Search

The wind turbine blades are exposed, during functioning, to the abrasive wear generated by the impact with air-borne sand particles. In this work, samples of a commercial wind turbine blade, made of a multi-layered composite material, are subjected to abrasive wear tests, using an air streamed wearing particles test rig. Following the analysis of the tests' results was found that the only protection against failure of the blade by abrasive damage is the surface layer. After its' penetration, the layers below are quickly destroyed, leading to the blade destruction. The investigation of the main abrasive wear influencing factors-particles' speed and acting time, showed that the particles' speed is the most important. To prove that an artificial neural network-based model was used. Also, a method for improvement of the blade resistance to abrasive wear is proposed, consisting of applying on the blade's surface of a polymeric foil. This offers supplementary protection of the surface layer, delaying its degradation. The tests performed on the protected samples prove the validity of the proposed method. Overall, the work showed the weakness of the blades' resistance in case of working in abrasive wear conditions and identified an improving method.

Rain erosion induced by raindrops impacting wind turbine blades at high velocity can change the aerodynamic characteristics of the blades and increase maintenance costs. Previous numerical studies on rain erosion have not considered the curvature of the blade leading-edge surfaces and assumed them to be flat surfaces. This study established a fluid-solid coupled numerical model combining the finite element method and smooth particle hydrodynamics. It models a water droplet with a diameter of 2.74 mm impacting the curved leading-edge surface of wind turbine blades with radii of curvature of 1.35 mm, 6.75 mm, 67.5 mm, and infinite at 110 m/s, and the effects of the radius of curvature on the impact response were analyzed. The results show that as the radius of curvature of the leading-edge surface increases, the surface obstructs the water droplet more significantly, and the lateral jetting of the water droplet is enhanced. A larger radius of curvature causes more droplet impact energy to be transferred to the curved surface, increasing the contact force between the water droplet and the surface. The increased transferred impact energy results in higher stress and plastic strain values. The decrease in the radius of curvature of a curved surface increases the error in the stress and strain results obtained by assuming it to be a flat surface., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This paper aimed to understand the AE signal characteristics and damage mechanism of wind turbine blade main spar materials with different defects during the damage evolution process. According to the typical delamination and wrinkle defects in wind turbine blades, the GFRP composite with defects is artificially prefabricated. Through acoustic emission experiments, the mechanical properties and acoustic emission characteristic trends of wind turbine blade main spar composites with different defects under tensile loading conditions were analyzed, and the damage evolution mechanism of different defects was explained according to the microscopic results. The results show that the existence of artificial defects will not only affect the mechanical properties of composite materials but also affect the damage evolution process of the materials. The size and location of delamination defects and the different aspect ratio of the wrinkle defects have a certain influence on the damage mechanism of the material. K-means cluster analysis of AE parameters identified the damage models of GFRP composites. The types of damage modes of delamination defects and wrinkle defects are the same, and the range of characteristic frequency is roughly the same. This study has important reference significance for structural damage monitoring and damage evolution research of wind turbine blade composites.

The recycling of end-of-life wind turbine blades has become a global environmental challenge driven by the rapid growth of wind power. Pyrolysis is a promising method for recovering glass fibers from these discarded blades, but traditional pyrolysis is often operated at high temperatures, which degrades the mechanical properties of recovered fibers. To address this issue, a swelling-assisted pyrolysis method was proposed to recover high-quality glass fibers from end-of-life wind turbine blades at low temperatures. The results confirmed that the decomposition of the resin matrix within the blade was significantly promoted at low temperatures in the swelling-assisted pyrolysis process, achieving a resin decomposition ratio of 76.8 % at 350 °C. This improvement was attributed to enhanced heat transfer and co-pyrolysis with acetic acid. Swelling could physically disrupt the cross-linked structure of the blade, creating a more porous and layered structure, thereby enhancing heat transfer during the pyrolysis process. Simultaneously, the co-pyrolysis with acetic acid could generate hydrogen radicals, which promoted the cracking of macromolecular oligomers into lighter products or gaseous alkanes. Consequently, the formation of pyrolysis char within the solid pyrolysis product was reduced, shortening the oxidation duration to 30 min. In comparison to traditional pyrolysis, the swelling-assisted pyrolysis process effectively suppressed the diffusion of surface defects over the recovered fibers, leading to promising improvements in their flexibility, elasticity, and mechanical properties, with tensile strength notably increased by 27.5 %. These findings provided valuable insights into recovering high-quality glass fibers from end-of-life wind turbine blades., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Graphene platelets (GPLs) are gaining popularity across various sectors for enhancing the strength and reducing the weight of structures, thanks to their outstanding mechanical characteristics and low manufacturing cost. Among many engineering structures, wind turbine blades are a prime candidate for the integration of such advanced nanofillers, offering potential improvements in the efficiency of energy generation and reductions in the construction costs of support structures. This study aims to explore the potential of GPLs for use in wind turbine blades by evaluating their impact on material costs as well as mechanical performance. A series of finite element analyses (FEAs) were conducted to examine the variations of mechanical performances-specifically, free vibration, bending, torsional deformation, and weight reductions relative to conventional fiberglass-based blades. Details of computational modeling techniques are presented in this paper. Based on the outcomes of these analyses, the mechanical performances of GPL-reinforced wind turbine blades are reviewed along with a cost-benefit analysis, exemplified through a 5MW-class wind turbine blade. The findings affirm the practicality and benefits of employing GPLs in the design and fabrication of wind turbine blades.

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1-20 Hz.

Although epoxy resin has been extensively used in the field of wind turbine blades, polyurethane has attracted much attention in recent years, due to its potential value of better fatigue resistance, lower processing viscosity and higher strength than epoxy resin blades. Herein, we construct a dense cross-linking structure in polyurethane (PU) based on different amounts of hydroxypropyl methacrylate (HPMA) with low processing viscosity and excellent mechanical properties. By increasing the content of HPMA, the thermal stability of PU is enhanced, but the micro-morphology does not change significantly. When the content of HPMA is 50 g (in 200 g copolymer), the PU sample PH-50 exhibits a viscosity of 70 MPa·s and a gelation time of 120 min at 25 °C, which is sufficient to complete processes like pouring and filling. By post-curing the PH-50 at 80 °C for 2 h, the heat distortion temperature can reach 72 °C, indicating the increase of temperature resistance. The PU copolymers also have excellent mechanical and dynamic thermo-mechanical properties due to the cross-linking structure between PU chains and poly-HPMA chains. Additionally, the PU copolymer has excellent compatibility with various glass fiber fabrics (GFF), showing a good match in the vacuum infusion experiment and great properties in the mechanical test. By compounding PH-50 with GFF, the composite with high strength is easily prepared for a wind turbine blade in various positions. The tensile strengths of the composites are all over 1000 MPa in the 0° direction. Such composites are promising for the future development of wind turbine blades that meet the stringent requirements for outstanding processing and mechanical properties.

Developing novel waste recycling strategies has become a feasible solution to overcome environmental pollution. In this work, a method of using waste wind turbine blade (WTB) as a carbon source to synthesize blue fluorescent carbon dots (B-CDs) by hydrothermal treatment is proposed. B-CDs are spherical and have an average particle size of 5.2 nm. The surface is rich in C-O, C=O, -CH 3 , and N-H bond functional groups, containing five elements: C, O, N, Si, and Ca. The optimal emission wavelength of B-CDs is 463 nm, corresponding to an excitation wavelength of 380 nm. Notably, a relatively high quantum yield of 29.9% and a utilization rate of 40% were obtained. In addition, B-CDs can serve as a photocatalyst to degrade methylene blue dye, with a degradation efficiency of 64% under 40-min irradiation conditions. The presence of holes has a significant influence on the degradation process., (© 2023 John Wiley & Sons Ltd.)

In this paper, a new concept of extra-durable and sustainable wind turbine blades is presented. The two critical materials science challenges of the development of wind energy now are the necessity to prevent the degradation of wind turbine blades for several decades, and, on the other side, to provide a solution for the recyclability and sustainability of blades. In preliminary studies by DTU Wind, it was demonstrated that practically all typical wind turbine blade degradation mechanisms (e.g., coating detachment, buckling, spar cap/shell adhesive joint degradation, trailing edge failure, etc.) have their roots in interface degradation. The concept presented in this work includes the development of bio-inspired dual-mechanism-based interface adhesives (combining mechanical interlocking of fibers and chemical adhesion), which ensures, on the one side, extra-strong attachment during the operation time, and on the other side, possible adhesive joint separation for re-use of the blade parts. The general approach and physical mechanisms of adhesive strengthening and separation are described.

Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is challenging. This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. The introduced faults in the wind turbine blades include surface erosion, cracked blade, mass imbalance, and twist blade fault. This data article serves as a valuable resource for validating condition monitoring methods in industrial wind turbine applications and facilitates a better understanding of vibration signal characteristics associated with different faults., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this document., (© 2023 The Author(s).)

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine's service life by improving the turbine blades' fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade's fatigue life is calculated using Morrow's equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade's fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.

To improve the quality and efficiency of robot grinding, a design and a control algorithm for a robot used for grinding the surfaces of large, curved workpieces with unknown parameters, such as wind turbine blades, are proposed herein. Firstly, the structure and motion mode of the grinding robot are determined. Secondly, in order to solve the problem of complexity and poor adaptability of the algorithm in the grinding process, a force/position hybrid control strategy based on fuzzy PID is proposed which greatly improves the response speed and reduces the error of the static control strategy. Compared with normal PID, fuzzy PID has the advantages of variable parameters and strong adaptability; the hydraulic cylinder used to adjust the angle of the manipulator can control the speed offset within 0.27 rad/s, and the grinding process can be carried out directly without obtaining the specific model of the surface to be machined. Finally, the experiments are carried out, the grinding force and feed speed are maintained within the allowable error range of the expected value, and the results verify the feasibility and effectiveness of the position tracking and constant force control strategy in this paper. The surface roughness of the blade is maintained within Ra = 2~3 μm after grinding, which proves that the grinding quality meets the requirements of the best surface roughness required for the subsequent process.

Due to its physical and mechanical properties, glass-fiber-reinforced polymer (GFRP) is utilized in wind turbine blades. The loads given to the blades of wind turbines, particularly those operating offshore, are relatively significant. In addition to the typical static stresses, there are also large dynamic stresses, which are mostly induced by wind-direction changes. When the maximum stresses resulting from fatigue loading change direction, the reinforcing directions of the material used to manufacture the wind turbine blades must also be considered. In this study, sandwich-reinforced GFRP materials were subjected to tensile testing in three directions. The parameters of the stress-strain curve were identified and identified based on the three orientations in which samples were cut from the original plate. Strain gauge sensors were utilized to establish the three-dimensional elasticity of a material. After a fracture was created by tensile stress, SEM images were taken to highlight the fracture's characteristics. Using finite element analyses, the stress-strain directions were determined. In accordance to the three orientations and the various reinforcements used, it was established that the wind turbine blades are operational.

An attempt at the treatment of the waste fiber (WF) from the wind turbine blade (WTB) was made through the modifier of dopamine hydrochloride and the compound modifier of dopamine hydrochloride and 2,5-dihydroxy terephthalic acid or 3,4-dihydroxy cinnamic acid or 3,4-dihydroxy benzonitrile, corresponding to obtain four modified waste fibers (MWF1, MWF2, MWF3, and MWF4). The MWFs samples' microstructure properties were characterized using SEM, EDS, XPS, FTIR analyses, and water contact angle tests. The results revealed that all the MWF surfaces were wrapped by a distinct coating layer and had different elemental compositions and chemical groups, demonstrating the significant effect of the four modifications on the WF surfaces. The hydroxyl, amino, or nitrile groups were grafted onto the WF surfaces causing improvement of the hydrophilicity and reactivity. Furthermore, all the MWFs as the reinforced materials were incorporated into the industrial waste phosphogypsum (PG) to manufacture the phosphorous-building gypsum composites (PBGC). The effects on the micro-morphology and mechanical properties of the PBGC were evaluated. The results also show the improvement in flexural and compressive strength with the addition of MWFs into the PBGC, due to the enhancement of the compactness between the MWF and phosphogypsum matrix. In particular, the effects of three compound modifiers on the flexural and compressive strength are more significant. The highest flexural and compressive strength was contributed by the PBGC-MWF4 with 2% dosage using a compound modifier of dopamine hydrochloride and 3,4-dihydroxy benzonitrile, which were enhanced 61.04% and 25.97% compared with the PBG.

The blade is a crucial part of large-scale wind turbine for converting wind energy into electricity. Increasing attentions have been paid to the blade health monitoring in recent years. This paper focuses on acoustic-based surface damage detection of the blade and presents a novel intelligent detection method of the whistle produced by the drain hold using the pattern recognition. In the algorithm, a developed preprocessing strategy with multiband adaptive spectral subtraction can well reduce the random wind noise from raw acoustic signal. Moreover, a correlated empirical mode decomposition method in combination with morphological filtering is proposed for extracting time-frequency ridge features of whistle event. Finally, an incremental support vector machine based on adaptive reserved set strategy is designed for recognizing the whistle event. Experimental results demonstrate that proposed method is feasible and effective in whistle detection of drain hole., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In life service, the wind turbine blades are subjected to compound loading: torsion, bending, and traction, all these resulting in the occurrence of normal and tangential stresses. At some points, the equivalent stresses, due to overlapping effects provided by normal and shear stresses, can have high values, close to those for which the structure can reach to the failure point. If the effects of erosion and clashes with foreign bodies are added, the structure of the blade may lose its integrity. Considering both the complex shape of the blade and internal structure used, the mechanical behavior of the blade, such as the rigidity and resistance along the length of the blade, are usually determined with some uncertainty. This paper presents the results obtained in the non-destructive tests at static torsion of a scalable wind turbine blade. The objective of the paper was to determine the variation of the equivalent stress in the most stressed points of the blade, in relation to the torques applied. To determine the points with the highest stress, a finite element analysis was performed on the scalable wind turbine blade. Electrotensiometric transducers were mounted at different points of the blade, determining the main stresses in the respective points, as well as their variation during the torsion test, by subsequent calculations. The determinations were performed by applying the torque in both senses, in relation to the blade axis, thus concluding the values of the equivalent stress in the two cases.

When designing a wind turbine, the main objective is to generate maximum effective power with the lowest possible production costs. The power of a wind turbine depends primarily on the aerodynamic properties of its blades. Moreover, the cost of making a blade for a wind turbine, and therefore also for the entire wind turbine, depends on the materials used for its production. Therefore, wind turbine blades are the most studied element of a wind turbine. By selecting the optimal material and geometric properties of the wind turbine blade, it is possible to reduce the costs of making the entire wind turbine. These rationales led the authors to investigate composite wind turbine blades. A two-criteria optimization task was formulated, which allowed for the simultaneous consideration of two criteria: minimizing the mass and minimizing the vertical deflection of the wind turbine blade. Geometric properties of the blade, influencing the considered criteria, were assumed as decision variables. The weighted sum method was used. The results obtained allowed us to determine the optimal geometric and material properties of a wind turbine blade.

The wind energy sector is growing rapidly. Wind turbines are increasing in size, leading to higher tip velocities. The leading edges of the blades interact with rain droplets, causing erosion damage over time. In order to mitigate the erosion, coating materials are required to protect the blades. To predict the fatigue lifetime of coated substrates, the Springer model is often used. The current work summarizes the research performed using this model in the wind energy sector and studies the sensitivity of the model to its input parameters. It is shown that the Springer model highly depends on the Poisson ratio, the strength values of the coating and the empirically fitted a2 constant. The assumptions made in the Springer model are not physically representative, and we reasoned that more modern methods are required to accurately predict coating lifetimes. The proposed framework is split into three parts-(1) a contact pressure model, (2) a coating stress model and (3) a fatigue strength model-which overall is sufficient to capture the underlying physics during rain erosion of wind turbine blades. Possible improvements to each of the individual aspects of the framework are proposed.

The leading edges of wind turbine blades are adhesively bonded composite sections that are susceptible to impact loads during offshore installation. The impact loads can cause localized damages at the leading edges that necessitate damage tolerance assessment. However, owing to the complex material combinations together with varying bondline thicknesses along the leading edges, damage tolerance investigation of blades at full scale is challenging and costly. In the current paper, we design a coupon scale test procedure for investigating bondline thickness effects on damage tolerance of joints after being subjected to localized impact damages. Joints with bondline thicknesses (0.6 mm, 1.6 mm, and 2.6 mm) are subjected to varying level of impact energies (5 J, 10 J, and 15 J), and the dominant failure modes are identified together with analysis of impact kinematics. The damaged joints are further tested under tensile lap shear and their failure loads are compared to the intact values. The results show that for a given impact energy, the largest damage area was obtained for the thickest joint. In addition, the joints with the thinnest bondline thicknesses displayed the highest failure loads post impact, and therefore the greatest damage tolerance. For some of the thin joints, mechanical interlocking effects at the bondline interface increased the failure load of the joints by 20%. All in all, the coupon scale tests indicate no significant reduction in failure loads due to impact, hence contributing to the question of acceptable localized damage, i.e., damage tolerance with respect to static strength of the whole blade.

The reliability of the wind turbine blade (WTB) evaluation using a new criterion is presented in the work. Variation of the ultrasonic guided waves (UGW) phase velocity is proposed to be used as a new criterion for defect detection. Based on an intermediate value between the maximum and minimum values, the calculation of the phase velocity threshold is used for defect detection, location and sizing. The operation of the proposed technique is verified using simulation and experimental studies. The artificially milled defect having a diameter of 81 mm on the segment of WTB is used for verification of the proposed technique. After the application of the proposed evaluation technique for analysis of the simulated B-scan image, the coordinates of defect edges have been estimated with relative errors of 3.7% and 3%, respectively. The size of the defect was estimated with a relative error of 2.7%. In the case of an experimentally measured B-scan image, the coordinates of defect edges have been estimated with relative errors of 12.5% and 3.9%, respectively. The size of the defect was estimated with a relative error of 10%. The comparative results obtained by modelling and experiment show the suitability of the proposed new criterion to be used for the defect detection tasks solving.

Structural health monitoring (SHM) has been increasingly exploited in recent years as a valuable tool for assessing performance throughout the life cycle of structural systems, as well as for supporting decision-making and maintenance planning. Although a great assortment of SHM methods has been developed, only a limited number of studies exist serving as reference basis for the comparison of different techniques. In this paper, the vibration-based assessment of a small-scale wind turbine (WT) blade is experimentally investigated, with the aim of establishing a benchmark case study for the SHM community. The structure under consideration, provided by Sonkyo Energy as part of the Windspot 3.5 kW WT model, is tested in both healthy and damaged states under varying environmental, that is, temperature, conditions as imposed by means of a climatic chamber. This study offers a thorough documentation of the configuration of this experimental benchmark, including the types of deployed sensors, the nature of excitation and available measurements, and the investigated damage scenarios and environmental variations enforced. Lastly, an overview of the raw and processed measurement data, made available to researchers via an open access Zenodo repository, is herein provided., (© 2020 The Authors. Structural Control and Health Monitoring published by John Wiley & Sons Ltd.)

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

The material selection process for producing a horizontal axis wind turbine blade for sustainable energy generation is a vital issue when using Nigeria as a case study. Due to the challenge faced with the low wind speed variations. However, this paper focuses on implementing MCDM for the material selection process for a suitable material for developing a horizontal wind turbine blade. This paper used a quantitative research approach using AHP and TOPSIS multi-criteria decision method. The study put into consideration the environmental conditions for the material selection process when designing the questionnaire. The authors extracted the data used for the selection process from the 130 research questionnaire distributed to materials engineers and renewable energy professionals. This research considered four alternatives that is, aluminum alloy, stainless steel, glass fiber, and mild steel to determine the best material for the wind turbine blade. Also, the model has four criteria and eight sub-criteria used for developing the pair-wise matrix and the performance score used for the ranking process of the alternatives. The result shows that a consistency index of 0.056 and a consistency ratio of 0.062 gotten via the AHP method is workable for material selection practice. 78%, 43%, 67%, and 25% are the performance scores for the four alternatives via the TOPSIS techniques. In conclusion, aluminum alloy is the best material, followed by glass fibre. Therefore, the decision-makers recommended aluminum alloy; hence, manufacturers should apply aluminum alloy to develop the wind turbine blade for sustainable energy generation., (© 2019 The Author(s).)

Wind turbine blades (WTB) mechanically recycled and used as a feedstock for thermoplastic composites. Physical properties (water sorption (WA), Thickness swelling (TS)) dataset of composites made from recycled wind turbine blades presented. Dataset also presented the influence of resin level, mill screen size and coupling agents on the physical properties of composites (Mamanpush et al., 2019).

The wind turbine blades (WTB) that face end-of-life was first mechanically milled and classified through a range of varying screen sizes. We then blended this with high density polyethylene (HDPE) thermoplastic resin and extruded it to a profiled composite. We determined the influence of refined particle size, resin content and coupling agents (maleic anhydride polyethylene (MAPE) and methacryloxypropyltriethoxysilane (Silane)) on the mechanical and CLTE properties of recycled composites (Mamanpush et al., 2019).

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

With an increase in renewable wind energy via turbines, an underlying problem of the turbine blade disposal is looming in many areas of the world. These wind turbine blades are predominately a mixture of glass fiber composites (GFCs) and wood and currently have not found an economically viable recycling pathway. This work investigates a series of second generation composites fabricated using recycled wind turbine material and a polyurethane adhesive. The recycled material was first comminuted via a hammer-mill through a range of varying screen sizes, resinated and compressed to a final thickness. The refined particle size, moisture content and resin content were assessed for their influence on the properties of recycled composites. Static bending, internal bond and water sorption properties were obtained for all composites panels. Overall improvement of mechanical properties correlated with increase in resin content, moisture content, and particle size. The current investigation demonstrates that it is feasible and promising to recycle the wind turbine blade to fabricate value-added high-performance composite., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This work proposes a novel hybrid signal processing technique to extract information on disbond-type defects from a single B-scan in the process of non-destructive testing (NDT) of glass fiber reinforced plastic (GFRP) material using ultrasonic guided waves (GW). The selected GFRP sample has been a segment of wind turbine blade, which possessed an aerodynamic shape. Two disbond type defects having diameters of 15 mm and 25 mm were artificially constructed on its trailing edge. The experiment has been performed using the low-frequency ultrasonic system developed at the Ultrasound Institute of Kaunas University of Technology and only one side of the sample was accessed. A special configuration of the transmitting and receiving transducers fixed on a movable panel with a separation distance of 50 mm was proposed for recording the ultrasonic guided wave signals at each one-millimeter step along the scanning distance up to 500 mm. Finally, the hybrid signal processing technique comprising the valuable features of the three most promising signal processing techniques: cross-correlation, wavelet transform, and Hilbert-Huang transform has been applied to the received signals for the extraction of defects information from a single B-scan image. The wavelet transform and cross-correlation techniques have been combined in order to extract the approximated size and location of the defects and measurements of time delays. Thereafter, Hilbert-Huang transform has been applied to the wavelet transformed signal to compare the variation of instantaneous frequencies and instantaneous amplitudes of the defect-free and defective signals., Competing Interests: The authors declare no conflict of interest.

The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency-frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency-MARSE, and average frequency-peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes., Competing Interests: The authors declare no conflict of interest.

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade., (Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.)

Although vibration monitoring is a popular method to monitor and assess dynamic structures, quantification of linearity or nonlinearity of the dynamic responses remains a challenging problem. We investigate the delay vector variance (DVV) method in this regard in a comprehensive manner to establish the degree to which a change in signal nonlinearity can be related to system nonlinearity and how a change in system parameters affects the nonlinearity in the dynamic response of the system. A wide range of theoretical situations are considered in this regard using a single degree of freedom (SDOF) system to obtain numerical benchmarks. A number of experiments are then carried out using a physical SDOF model in the laboratory. Finally, a composite wind turbine blade is tested for different excitations and the dynamic responses are measured at a number of points to extend the investigation to continuum structures. The dynamic responses were measured using accelerometers, strain gauges and a Laser Doppler vibrometer. This comprehensive study creates a numerical and experimental benchmark for structurally dynamical systems where output-only information is typically available, especially in the context of DVV. The study also allows for comparative analysis between different systems driven by the similar input.

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.