of the main damage types, damage-generation mechanisms, and basic principles of the damage-detection technology of wind turbine blades. In this paper, firstly, the common fault types of wind turbine blades, such as trailing edge cracking, lightning strike, leading edge corrosion pollution, icing, and delamination, as well as their generation mechanism, are comprehensively analyzed. Then, the basic principles and the latest research progress of the current main detection technologies, such as vision, ultrasonic, thermal imaging, vibration, acoustic emission, and so on, are comprehensively reviewed. The advantages and limitations of the various detection technologies for practical application are summarized. Finally, through a comparative analysis of the various damage-detection technologies, we try to find potential future research directions, and draw conclusions. This paper will provide a reference for understanding the mechanism behind the main damage types and the damage-detection methods of wind turbine blades. It has important reference value for further promoting practical research of wind turbine blade damage-detection technology and grasping this research direction.

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

The structural condition of blades is mainly evaluated using manual inspection methods. However, these methods are time-consuming, labor-intensive, and costly, and the detection results significantly depend on the experience of inspectors, often resulting in lower precision. Focusing on the dynamic characteristics (i.e., natural frequencies) of large wind turbine blades, this study proposes a monitoring method based on the target-free DSST (Discriminative Scale Space Tracker) vision algorithm and UAV. First, the displacement drift of UAV during hovering is studied. Accordingly, a displacement compensation method based on high-pass filtering is proposed herein, and the scale factor is adaptive. Then, the machine learning is employed to map the position and scale filters of the DSST algorithm to highlight the features of the target image. Subsequently, a target-free DSST vision algorithm is proposed, in which illumination changes and complex backgrounds are considered. Additionally, the algorithm is verified using traditional computer vision algorithms. Finally, the UAV and the target-free DSST vision algorithm are used to extract the dynamic characteristic of the wind turbine blades under shutdown. Results show that the proposed method can accurately identify the dynamic characteristics of the wind turbine blade. This study can serve as a reference for assessment of the condition of wind turbine blades.

Uncertainty quantification of modal parameter estimates obtained from subspace identification: An experimental validation on a laboratory test of a large-scale wind turbine blade

The uncertainty afflicting modal parameter estimates stems from e.g., the finite data length, unknown, or partly measured inputs and the choice of the identification algorithm. Quantification of the related errors with the statistical Delta method is a recent tool, useful in many modern modal analysis applications e.g., damage diagnosis, reliability analysis, model calibration. In this paper, the Delta method-based uncertainty quantification methodology is validated for obtaining the uncertainty of the modal parameter and the modal indicator estimates in the context of several well-known subspace identification algorithms. The focus of this study is to validate the quality of each Delta method-based approximation with respect to the experimental Monte Carlo distributions of parameter estimates using a statistical distance measure. On top of that, the accuracy in obtaining the related confidence intervals is empirically assessed. The case study is based on data obtained from an extensive experimental campaign of a large scale wind turbine blade tested in a laboratory environment. The results confirm that the Delta method is, on average, adequate to characterize the distribution of the considered estimates solely based on the quantities obtained from one data set, validating the use of this statistical framework for uncertainty quantification in practice.

Fault Detection of Wind Turbine Blades Using Multi-Channel CNN

This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades.

Stall-induced vibrations analysis and mitigation of a wind turbine rotor at idling state: Theory and experiment

With the development of wind turbine manufacturing technology, the wind turbine rotor is facing more design challenges for exposing to complex environmental conditions and severe wind loads. This paper investigates the stall-induced vibrations of a wind turbine rotor operating at idling state, and a strategy to mitigate the stall-induced vibrations is proposed. The equations of motion of the wind turbine rotor are established by Hamilton's principle and solved using the finite element method (FEM). The aerodynamic forces are evaluated based on the blade element theory, and the aero-damping is obtained by solving the eigenvalue problem of the rotor system. The stability of the wind turbine rotor is analyzed under different wind speeds, wind deviation angles, azimuth angles and pitch angles. The calculation results show that negative aero-damping exists in specific wind deviation angles, which leads to the divergence of edge-wise motions of the blades. An on-site experimental study validates the criteria of stability of the wind turbine rotor. The present work provides positive outlooks for aeroelastic analysis of the wind turbine blades at idling states, and it is significant for the design and safety improvement of large wind turbine blades.

Nonlinear flutter analysis of a bend-twist coupled composite wind turbine blade in time domain

Modern wind turbine blade having large slender structure are vulnerable to aeroelastic instability. Aeroelastic tailoring through bend-twist stiffness coupling is an effective way to improve the aeroelastic performance of such large blades. Effect of geometric nonlinearity is significant in these blades. This study presents a time domain aeroelastic analysis followed by a detailed parametric study of large composite wind turbine blade considering von-Karman nonlinearity in the strain–displacement relationship. Flutter limits of three MW sized namely, NREL 5 MW, SNL 61.5 and SNL 100–00 wind turbine blades considering and without considering geometric nonlinearity are compared. A parametric study of SNL 61.5 is conducted by changing the lamination sequence in the spar cap and all section of blade considering the symmetric and asymmetric skins. It has been observed that both nonlinear and linear structural models i.e., models considering and without considering geometric nonlinearity predict highest flutter speed at the same blade configuration and lamination sequence. However, there is a significant difference in the flutter speeds predicted by these models.

Analytical and numerical study on centrifugal stiffening effect for large rotating wind turbine blade based on NREL 5 MW and WindPACT 1.5 MW models

To pursue high efficiency for wind turbine and harness more energy from wind, the wind power generation system has been designed larger and larger. With length scale increasing, the natural frequency of blade will be lower and changing dramatically with rotational speed, which will threaten the stability of the blade and turbine. However, in the previous research, the parametric, analytical, and quantitative study of centrifugal stiffening effect is rare to see. As a result, the current research has comprehensively studied the centrifugal stiffening effect on the structural characteristics of large wind turbine blade. Specifically, the equivalent rotating wedge beam model has been proposed for composite wind turbine blade. Based on the proposed wedge beam model and Rayleigh-Ritz method, the natural frequency variation curve has been derived and verified by ANSYS 3D simulation results. The parameters, including blade length, stiffness-mass ratio, and aspect ratio, affecting the natural frequency curve have been uncovered and analysed. It was found that the centrifugal stiffening effect has a great impact on the fundamental frequency of blade, e.g. 10% for NREL 5 MW, while it has less impact on other modal frequencies. The variation curve of fundamental natural frequency can be approximated by parabola. The product of the parabola curvature and fundamental frequency is found to be approximately constant for both NREL 5 MW and WindPACT 1.5 MW turbine blades. This research will provide guidelines for the resonance avoidance design of large wind turbine blade in the future.

Investigation of the three-dimensional flow past a flatback wind turbine airfoil at high angles of attack

Flatback airfoils are airfoils with a blunt trailing edge. They are currently commonly used in the inboard part of large wind turbine blades, as they offer a number of aerodynamic, structural, and aeroelastic benefits. However, the flow past them at high angles of attack (AoA) has received relatively little attention until now. This is important because they usually operate at high AoA at the inboard part of Wind Turbine blades. The present investigation uses Reynolds averaged Navier–Stokes (RANS) and hybrid RANS + large eddy simulation predictions to analyze the flow in question. The numerical results are validated against previously published wind tunnel experiments. The analysis reveals that to successfully simulate this flow, the spanwise extent of the computational domain is crucial, more so than the selection of the modeling approach. Additionally, a low-drag regime observed at angles of attack before stall is identified and analyzed in detail. Finally, the complex interaction between the three-dimensional separated flow beyond maximum lift (stall cells) with the vortex shedding from the blunt trailing edge is revealed.

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.