Search Results (706)

Repurposing decommissioned wind turbine blades provides a vital pathway to mitigate carbon emissions, yet the escalating volume of large-scale waste poses a severe environmental challenge. Recognizing the limitation that existing research focuses predominantly on small-scale legacy blades, this study addresses this gap by assessing the mechanical properties and microstructure of a 54-m (2.0 MW) blade decommissioned due to repowering after 10 years of service. GFRP samples extracted from the root, mid-span, and tip were investigated using X-ray computed tomography and a comprehensive suite of mechanical tests. The investigation confirmed a low internal porosity (~1.2%) without service-induced macroscopic interfacial cracking, alongside superior residual performance, exemplified by a tensile strength of 849.5 MPa at the root. Statistical analysis employing ANOVA revealed significant spatial variations, supporting a graded reuse strategy: roots with superior tensile strengths for critical members, mid-spans for axial compression, and tips as a reliable property baseline for general reuse, while Weibull analysis verified the statistical reliability required for structural design. Based on these superior residual properties, a raft-type wave energy converter utilizing repurposed blade segments was proposed. A comparative carbon footprint assessment revealed that this blade-repurposed WEC achieved a 71.5% reduction in carbon emissions and a 37.4% reduction in structural mass compared to conventional steel counterparts. These findings substantiate the viability of large-scale DWTBs as high-value resources for decarbonizing marine infrastructure within a circular economy. Full article

Vertical axis wind turbines (VAWTs) are promising systems for urban wind energy applications because of their compact layout, omni-directional operation, and favorable integration potential. However, their broader deployment remains limited by poor self-starting capabilities and relatively low aerodynamic efficiency compared to horizontal axis wind turbines. In this study, a passive flow control concept for a straight-bladed VAWT is numerically investigated using a NACA0012 airfoil modified with 45° inclined perforations on the extrados. Four perforated configurations were generated and compared with the baseline profile through a two-stage computational approach. First, steady 2D computational fluid dynamics (CFD) simulations of the isolated airfoils were performed at a free stream velocity of 12 m/s over an angle of attack range of 0–180°. Subsequently, the most relevant aerodynamic trends were assessed at rotor level using transient 2D Moving Mesh simulations for a three-bladed wind turbine with tip speed ratios (TSRs) between 0.5 and 3.5. All perforated variants exhibited higher lift than the baseline airfoil, while the configuration with smaller, denser perforations distributed over the downstream two-thirds of the extrados provided the best overall aerodynamic performance. At TSR = 2.5, this geometry increased the mean moment coefficient from 0.044 to 0.0525 and the power coefficient from 0.109 to 0.131, corresponding to an increase in power output of approximately 20%. These results indicate that inclined extrados perforations constitute a promising passive strategy for improving the aerodynamic performance of small straight-bladed VAWTs, although further 3D and experimental validations are required. Full article

This study develops and evaluates three complementary predictive modeling frameworks for gearbox oil temperature forecasting: Stochastic Differential Equation (SDE) modeling with iterative Markov correction, multi-objective genetic algorithm-enhanced grey modeling (MOGA-GM(1,N)), and multi-output Gaussian Process Regression (MO-GPR). The study used supervisory control and data acquisition (SCADA) data from a 1.5 MW wind turbine gearbox, comprising 14 temperature measurements spanning 789 operational hours. The SDE framework partitions temperature evolution into deterministic aging effects and stochastic environmental perturbations, achieving a fitting accuracy of 2.5% and testing accuracy of 8.0% after thirty iterative corrections. The MOGA-GM(1,N) approach optimizes weight coefficients through the dual objective of minimizing the posterior difference ratio and maximizing small error probability, attaining first-class accuracy classification ($C=0.06$; $P=0.99$) while identifying mechanical loads and rotational speeds as dominant thermal drivers. MO-GPR demonstrates competitive performance with uncertainty quantification capabilities, achieving RMSE values of 2.51–7.48 depending on training SCADA data proportions. Comparative analysis shows that the iteratively refined SDE methodachieves the best prediction accuracy in this case study for continuous thermal trajectory forecasting, while MOGA-GM(1,N) excels at wear source diagnostics and operational factor analysis. The proposed framework addresses persistent challenges in wind turbine condition monitoring, including extreme nonlinearity, discontinuous data, and unpredictable thermal spikes. The results suggest potential for implementation in preventive maintenance systems, enabling timely intervention before critical thermal thresholds that precipitate component failure. Full article

Deep learning has become an important tool for wind power forecasting because it can help improve wind energy utilization and support reliable grid-connected operation. For wind farms, accurate turbine-level forecasting depends on spatial interactions among turbines and temporal evolution of historical operating data. In this study, a spatio-temporal forecasting framework is developed by combining a Graph Attention Network with a Temporal Convolutional Network. The graph attention module describes the neighborhood relations among turbines and learns their influence strengths adaptively, while the temporal convolution module extracts temporal patterns from multivariate SCADA sequences for multi-step prediction. On this basis, the learned attention weights are further used to define a node influence metric. This makes it possible to identify a small set of key turbines and use only their historical data to predict the future power output of the whole wind farm. The proposed framework is evaluated using one year of SCADA data from 134 turbines. A sliding-window dataset is constructed, and the model is tested on the training, validation, and test sets. The results show that the method can capture the spatio-temporal dependencies within the wind farm and still provide effective farm-wide forecasting when only limited observation nodes are available. The value of this work lies in organizing existing techniques around a practical wind farm forecasting task and in providing an interpretable prediction strategy based on key turbine selection, rather than in proposing a fundamentally new theoretical model. Full article

The implementation of small-scale renewable energy systems in remote island environments poses unique engineering and logistical challenges. This case study examines the installation of a 1 kW vertical-axis wind turbine (VAWT) in Pulau Tioman, Malaysia, to enhance localized energy generation in coastal regions. Constraints such as limited heavy lifting equipment, reliance on basic machinery, and restricted transport required modular handling, manual assembly, and terrain-adapted foundations. Environmental conditions, including humidity, rainfall, and irregular wind patterns, influenced planning and execution. While the installation was successfully completed, prolonged low-wind conditions prevented post-installation performance monitoring. This study documents the installation challenges, engineering adaptations, and lessons learned, offering guidance for future renewable energy deployments in Southeast Asia island environments. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

This paper investigates the electromagnetic performance of an outer-rotor spoke-type permanent magnet synchronous generator intended for small wind turbine applications below 5 kW. The study focuses on the influence of structural ferromagnetic components on magnetic flux distribution and overall machine performance. The generator was initially designed and optimized using 2D finite element analysis, followed by a comprehensive 3D model to account for axial flux leakage and structural details; particular attention was given to the fastening screws used. Experimental validation on a dedicated laboratory test bench confirms the accuracy of the 3D model, mainly at lower wind speeds. The results highlight the necessity of including structural components in three-dimensional electromagnetic modeling for accurate performance prediction of flux-concentrating wind turbine generators. Full article

Wind energy has become an essential resource for the development and diversification of the energy sector in México and worldwide. In this context, the mechanical design of turbine blades has emerged as a priority research topic, given its impact on performance and viability. The present research evaluates the aero-structural response of multiple lay-up configurations of a 6 m blade by coupling computational fluid dynamics (CFD) and finite element analysis (FEA). The fluid–structure interaction (FSI) was simulated in ANSYS, a commercial software chosen for its capacity for multivariable analysis. The nominal operating conditions included a wind speed of 10.5 m/s and a rotational speed of 100 rpm, leading to a theoretical power output of 6591 W. For the proposed lay-up configurations, the Tsai-Wu and Puck (Global IRF) criteria were estimated and remained below the critical threshold of 1.0, indicating no risk of structural failure. However, some carbon fiber/epoxy layers, including unidirectional layers in the spar caps and bidirectional layers in the structural shear web, may present failure risks under extreme loading conditions. This applies to configurations with the lowest number of layers in the mid-span spar caps; this fact is reinforced by the main effects analysis. The results emphasize the relevance of conducting comprehensive composite failure evaluations to optimize material selection and structural design, even for small-scale blades. Full article

Coastal wind turbines operate under severe salt spray, high humidity, and wind-driven erosion, which accelerate coating degradation and corrosion-induced cracking. In such environments, corrosion defects exhibit blurred boundaries, weak textures, and significant scale variations, challenging object detectors in small-target localization and precise boundary regression. To address these limitations, this study proposes CoastCor-Net, an enhanced YOLOv11-based framework that improves spatial–semantic alignment, boundary representation, and channel–spatial dependency modeling. The architecture integrates three complementary modules to enhance boundary sensitivity, spatial–semantic consistency, and cross-channel interaction: a Decoding-Driven Enhancement Block, a Complementary Feature Alignment Module, and a Channel-Transposed Coordinate Attention module. Extensive experiments on the Wind Turbine Blade Damage Dataset show that CoastCor-Net achieves 84.7% mAP@0.5 and 54.1% mAP@0.5:0.95, surpassing YOLOv13n by 3.2 percentage points in mAP@0.5 and improving AP_damage by 5.2 percentage points. The framework also demonstrates strong robustness under composite coastal perturbations. These findings highlight the practical effectiveness of structured multi-level feature enhancement for reliable and high-precision blade inspection in complex coastal environments. Full article

The structural segmentation of wind turbine blades offers advantages in transportation, manufacturing, and maintenance; however, it introduces interfaces that may compromise load transfer and fatigue performance. This study presents the experimental and numerical validation of a composite coupling system designed for small wind turbine blades compliant with IEC 61400-2 requirements. A 2 m representative section extracted from the mid-span region of a 9 m blade was manufactured using vacuum-assisted resin infusion and tested under static loading conditions. A detailed finite element model based on classical laminate theory and orthotropic material properties was developed to predict structural response. Experimental measurements showed a maximum tip deflection of 15 mm under the applied load, compared to 13.76 mm predicted numerically, corresponding to a deviation of 8.9%. Surface strain measurements obtained from eight strain gauges installed across the blade–coupling interface indicated maximum mean values of +632.4 με in tension and −664.2 με in compression, with no evidence of localized strain amplification at the instrumented locations. These findings demonstrate that fully composite permanent segmentation can preserve stiffness continuity while maintaining strain levels below reported fatigue initiation thresholds, supporting the structural feasibility of segmented blade architectures for small wind turbine applications. Full article

In this paper, the authors developed a data-driven model to classify radar measurements into precipitation (P) and non-precipitation (NP) echoes using the Random Forest machine learning algorithm. Dual-polarimetric radar variables and their local variability exhibit distinctive characteristics between P and NP echoes. The authors found that using larger search window sizes generally improves classification accuracy, though it involves a trade-off: while it helps eliminate small clusters of NP echoes, it may also suppress weak precipitation signals near storm edges. Incorporating multiscale local variability estimates computed with varying window sizes further enhances classification performance by capturing spatial-scale-dependent features characteristic of P and NP echoes. The main model uses radar variables obtained from a single scan and demonstrates consistent performance across all distances from the radar. This consistency allows reliable use of the model out to 230 km—the maximum range at which dual-polarimetric variables are used for rainfall estimation from NEXRAD radars—without significant degradation in accuracy due to range effects. Supplementing the model with independent information from GOES-16 infrared channel products further improves classification by helping to eliminate localized NP echoes remaining after the main model, particularly those caused by wind turbines that mimic precipitation in dual-polarimetric signatures. This is based on the tendency of water vapor and/or raindrops to absorb terrestrial radiation, thereby lowering brightness temperatures. A practical challenge remains near the radar, where the sampling volume is small and signal processing (e.g., sidelobe impact and ground clutter suppression) can distort radar measurements. The under-detection of precipitation in these regions is likely due to such corrupted data. This issue may be mitigated by adopting a hybrid scan strategy—such as a Constant Altitude Plan Position Indicator (CAPPI)—specifically for regions close to the radar. Full article

With the growth of urban zones and the increasing need for energy, the use of renewable energy solutions in the built environment becomes a must. Due to their small size and the ability to capture wind from any direction, vertical-axis wind turbines are an alternative to conventional wind energy generators. However, the use of these turbines in the built environment faces difficulties due to performance inefficiencies, particularly because of the intricate aerodynamic characteristics of the blades. This work investigates a method for increasing the efficiency of VAWTs by addressing blade-to-blade interactions using Computational Fluid Dynamics simulations. The research aims to improve turbine design for urban locations, which motivates the application context of the study. The present numerical model employs a uniform inflow to isolate blade–blade interaction mechanisms under controlled conditions. The paper presents a design that minimizes aerodynamic losses, decreases turbulence-induced drag, and increases overall energy capture efficiency by modeling different blade configurations and their interactions. The performance of four asymmetric configurations of blade chord and radius was numerically studied and compared to a symmetric configuration. Full article

For geotechnical structures with a strict control requirement of deformation, the high modulus and non-linear attenuation characteristics of the surrounding soil under small-strain conditions cannot be ignored during performance evaluation; the HSS constitutive model offers significant advantages over conventional approaches (e.g., Mohr–Coulomb) to describe the above soil behaviors. In this study, the theoretical framework of the HSS model, i.e., the yield function, hardening laws, and flow rule, is first elucidated. Subsequently, it is numerically implemented into the finite element software FssiCAS. The reliability of the FssiCAS software (Version 3.5) incorporating the HSS model is validated through a triaxial test and a physical test involving the horizontal loading of the monopile. Finally, taking the four-monopile jacket foundation of an offshore wind turbine (OWT) in Lianjiang County, China, as a representative, the HSS model is adopted to describe the mechanical behaviors of a seabed foundation. The horizontal bearing characteristics of the jacket foundation–seabed system under multi-angle horizontal loading are investigated, and the influence of the horizontal loading angle on the horizontal bearing capacity, jacket displacement, and seabed deformation is quantitatively elucidated. The results indicate that (1) the horizontal bearing capacity of the jacket is minimal when horizontal loading is along the diagonal of the four piles, representing the most severe loading case, and therefore, the horizontal bearing capacity of the jacket foundation–seabed system should be evaluated based on this case; and (2) the FE software FssiCAS has good reliability when dealing with pile–soil interaction problems involving complex geometries and complex mechanical behaviors of seabed soils. This study could provide technical support and an analysis platform for the design of jacket foundations for complex marine structures, such as OWTs. Full article

The suction pile–gravity hybrid foundation (SPGH) has emerged as a novel foundation for floating wind turbines (FWTs) due to its superior bearing mechanism. In harsh marine environments, offshore wind turbine structures endure multidirectional wave–wind current loads, which are transmitted through mooring systems as complex multidirectional coupled loads (horizontal, vertical, bending moments, and torque), imposing severe challenges to the bearing capacity. Therefore, this study carries out 3D finite element simulations, utilizing the Hardening Soil–Small Strain constitutive model to simulate the stress–strain behavior of sand, to systematically investigate the failure modes and bearing capacity of SPGH foundations. The method underlying the failure envelope theory is proposed, applicable to tension-leg mooring systems (dominated by uplift and lateral loads) and catenary mooring systems (dominated by compression and lateral loads). Results indicate that under pure vertical uplift or torque loading, both SPGH and traditional SP foundations exhibit typical interfacial shear failure modes. Under pure horizontal or bending moment loading, SPGH and SP foundations exhibit rotational instability failure. The direction of vertical load has a significant impact on the bearing performance of SPGH foundations. In addition, horizontal load can increase its vertical uplift-bearing capacity by 46% and torque capacity by 48%. The enhancement effect of the bending moment load is more significant, and can increase the vertical uplift-bearing capacity by 115% and the torque-bearing capacity by 112%, respectively, while vertical downward loads within a certain range significantly improve horizontal and bending-bearing performance. Full article

Seabed response and local scouring around pile groups under combined wave–current loading pose critical threats to the stability and long-term performance of offshore structures, particularly those supporting offshore renewable energy infrastructures. In this study, we present a systematic experimental investigation on the pore-water pressure and local scour around pile groups subjected to regular waves, combined regular wave–current conditions, and irregular waves generated using the JONSWAP spectrum under wave-only conditions. Pore-water pressures and seabed morphology were analyzed for different hydrodynamic conditions, pile spacings, and pile arrangements. The experimental results demonstrate that the presence and magnitude of current are the dominant factors controlling scour development. Increasing the current velocity from 0 to 0.25 m/s leads to a three (3) to five (5) times increase in maximum scour depth, whereas comparable variations in wave height and wave period produce relatively small effects. The direction of a current affects the location of maximum scour, with the wave–forward current condition promoting the development of an interconnected scour area within the pile array and wave–opposing current condition, shifting local scour toward downstream piles. Small-spaced piles ($G/D$ = 1) intensify hydrodynamic interactions and increase scour depth by approximately 30–40% compared with wider spacing. Irregular waves generate more spatially distributed but shallower scour than regular waves of comparable wave characteristics. These findings provide insights into the mechanisms governing seabed instability around pile group foundations and contribute to more sustainable design and operation of offshore infrastructure, such as offshore wind turbine foundations. Full article