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Wind energy is gaining critical ground in the area of renewable energy, with wind energy being predicted to provide up to 8% of the worlds consumption of electricity by 2021. This book reviews the design and functionality of wind turbine rotor blades as well as the requirements and challenges for composite materials used in both current and future designs of wind turbine blades. Part one outlines the challenges and developments in wind turbine blade design, including aerodynamic and aeroelastic design features, fatigue loads on wind turbine blades, and characteristics of wind turbine blade airfoils. Part two discusses the fatigue behavior of composite wind turbine blades, including the micromechanical modelling and fatigue life prediction of wind turbine blade composite materials, and the effects of resin and reinforcement variations on the fatigue resistance of wind turbine blades. The final part of the book describes advances in wind turbine blade materials, development and testing, including biobased composites, surface protection and coatings, structural performance testing and the design, manufacture and testing of small wind turbine blades This book offers a comprehensive review of the recent advances and challenges encountered in wind turbine blade materials and design, and will provide an invaluable reference for researchers and innovators in the field of wind energy production, including materials scientists and engineers, wind turbine blade manufacturers and maintenance technicians, scientists, researchers and academics.

A review of the root causes and mechanisms of damage and failure to wind turbine blades is presented in this paper. In particular, the mechanisms of leading edge erosion, adhesive joint degradation, trailing edge failure, buckling and blade collapse phenomena are considered. Methods of investigation of different damage mechanisms are reviewed, including full scale testing, post-mortem analysis, incident reports, computational simulations and sub-component testing. The most endangered regions of blades include the protruding parts (tip, leading edges), tapered and transitional areas and bond lines/adhesives. Computational models of different blade damage mechanisms are discussed. The role of manufacturing defects (voids, debonding, waviness, other deviations) for the failure mechanisms of wind turbine blades is highlighted. It is concluded that the strength and durability of wind turbine blades is controlled to a large degree by the strength of adhesive joints, interfaces and thin layers (interlaminar layers, adhesives) in the blade. Possible solutions to mitigate various blade damage mechanisms are discussed.

Various scenarios of end-of-life management of wind turbine blades are reviewed. “Reactive” strategies, designed to deal with already available, ageing turbines, installed in the 2000s, are discussed, among them, maintenance and repair, reuse, refurbishment and recycling. The main results and challenges of “pro-active strategies”, designed to ensure recyclability of new generations of wind turbines, are discussed. Among the main directions, the wind turbine blades with thermoplastic and recyclable thermoset composite matrices, as well as wood, bamboo and natural fiber-based composites were reviewed. It is argued that repair and reuse of wind turbine blades, and extension of the blade life has currently a number of advantages over other approaches. While new recyclable materials have been tested in laboratories, or in some cases on small or medium blades, there are remaining technological challenges for their utilization in large wind turbine blades.

Results of a survey of failure mechanisms of wind turbine blades in India, observed by service companies, are presented. Surface erosion is the most often observed blade damage mechanism, followed by lightning strikes. Leading edge erosion can be observed even 1–2 years after wind turbine installation, while structural cracks are observed most often only 5–8 years after installation of the wind turbines. The most often emergency repair requests are connected with blade bolt replacement, followed by lightning strikes. Lightning strikes are registered relatively often, every 1–2 years, depending on climate. Lightning strikes are also most often observed in monsoon areas of India and are most common reason for the wind turbine downtimes.

Structural optimization has been shown to be an invaluable tool for solving large-scale challenging design problems, and this work concerns such optimization of a state-of-the-art laminated composite wind turbine blade root section. For laminated composites structures, the key design parameters are material choice, fiber orientation, stacking sequence, and layer thickness, however a framework for treating these simultaneously in optimization, on the current wind turbine blade scale, has not been demonstrated. Thus, the motivation and novelty of the present work is providing and demonstrating a general gradient-based approach applicable to wind turbine blades, where the key design parameters and structural criteria, i.e., buckling, static strength, and fatigue damage, are considered for multiple design load cases. The optimization framework is based on a variation of the Discrete Material and Thickness Optimization approach, where the thickness is directly parametrized, allowing for appropriately treating the sandwich parts of the blade. It is demonstrated how optimization leads to a design consisting of complex variable-thickness laminates, a good overall distribution of the structural criteria in the model, and a significant reduction in mass compared to the initial design.

This paper studies reliability-based design optimization (RBDO) of a 5-MW wind turbine blade for designing reliable as well as economical wind turbine blades. A novel dynamic wind load uncertainty model has been developed using 249 groups of wind data to consider wind load variation over a large spatiotemporal range. The probability of fatigue failure during a 20-year service life is estimated using the uncertainty model in the RBDO process and is reduced to meet a desired target reliability. Meanwhile, the cost of composite materials used in the blade is minimized by optimizing the composite laminate thicknesses of the blade. In order to obtain the RBDO optimum design efficiently, deterministic design optimization (DDO) of the 5-MW wind turbine blade is carried out first using the mean wind load obtained from the wind load uncertainty model. The RBDO is then initiated from the DDO optimum. During the RBDO iterations, fatigue hotspots for RBDO are identified among the laminate section points. For an efficient RBDO process, surrogate models of 10-min fatigue damages D 10 at the hotspots are accurately created using the Kriging method. Using the wind load uncertainty model and surrogate models, probability of fatigue failure during a 20-year lifespan at the hotspots and the design sensitivities are calculated at given design points. Using the probability of fatigue failure and design sensitivity, RBDO of the 5-MW wind turbine blade has been successfully carried out, satisfying the target probability of failure of 2.275 %.

In this paper, a framework for stochastic optimization of horizontal-axis wind turbine composite blades is presented. It is well known that the structural responses of the wind turbines (e.g., natural frequency, blade tip displacement) are affected by uncertainties in, for instance, wind conditions and material properties. These uncertainties can have an undesirable impact on the performance and reliability of wind turbine blades, and therefore must be accounted for. However, performing the stochastic optimization of wind turbine blades is challenging because of the computational cost and the need to incorporate several disciplines. To make the stochastic problem tractable, a surrogate-based optimization framework using Kriging and support vector machines with adaptive refinement was developed. The framework is based on blade element momentum theory for aerodynamics coupled with a fully parameterized finite element structural model. The framework is used to find the optimal chord and twist distribution of a composite blade and, notably, the optimal control features such as tip-speed ratio and pitch angle with respect to operating wind speeds. The objective function considered is the ratio of mass to the expected value of the Annual Energy Production subjected to several probabilistic constraints on the blade tip deflection, natural frequencies, and failure indices. Uncertainties in material properties, as well as wind conditions are considered. The results of this industrial application demonstrate that the framework can lead, in a reasonable number of function calls, to an optimal composite blade with higher efficiency and robustness to uncertainty.

For wind turbines operating in cold weather conditions, ice accretion is an established issue that remains an obstacle in effective turbine operation. While the aerodynamic performance of wind turbine blades with ice accretion has received considerable research attention, few studies have investigated the structural impact of blade ice accretion. This work proposes an adaptable projection-based method to superimpose complex ice configurations onto a baseline structure. The proposed approach provides an efficient methodology to include ice accretion in the high-fidelity isogeometric shell analysis of a realistic wind turbine blade. Linear vibration and nonlinear deflection analyses of the blade are performed for various ice configurations to demonstrate the impact of different ice accretion distributions on structural performance. These analyses indicate decreases in the blade natural frequencies and deflection under icing conditions. Such ice-induced changes clearly reveal the need for structural design consideration for turbines operating under icing conditions.

For the current visual detection methods of wind turbine blade defects, their detection models are usually excessively large, making it difficult to achieve a balance between model accuracy and inference speed. To address this problem, this paper introduces a lightweight wind turbine blade defect detection network, GCB‐YOLO, which attempts to maintain high detection accuracy and simultaneously achieve rapid detection speed. Initially, a GhostNet network was employed to replace a portion of the YOLOv5s backbone network responsible for feature extraction. This replacement serves to reduce the network's parameter size and computational load, thereby achieving compression of the feature extraction network. A coordinate attention (CA) mechanism is subsequently incorporated into the backbone network, which enhances its ability to focus on small defects. Finally, the neck network was ultimately replaced with a bidirectional feature pyramid network (BiFPN) to optimize multiscale feature fusion, bolstering its ability to discern small defects. A series of validation experiments were conducted using an image dataset gathered from real wind farms. Compared with YOLOv5s, GCB‐YOLO resulted in a 46.2% reduction in the number of model parameters. The improved model has a 7.5 MB volume. Hence, in GPU computation mode, the image detection speed reached 115.3 frames per second. More importantly, the proposed method achieves an mAP@0.5 of 94.72%, simplifying deployment on edge computing devices and simultaneously meeting the real‐time defect detection requirement with a sustained high level of detection accuracy.

Wind-turbine blade rain and sand erosion, over long periods of time, can degrade the aerodynamic performance and therefore the power production. Computational analysis of the erosion can help engineers have a better understanding of the maintenance and protection requirements. We present an integrated method for this class of computational analysis. The main components of the method are the streamline-upwind/Petrov–Galerkin (SUPG) and pressure-stabilizing/Petrov–Galerkin (PSPG) stabilizations, a finite element particle-cloud tracking method, an erosion model based on two time scales, and the solid-extension mesh moving technique (SEMMT). The turbulent-flow nature of the analysis is handled with a Reynolds-averaged Navier–Stokes model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated in different ways depending on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the fluid–particle simulation. As the blade geometry evolves, the mesh is updated with the SEMMT. We present computational analysis of rain and sand erosion for a wind-turbine blade strip, including a case with actual rainfall data and experimental aerodynamic data for eroded airfoil geometries.