Search Results (249)

This research presents an experimental analysis of the influence of atmospheric pressure plasma on the performance of a micro horizontal-axis wind turbine blade. The investigation was conducted using an NACA 4412 airfoil equipped with a dielectric barrier discharge (DBD) plasma actuator. The electrodes were configured asymmetrically, with a 2 mm gap and copper electrodes that are 0.20 mm in thickness. A high voltage of 6 kV was applied, resulting in a current of 0.071 mA and a power output of 0.426 W. Optical emission spectroscopy identified the excited components through the interaction of the high-voltage AC electric field with air molecules: ${\mathrm{N}}_2$, ${\mathrm{N}}_2^{+}$, ${\mathrm{O}}_2^{+}$, and O. The electrohydrodynamic force mainly results from the observed charged ions that, when accelerated by the electric field, transfer momentum to neutral molecules via collisions, leading to the formation of the observed jet plasma. The findings indicated a notable enhancement in aerodynamic performance attributable to the electrohydrodynamic (EHD) flow generated by the plasma. The estimated electrohydrodynamic force ($8.712\times {10}^{-4}$ N) is capable of maintaining the flow attached to the airfoil surface, thereby augmenting flow circulation and, consequently, enhancing the lift force. According to blade element theory, the lift and drag coefficients directly influence the torque and mechanical power generated by the wind turbine rotor. Schlieren imaging was utilized to observe alterations in air density and flow patterns. Lissajous curve analysis was used to examine the electrical discharge behavior, showing that only 7.04% of the input power was converted into heat. This indicates that nearly all input electric energy was transformed into EHD force by the atmospheric pressure plasma. Compared to traditional aerodynamic control methods, DBD actuators are a feasible alternative for small wind turbines due to their lightweight design, absence of moving parts, ability to be surface-embedded without altering blade geometry, and capacity to generate active, dynamic flow control with reduced energy consumption. Full article

Growing interest in organic food, along with European regulations limiting chemical usage, and the declining effectiveness of herbicides due to weed resistance, are all contributing to the growing trend towards mechanical weeding. For mechanical weeding to be effective, tools must pass near the crops in both the inter- and intra-row areas. The use of AI-based computer vision can assist in detecting crop lines and accurately guiding weeding tools. Additionally, AI-driven image analysis can be used for selective intra-row weeding with mechanized blades, distinguishing crops from weeds. However, until now, there have been two separate systems for these tasks. To enable simultaneous in-row weeding and row alignment, YOLOv8n and YOLO11n were trained and compared in a lettuce field (Lactuca sativa L.). The models were evaluated based on different metrics and inference time for three different image sizes. Crop lines were generated through linear regression on the bounding box centers of detected plants and compared against manually drawn ground truth lines, generated during the annotation process, using different deviation metrics. As more than one line appeared per image, the proposed methodology for classifying points in their corresponding crop line was tested for three different approaches with different empirical factor values. The best-performing approach achieved a mean horizontal error of 45 pixels, demonstrating the feasibility of a dual-functioning system using a single vision model. Full article

This study explores the effectiveness of 3D Digital Image Correlation (DIC) for measuring displacement and strain of a propeller undergoing angular motion. Traditional methods, such as strain gauges, face limitations including physical interference, technical difficulties in sensor connections, and restricted measurement points, leading to inaccuracies in capturing true conditions. To overcome these challenges, this research utilizes non-contact 3D DIC technology, enabling measurement of surface displacements and deformations without interfering with the tested component. Experiments were conducted using the model aircraft propellers mounted on a custom-built test stand for partial angular motion. The 1 Mpx high-speed cameras captured strain and displacement data across the propeller blades during motion. The DIC strain measurements were then compared to strain gauge data to evaluate their accuracy and reliability. The results demonstrate that 3D DIC enables precise displacement measurements, while strain measurements are subject to certain limitations. Displacement measurements were achieved with a noise level of ±10 μm, while strain measurement noise ranged from 26 to 174 µm/m depending on direction. Strain gauge measurements were also performed for verification of the DIC measurements and calibration of the filtering procedure. Two types of non-metallic materials were used in the study: Nylon LGF60 PA6 for the propeller and 3D-printed PC ABS for the cantilever beam used in strain measurement validation. This study underscores the potential of DIC for monitoring rotating components, with a particular focus on measuring strains that are often overlooked in publications addressing similar topics. Additionally, it focuses on comparing DIC strain measurements with strain gauge data on rotating components, addressing a critical gap in existing literature, as strain measurement in rotating structures remains underexplored in current research. Full article

Although the ultimate drivers of bat fatalities at wind turbines are still not well understood, the foraging behavior of insectivorous bats puts them at increased risk of collision with rotating blades. Wind energy facilities are commonly located in agriculture fields where bats can exploit periodic superabundant insect emergence events in the late summer and early autumn. Thermal imaging, acoustic monitoring, and bat carcass stomach content analyses show that bats prey upon insects on and near wind turbine towers. Studies have shown a positive association between insect abundance and bat activity, including in agricultural systems. We conducted a systematic review of bat diets for four common bat species in the Midwest and northern Great Plains to synthesize existing knowledge across species, assess the extent to which these bat focal species consume crop pests, and evaluate the potential for crop pest emergence models to predict temporal and spatial patterns of bat fatalities in this region. Big brown bats and eastern red bats consumed a variety of crop pests, including some for which emergence models may be available. In contrast, there were few studies for hoary bats or silver-haired bats, and the dietary evidence available has insufficient taxonomic resolution to conclude that crop pests were consumed. To augment existing data and illuminate relationships, we recommend that genetic diet analyses for bats, specifically hoary and silver-haired, be conducted in the late summer and autumn in this region. The results of these studies may provide additional candidate insect models to evaluate for predicting bat fatalities at wind turbines and clarify if the superabundant insect emergence hypothesis warrants further investigation. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

The inclusion of an inducer is an effective approach to improve the cavitation performance of centrifugal pumps, significantly influencing both the internal flow characteristics and the external performance of the pumps. This study examines a miniature high-speed centrifugal pump (MHCP) using numerical simulations based on the k-ε turbulence model, comparing the cases with an inducer and without one. Experimental tests on the pump’s external performance are conducted and flow visualization images are presented to validate the findings. The effects of the inducer on the tip leakage backflow, cavitation performance, and external pump performance are analyzed. The results show that the inducer provides pre-pressurization of the fluid, leading to a higher circumferential velocity at the impeller inlet and a reduced inlet flow angle. This allows for a reduction in the impeller blade inlet angle, resulting in smoother flow streamlines inside the impeller. Moreover, the inducer helps to suppress local low-pressure regions caused by the vortex and cavities generated by the interaction between the tip clearance backflow and the main flow, thereby mitigating cavitation in the non-blade zone. Within the investigated operating range, the pump with an inducer exhibits a significantly improved external hydraulic performance, including an increased head and efficiency, a reduced required net positive suction head (NPSHr), and a broader stable operating range. Full article

This paper explores the potential of the airbrushing method as a novel and cost-effective method for producing uniform titanium dioxide (TiO₂) films, crucial for enhancing the efficiency of dye-sensitized solar cells. The techniques performed were SEM and EDS images, OCP curves, photochronoamperometry, j-V curves, and impedance spectroscopy. Comparative analysis with the doctor blade methodology has noted a higher uniformity compared to the AB method, with the ability to improve the charge transportation and PCE (1.987%) and reduce the recombination process in the TiO₂/electrolyte interface (ԏe = 0.012 s). Insights from EIS spectroscopy and intensity-modulated spectroscopy offer mechanistic elucidations of the enhanced performance. Overall, this study highlights airbrushing as a promising approach for advancing the development of high-performance solar energy systems. Full article

Non-synchronous vibration (NSV), flutter, or rotating stall can cause severe blade vibrations and limit the operating range of compressors and fans. To enhance the understanding of these phenomena, this study investigated the corresponding mechanisms in modern composite ultra-high-bypass-ratio (UHBR) fans based on the ECL5/CATANA test campaign. Extensive steady and unsteady instrumentation such as stereo-PIV, fast-response pressure probes, and rotor strain gauges were used to derive the aerodynamic and structural characteristics of the rotor at throttled operating conditions. The study focused on the analysis of the transition region from transonic to subsonic speeds where two distinct phenomena were observed. At transonic design speed, rotating stall was encountered, while NSV was observed at 90% speed. At the intermediate 95% speedline, a peculiar behavior involving a single stalled blade was observed. The results emphasize that rotating stall and NSV exhibit different wave characteristics: rotating stall comprises lower wave numbers and higher propagation speeds at around 78% rotor speed, while small-scale disturbances propagate at 57% rotor speed and lock-in with blade eigenmodes, causing NSV. Both phenomena were observed in a narrow range of operation and even simultaneously at specific conditions. The presented results contribute to the understanding of different types of operating range-limiting phenomena in modern UHBR fans and serve as a basis for the validation of numerical simulations. Full article

Condition monitoring and fault detection in wind turbines are essential for reducing repair and maintenance costs. Early detection of faults enables timely interventions before the damage worsens. However, existing methods often rely on costly scheduled inspections or lack the ability to effectively detect early stage damage, particularly under different operational speeds. This article presents a methodology based on convolutional neural networks (CNNs) and empirical mode decomposition (EMD) of vibration signals for the detection of blade crack damage. The proposed approach involves acquiring vibration signals under four conditions: healthy, light, intermediate, and severe damage. EMD is then applied to extract time–frequency representations of the signals, which are subsequently converted into images. These images are analyzed by a CNN to classify the condition of the wind turbine blades. To enhance the final CNN architecture, various image sizes and configuration parameters are evaluated to balance computational load and classification accuracy. The results demonstrate that combining vibration signal images, generated using the EMD method, with CNN models enables accurate classification of blade conditions, achieving 99.5% accuracy while maintaining a favorable trade-off between performance and complexity. 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

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

Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades. Full article

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

Given the exceptional capabilities of femtosecond laser processing in achieving high-precision ablation for air-film cooling hole fabrication on turbine blades, it is imperative to develop an advanced monitoring methodology that enables real-time feedback control to automatically terminate the laser upon complete penetration detection, thereby effectively preventing backside damage. To tackle this issue, a spectrum-domain coherent imaging technique has been developed. This innovative approach adapts the fundamental principle of fiber-based Michelson interferometry by integrating the air-film hole into a sample arm configuration. A broadband super-luminescent diode with a 830 nm central wavelength and a 26 nm spectral bandwidth serves as the coherence-optimized illumination source. An optimal normalized reflectivity of 0.2 is established to maintain stable interference fringe visibility throughout the drilling process. The system achieves a depth resolution of 11.7 μm through Fourier transform analysis of dynamic interference patterns. With customized optical path design specifically engineered for through-hole-drilling applications, the technique demonstrates exceptional sensitivity, maintaining detection capability even under ultralow reflectivity conditions (0.001%) at the hole bottom. Plasma generation during laser processing is investigated, with plasma density measurements providing optical thickness data for real-time compensation of depth measurement deviations. The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications. Full article

Background/Objectives: Advancements in neuronavigation and intraoperative imaging have made gross-total resection of deep-seated lesions more feasible. However, in eloquently located regions, brain shift can lead to unintentional damage of functionally critical tissue during the approach. This study analyzes the feasibility and outcomes of a stereotactically guided microsurgical approach supported by intraoperative CT (iCT) for such lesions. Methods: Patients with deep-seated, eloquently located lesions treated between 03/2017 and 04/2023 at the Department of Neurosurgery, Ludwig-Maximilians-University (LMU) Munich, Germany, were included. Frame-based, image-guided stereotaxy was used for trajectory planning and catheter placement, verified by iCT. Microsurgical resection was conducted along the catheter trajectory using 2 mm conical blade retractors and continuous neurophysiological monitoring. Postoperative MRI assessed the extent of resection. Neurological outcomes were evaluated postoperatively, at 6 weeks, and at long-term follow-up in 12/2023. Results: A total of 12 patients were treated using the stereotactically guided microsurgical approach described in this study. In all cases, the implanted catheter precisely matched the preoperative trajectory, as confirmed by fused iCT data. Median durations were 23 min for stereotaxy and 3 h 7 min for microsurgery. Complete resection was achieved in all cases. One patient experienced transient hemiparesis and aphasia, both of which were fully resolved. All other patients showed neurological improvement or remained seizure-free at long-term follow-up. Conclusions: In selected cases, a stereotactically guided microsurgical approach with iCT enabled intraoperative localization of the target with high spatial accuracy and without immediate procedure-related complications in this limited cohort. Our findings support the feasibility of the technique; however, conclusions regarding clinical efficacy or broader applicability are limited by the small sample size and non-comparative study design. Full article