Search Results (2,421)

The wind energy sector is experiencing substantial growth, with global wind turbine capacity increasing and projected to expand further in the coming years. However, rain erosion on the leading edges of turbine blades remains a significant challenge, affecting both aerodynamic efficiency and structural longevity. The associated degradation reduces annual energy production and leads to high maintenance costs due to frequent inspections and repairs. To address this issue, researchers have developed numerical models to predict blade erosion caused by water droplet impacts. This study presents a finite element analysis model in Abaqus to simulate the interaction between a single water droplet and wind turbine blade material. The novelty of this model lies in evaluating the influence of several parameters on von Mises and S33 peak stresses in the leading-edge protection, such as friction coefficient, type of contact, impact velocity, and droplet diameter. The findings provide insights into optimising LEP numerical models to simulate rain erosion as closely as possible to real-world scenarios. Full article

Nickel-based superalloys are widely utilized in critical hot-end components, such as aeroengine turbine blades, owing to their exceptional high-temperature strength, creep resistance, and oxidation resistance. During service, these components are frequently subjected to complex localized loading, leading to non-uniform plastic deformation and microstructure evolution within the material. Combining nanoindentation experiments with the crystal plasticity finite element method (CPFEM), this study systematically investigates the effects of loading rate and crystal orientation on the elastoplastic deformation of DD6 alloy under spherical indenter loading. The results indicate that the maximum indentation depth increases and hardness decreases with prolonged loading time, exhibiting a significant strain rate strengthening effect. The CPFEM model incorporating dislocation density effectively simulates the nonlinear characteristics of the nanoindentation process and elucidates the evolution of dislocation density and slip system strength with indentation depth. At low loading rates, both dislocation density and slip system strength increase with loading time. Significant differences in mechanical behavior are observed across different crystal orientations, which correspond to the extent of lattice rotation during texture evolution. For the [111] orientation, crystal rotation is concentrated and highly regular, while the [001] orientation shows uniform texture evolution. This demonstrates that anisotropy governs the deformation mechanism through differential slip system activation and texture evolution. Full article

This study analyzes the impact of geometric variations induced by the manufacturing process on the aerodynamic efficiency of an airfoil used in the design of a 3 kW wind turbine blade. For this purpose, a computational fluid dynamics (CFD) analysis was implemented, and the results were compared with those obtained using QBlade software. After blade fabrication, experimental evaluation was performed using the laser triangulation technique, enabling the reconstruction of the deformed airfoils and their comparison with the original geometry. Additional CFD simulations were carried out on the manufactured airfoil to quantify the loss of aerodynamic efficiency due to geometrical deformations. The results show that the geometric deviations significantly affect the aerodynamic coefficients, generating a decrease in the lift coefficient and an increase in the drag coefficient, which negatively impacts the airfoil aerodynamic efficiency. A 14.9% reduction in the rotor power coefficient was observed with the deformed airfoils compared to the original design. This study emphasizes the importance of quality control in wind turbine blade manufacturing processes and its impact on turbine power performance. In addition, the findings can contribute to the development of design compensation strategies to mitigate the adverse effects of geometric imperfections on the aerodynamic performance of wind turbines. Full article

Wind turbine blades face significant challenges from stochastic wind loads, impacting structural integrity. Traditional analysis often isolates Computational Fluid Dynamics (CFD) from Building Information Modeling (BIM) in the design process. This study bridges this gap by integrating BIM forward design with CFD simulation. A universal BIM modeling framework is developed for rapid blade modeling, which is compatible with ANSYS Workbench 2022 R1 through intermediate format conversion. The influence of wind load on the blades under various wind speed conditions is analyzed, and the results indicate a significant correlation between wind load intensity and blade structural response. The maximum windward pressure reaches 4.96 kPa, while the leeward suction peaks at −6.28 kPa. The displacement at the tip and middle part of the blades significantly increases with the increase in wind speed. The growth rate of displacement between adjacent speeds rises from 1.20 to 1.94, and the overall increase rate within the entire range rises from 1.02 to 4.16. These results demonstrate the feasibility of using BIM forward design in accurate performance analysis, and also extends the value of BIM in wind energy. Furthermore, a bidirectional information flow is established, where BIM provides geometry for CFD, and simulation results will inform BIM design refinement. Full article

A jet flow mixer is a novel agitator type widely used in the industry. However, scientific research has yet to be conducted on this impeller type. In this study, six types of fluids with various properties widely used in the paint industry were chosen to calculate the positioning of the jet flow mixer in the tank. Calculations were performed using computational fluid dynamics (CFD) software and validated using literature data. Simulations were conducted to consider the inside of the jet flow mixer and the inside of the tank. The initial calculations made for jet flow mixers allowed the determination of volume flow and power numbers for three types of mixers (propeller agitator and Pitched Blade Turbine with three and four blades). Those parameters were then used in subsequent calculations, obtaining the optimal inclination angle of the agitator and power consumption for each considered case. The jet flow mixer with a propeller impeller positioned at an angle of 45° proved to be the choice to achieve the best results. Full article

Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background interference. To address this, based on the high-speed detection model YOLOv10-N, this paper proposes a novel detection model named MMC-YOLO. First, the Multi-Scale Perception Gated Convolution (MSGConv) Module was designed, which constructs a full-scale receptive field through multi-branch fusion and channel rearrangement to enhance the extraction of geometric asymmetry features. Second, the Multi-Scale Enhanced Feature Pyramid Network (MSEFPN) was developed, integrating dynamic path aggregation and an SENetv2 attention mechanism to suppress background interference and amplify damage response. Finally, the Channel-Compensated Filtering (CCF) module was constructed to preserve critical channel information using a dynamic buffering mechanism. Evaluated on a dataset of 4818 wind turbine blade damage images, MMC-YOLO achieves an 82.4% mAP [0.5:0.95], representing a 4.4% improvement over the baseline YOLOv10-N model, and a 91.1% recall rate, an 8.7% increase, while maintaining a lightweight parameter count of 4.2 million. This framework significantly enhances geometric asymmetry defect detection accuracy while ensuring real-time performance, meeting engineering requirements for high efficiency and precision. Full article

This study investigates the downstream wake characteristics of a helical hydrokinetic turbine through combined experimental and numerical analyses. A four-bladed helical turbine with a 20 cm rotor diameter and blockage ratio of 53.57% was tested in an open water channel under a flow rate of 180 m³/h, corresponding to a Reynolds number of approximately 90 × 10³. Velocity measurements were collected at 13 downstream cross-sections using an Acoustic Doppler Velocimeter, with each point sampled repeatedly. Standard error analysis was applied to quantify measurement uncertainty. Complementary numerical simulations were conducted in ANSYS Fluent using a steady-state k-ω Shear Stress Transport (SST) turbulence model, with a mesh of 4.7 million elements and mesh independence confirmed. Velocity deficit and turbulence intensity were employed as primary parameters to characterize the wake structure, while the analysis also focused on the recovery of cross-sectional velocity profiles to validate the extent of wake influence. Experimental results revealed a maximum velocity deficit of over 40% in the near-wake region, which gradually decreased with downstream distance, while turbulence intensity exceeded 50% near the rotor and dropped below 10% beyond 4 m. In comparison, numerical findings showed a similar trend but with lower peak velocity deficits of 16.6%. The root mean square error (RMSE) and mean absolute error (MAE) between experimental and numerical mean velocity profiles were calculated as 0.04486 and 0.03241, respectively, demonstrating reasonable agreement between the datasets. Extended simulations up to 30 m indicated that flow profiles began to resemble ambient conditions around 18–20 m. The findings highlight the importance of accurately identifying the downstream distance at which the wake effect fully dissipates, as this is crucial for determining appropriate inter-turbine spacing. The study also discusses potential sources of discrepancies between experimental and numerical results, as well as the limitations of the modeling approach. Full article

Double-suction centrifugal pumps are extensively employed in industrial applications owing to their high efficiency, low vibration, superior cavitation resistance, and operational durability. This study analyzes how impeller oblique cutting angles (0°, 6°, 9°, 12°) affect a double-suction pump at a fixed 4% trimming ratio and constant average post-trim diameter. Numerical simulations and tests reveal that under low-flow (0.7Q_d) and design-flow conditions, the flat-cut (0°) minimizes reflux ratio and maximizes efficiency by aligning blade outlet flow with the mainstream. Increasing oblique cutting angles disrupts this alignment, elevating reflux and reducing efficiency. Conversely, at high flow (1.3Q_d), the 12° bevel optimizes outlet flow, achieving peak efficiency. Pressure pulsation at the volute tongue (P11) peaks at the blade-passing frequency, with amplitudes significantly higher for 9°/12° bevels than for 0°/6°. The flat-cut suppresses wake vortices and static–rotor interaction, but oblique cutting angle choice critically influences shaft-frequency pulsation. Entropy analysis identifies the volute as the primary loss source. Larger oblique cutting angles intensify wall effects, increasing total entropy; pump chamber losses rise most sharply due to worsened outlet velocity non-uniformity and turbulent dissipation. The flat-cut yields minimal entropy at Q_d. These findings provide a basis for tailoring impeller trimming to specific operational requirements. Furthermore, the systematic analysis provides critical guidance for impeller trimming strategies in other double-suction pumps and pumps as turbines in micro hydropower plants. Full article

Offshore wind turbine structures (OWTs) commonly use monopile foundations for support, and long-term exposure to wind–wave cyclic loads may induce changes in foundation stiffness. Variations in foundation stiffness can significantly alter the inherent vibration characteristics of OWTs, potentially leading to amplified vibrations or resonant conditions. In this study, a numerical model considering soil–pile interaction was developed on the FLAC3D platform to analyze the natural frequency of OWTs under long-term cyclic loading. The study first validated the numerical model’s effectiveness through comparison with measured data; a degradation stiffness model (DSM) was then embedded to assess how prolonged cyclic loading affects the degradation of foundation stiffness. A series of parametric studies were conducted in medium-dense and dense sand layers to investigate natural frequency alterations induced by prolonged cyclic loading. Finally, a simplified method for evaluating long-term natural frequency changes was established, and a 3.6 MW offshore wind turbine case was used to reveal the evolution characteristics of its natural frequency under long-term cyclic loads. The data reveal that the natural frequency of the structure undergoes a downward tendency as cyclic loading and frequency increase. To ensure long-term safe operation, the designed natural frequency should preferably shift toward 3P (where P is the blade rotation frequency). Full article

This research investigates enhancing the performance of an Archimedes screw-type hydrokinetic turbine (ASHT). A 3D transient computational model employing the six degrees of freedom (6-DOF) methodology within the ANSYS Fluent software 2022 R1, was selected for this purpose. A central composite design (CCD) methodology was applied within the response surface methodology (RSM) to enhance the turbine’s power coefficient ($C_p$). Key independent factors, including blade length (L), blade inclination angle ($\gamma$), and external diameter ($D_e$), were systematically varied to determine their optimal values. The optimization process yielded a maximum $C_p$ of 0.337 for L, $\gamma$, and $D_e$ values of 168.921 mm, 51.341°, and 245.645 mm, respectively. Experimental validation was conducted in a hydraulic channel, yielding results that demonstrated a strong correlation with the numerical predictions. This research underscores the importance of geometric design optimization in improving the energy capture efficiency of the ASHT, contributing to its potential viability as a competitive renewable energy solution in the pre-commercial phase of development. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article

GTD 111 has been employed in first-stage blades in different gas turbines. The study of microstructural evolution is essential for the lifetime assessment and development of turbine blades. The microstructural stability of a 130 MW gas turbine first-stage blade at 800 °C was studied. The microstructure’s evolution was analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculation. As thermal exposure time increases, the shape of γ′ precipitates changes from square to spherical. During thermal exposure, MC particles formed and coarsened along the grain boundaries, and primary MC carbide decomposed into the η phase and M₂₃C₆. The stability of MC carbide at the grain boundaries was lower than that within the grains. MC carbide precipitated at the grain boundaries tends to grow along the boundaries and eventually forms elongated carbide. High-resolution transmission electron microscopy (HRTEM) images indicate that the orientation of the γ′ precipitate changes during the coarsening process. The GTD 111 alloy can be deformed through dislocation shearing at 800 °C. The hardness value initially increases, then decreases with further exposure, which is related to the reduced precipitation strengthening by γ′ precipitates and the reduction in the hardness of the γ matrix. Full article

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

A standard approach to design begins with scaling up state-of-the-art machines to new target dimensions, moving towards larger rotors with lower specific energy to maximize revenue and enable power production in lower wind speed areas. This trend is particularly crucial in floating offshore wind in the Mediterranean Sea, where the high levelized cost of energy poses significant risks to the sustainability of investments in new projects. In this context, the conventional approach of scaling up machines designed for fixed foundations and strong offshore winds may not be optimal. Additionally, modern large-scale wind turbines for offshore applications face challenges in achieving high aerodynamic performance in thick root regions. This study proposes a holistic optimization framework that combines multi-fidelity analyses and tools to address the new challenges in wind turbine rotor design, accounting for the novel demands of this application. The method is based on a modular optimization framework for the aerodynamic design of a new wind turbine rotor, where the cost function block is defined with the aid of a model reduction strategy. The link between the full-order model required to evaluate the target rotor’s performance, the physical aspects of blade aerodynamics, and the optimization algorithm that needs several evaluations of the cost function is provided by the definition of a surrogate model (SM). An intelligent SM definition strategy is adopted to minimize the computational effort required to build a reliable model of the cost function. The strategy is based on the construction of a self-adaptive, automatic refinement of the training space, while the particular SM is defined by the use of stochastic radial basis functions. The goal of this paper is to describe the new aerodynamic design strategy, its performance, and results, presenting a case study of a 15 MW wind turbine blades optimized for specific deepwater sites in the Mediterranean Sea. Full article