Search Results (1,850)

To address the low efficiency of corn straw collection, this study aims to optimize the design of the straw shredding mechanism of corn straw harvesters. A multi-blade arrangement shredding mechanism was designed, with ANSYS 2022 employed for gas-phase flow field simulation of the pick-up and fan conveying chambers, and a multi-field coupled simulation was conducted to evaluate performance using pick-up rate and qualified cutting length rate as metrics. Field tests were carried out to validate the simulation results. The results show that the DC-type pick-up (symmetrically arranged Y-shaped and hammer claw blades) exhibited optimal performance. At a travel speed of 1.2 m/s and rotational speed of 2100 r/min, the pick-up rate and qualified cutting length rate reached 93.62% and 93.94%, respectively, in field tests (81.34% pick-up rate in simulation); its maximum collection efficiency reached 92.98% under the conditions of fan 1 speed of 2300 r/min, fan 2 speed of 4600 r/min, and single feed rate of 9.4 kg. All pick-up types had maximum forces below the stress limit (348 MPa), meeting operational requirements. This research provides reliable references for the design and optimization of corn straw returning machines and verifies the accuracy of the simulation method. Full article

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

The noise generated by wind turbines is a critical issue that impacts both operational efficiency and public health, necessitating a comprehensive investigation into its sources and propagation. This study investigates the near-wake noise of an S-airfoil horizontal-axis wind turbine using statistically optimized near-field acoustic holography (SONAH) with a 60-channel rotating microphone array in an open-jet wind tunnel. The results show that the noise in the wake is predominantly caused by the rotation of the rotor. The position of the highest sound pressure level concentration is at 0.78R of the blade under different operating conditions within the rotor’s rotation plane. The sound pressure level radiates outward in a spiral pattern across eleven identified sections, progressively decreasing with distance. The most significant attenuation occurs between 0.04 m and 0.06 m from the rotating surface. This study provides foundational insights into the near-field acoustic characteristics of wind turbines, serving as a valuable reference for noise reduction strategies and environmental impact assessments in wind energy projects. Full article

In conventional band sawing, the long-span compression of the flexible saw blade often results in large fluctuations in cutting force, low cutting efficiency, and poor force predictability. To address these issues, this study investigates the dynamic cutting force modeling and experimental validation of ultrasonic vibration-assisted band sawing using 304 stainless steel as the workpiece material. Based on an analysis of the band sawing mechanism, an ultrasonic vibration-assisted approach is proposed to modify the contact conditions between the saw blade and the workpiece. A dynamic model of the saw blade is established using the string vibration equation, and a multi-tooth dynamic cutting force prediction model is further developed by incorporating variable cutting depth characteristics under ultrasonic vibration. Comparative experiments are conducted between conventional sawing and ultrasonic vibration-assisted sawing to validate the proposed model. At feed rates of 0.1–0.4 mm/s and preload values of 0.1–0.5 mm, the proposed model predicts dynamic cutting forces with good agreement to experimental results, achieving an average relative error of 5.44%. Under typical cutting conditions for difficult-to-machine materials, ultrasonic vibration-assisted sawing reduces the average cutting force and feed force by approximately 15% and 18%, respectively, while decreasing surface roughness along the feed direction by about 21%, thereby improving sawing efficiency and surface quality. Full article

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

Biomimetic microstructured surfaces offer a promising passive strategy for drag reduction in marine and aerospace applications. This study employs computational fluid dynamics (CFD) simulations to systematically investigate the drag reduction performance and mechanisms of groove-type microstructures, addressing both geometry selection and dimensional optimization. Three representative geometries (V-groove, blade-groove, and arc-groove) were compared under identical flow conditions (inflow velocity 5 m/s, Re = 7.5 × 10⁵) using the shear-stress-transport (SST k-ω) turbulence model, and the third-generation Ω criterion was employed for threshold-independent vortex identification. The results establish a clear performance hierarchy: blade-groove achieves the highest drag reduction rate of 18.2%, followed by the V-groove (16.5%) and arc-groove (14.7%). The analysis reveals that stable near-wall microvortices form dynamic vortex isolation layers that separate the high-speed flow from the groove valleys, with blade grooves generating the strongest and most fully developed vortex structures. A parametric study of blade-groove aspect ratios (h⁺/s⁺ = 0.35–1.0) further demonstrates that maintaining h⁺/s⁺ ≥ 0.75 preserves effective vortex-isolation layers, whereas reducing h⁺/s⁺ below 0.6 causes vortex collapse and performance degradation. These findings establish a comprehensive design framework combining geometry selection (blade-groove > V-groove > arc-groove) with dimensional optimization criteria, providing quantitative guidance for practical biomimetic drag-reducing surfaces. Full article

In recent years, new methods to reuse, repurpose, recycle, and recover decommissioned wind turbine blades (dWTBs) have actively been developed in the wind industry. In this study, the authors address the scientific challenge of repurposing decommissioned wind turbine blades for earthwork applications, particularly as part of retaining structures. A gravity retaining structure made entirely from recycled materials is introduced, consisting of glass fibre-reinforced polymer (GFRP) composite modular units derived from dWTBs. To improve the structure’s sustainability, a mixture of typical sand and lightweight waste materials is considered for filling and backfilling of the GFRP units. In particular, two waste materials are examined—a polymer foil derived from recycled laminated glass and tyre-derived aggregate (TDA) in the form of rubber powder—which are incorporated into the sand matrix in typical dry mass proportions ranging from 2% to 32% and 5% to 20%, respectively, reflecting practical ranges considered in geotechnical backfill applications. The research involved material testing of all recyclates and their mixtures with standard sand, as well as two-dimensional finite-element (2D FE) analysis of a retaining structure using the determined material properties. To facilitate the real-world implementation of this novel technology, a structure was designed to account for ground conditions at a specific site to protect against an existing landslide. In summary, this study presents the concept of a sustainable retaining structure along with results from material tests and an initial design for implementation, supported by FE analysis of overall stability. Full article

Background/Objectives: Effective airway management during pediatric cardiopulmonary resuscitation (CPR) is crucial but technically challenging, especially during continuous chest compressions. While direct laryngoscopy with Macintosh (MAC) or Miller (MIL) blades remains the standard, optical devices such as the VieScope (VSL) may enhance performance under dynamic resuscitation conditions. This study compared first-pass success and intubation time, as well as procedural difficulty and glottic visualization, of MAC, MIL, and VSL during simulated pediatric cardiopulmonary resuscitation. Methods: This prospective, randomized crossover simulation study involved 53 medical students. Participants performed endotracheal intubation on a high-fidelity manikin simulating a 5-year-old pediatric patient using MAC, MIL, and the Bebé VieScope laryngoscope. Each technique was evaluated in two scenarios: with and without continuous chest compressions. Results: Without chest compressions, first-pass success (FPS) and intubation time varied significantly between techniques. VSL achieved the highest FPS (100%; p = 0.032) and the shortest intubation time (27.9 ± 9.2 s; p = 0.040), performing faster than MIL and achieving higher FPS than MAC. Visualization quality, ease of intubation, and optimization maneuvers were similar across techniques. During continuous chest compressions, all outcomes differed significantly. FPS increased from MAC to MIL and VSL (p = 0.001), with MAC showing the lowest success rate. VSL showed the shortest intubation time (35.9 ± 13.0 s; p < 0.001), better glottic visualization, easier intubation, and fewer optimization maneuvers, followed by MIL. Conclusions: In this simulated pediatric cardiac arrest model, the VieScope laryngoscope demonstrated superior overall performance, especially during uninterrupted chest compressions. Optical tubular laryngoscopy may therefore provide clinically relevant benefits in pediatric resuscitation where maintaining high-quality chest compressions is crucial. Given the manikin-based design of this study, confirmation of these findings in clinical pediatric cardiac arrest settings will require further prospective clinical investigation. Full article

Targeted coverage of crushed grass segments under the fruit tree canopy synergistically achieves the agronomic goals of soil moisture conservation, weed suppression, and soil fertility improvement. To address issues like incomplete grass cutting and high risk of damaging fruit trees in complex orchard environments with traditional mowing devices, a lateral grass discharge blade for orchard mowers was designed. Based on airflow field theory, the dynamic basis of the airflow field, critical conditions for carrying crushed grass segments, and their movement laws on the blade and in the air were analyzed to identify key factors affecting discharge. CFD simulations were conducted using the Flow Simulation module of SolidWorks 2021 to explore the effects of the blade airfoil’s long side, short side lengths, and horizontal included angle on the outlet velocity and outlet volumetric flow rate of crushed grass segments, determining the reasonable parameter range. With these three as test factors and the two indicators above, orthogonal tests and parameter optimization were performed via Design-Expert 13.0 software, yielding optimal parameters: long side 125 mm, short side 35 mm, horizontal included angle 60°, corresponding to 9.105 m/s outlet velocity and 0.045 m³/s volume flow rate. A prototype mowing device with these parameters was fabricated for orchard field tests. Results show an average stubble stability coefficient of 94.2%, average over-stubble loss rate of 0.39%, and crushed grass segment distribution variation coefficient of 23.8%, meeting orchard mower operation requirements and providing technical support for orchard weed mowing, coverage, and utilization. Full article

This paper presents a novel hypersector-based method with Fuzzy Learning Vector Quantization (FLVQ) for the real-time classification of wind turbine blade defects using data acquired by unmanned aerial vehicles (UAVs). Unlike conventional prototype-based FLVQ approaches that rely on Euclidean distance in the feature space, the proposed method models each defect class as a hypersector on an n-dimensional hypersphere, where class boundaries are defined by angular similarity and fuzzy membership transitions. This geometric reinterpretation of FLVQ constitutes the core innovation of the study, enabling improved class separability, robustness to noise, and enhanced interpretability under uncertain operating conditions. Feature vectors extracted via the pre-trained SqueezeNet convolutional network are normalized onto the hypersphere, forming compact directional clusters that serve as the geometric foundation of the FLVQ classifier. A fuzzy softmax membership function and an adaptive prototype-updating mechanism are introduced to handle class overlap and improve learning stability. Experimental validation on a custom dataset of 900 UAV-acquired images achieved 95% classification accuracy on test data and 98.3% on an independent dataset, with an average F1-score of 0.91. Comparative analysis with the classical FLVQ prototype demonstrated superior performance and noise robustness. Owing to its low computational complexity and transparent geometric decision structure, the developed model is well-suited for real-time deployment on UAV embedded systems. Furthermore, the proposed hypersector FLVQ framework is generic and can be extended to other renewable-energy diagnostic tasks, including solar and hydropower asset monitoring, contributing to enhanced energy security and sustainability. Full article

Unsteady flow-induced pressure fluctuations and the consequent dynamic stresses in pump-turbines are critical determinants of their operational reliability and fatigue resistance. This investigation systematically examines the spatiotemporal propagation of Rotor–Stator Interaction (RSI)-induced pressure pulsations and evaluates the corresponding dynamic stress mechanisms based on a phase-resolved fluid–structure interaction strategy. The results reveal a significant hydrodynamic duality: RSI pressure waves manifest as convective traveling waves on the pressure side but as modal standing waves on the suction side. Crucially, a severe spanwise phase mismatch is identified between the hub and shroud streamlines, which induces a periodic hydrodynamic torsional moment on the blade. Due to the rigid constraint at the blade–crown junction, this torsional tendency is restricted, resulting in high-amplitude constrained tensile stresses at the root. This explains why the stress concentration at the crown inlet is significantly higher than in other regions. Additionally, the stress spectrum shows strong load dependence, characterized by low-frequency modulations on the suction side under high-load conditions. Full article

This study presents the first computational investigation of hybrid propeller configurations that combine toroidal and conventional blade geometries. Using Delayed Detached Eddy Simulation (DDES) with the Shear Stress Transport (SST) $k-\omega$ model for flow analysis and the Ffowcs Williams and Hawkings (FW–H) formulation for aeroacoustic prediction, five hybrid propeller designs were evaluated: a baseline model and four variants with modified loop-element spacing. The results show that the V-Gap-S configuration achieves the highest figure of merit (FM), producing over 10% improvement in propeller performance relative to the baseline, while also exhibiting the lowest turbulence kinetic energy (TKE) levels across multiple radial planes. Aeroacoustic analysis reveals quadrupole-like directivity for primary tonal noise, primarily driven by blade tip–vortex interactions, with primary tonal noise strongly correlated with thrust. Broadband noise and overall sound pressure level (OASPL) exhibited dipole-like patterns, influenced by propeller torque and FM, respectively. Comparisons of surface pressure, vorticity, and time derivatives of acoustic pressure further elucidate the mechanisms linking blade spacing to aerodynamic loading and noise generation. The results demonstrate that aerodynamic performance and aeroacoustics are strongly coupled and that meaningful noise reduction claims require performance conditions to be matched. Full article

Large-diameter butterfly valves are essential control components in high-flow hydraulic systems, where blade geometry directly impacts operational reliability, energy efficiency, and lifecycle cost. This study presents an integrated structural–hydraulic optimization of a DN3000 Boving butterfly valve blade rated for a maximum operating pressure of 10 bar with comparative analysis of a conventional flat blade and an optimized curved blade. The work applies a CFD–FEM framework specifically to DN3000 Southern African valves, which is rare in the literature. Numerical simulations evaluated stress distribution, deformation, pressure losses, and flow stability under design and hydrostatic test conditions. The curved blade achieved a 58.6% reduction in peak von Mises stress, a 50% reduction in weight, a 22% reduction in load loss, and a 33% reduction in actuation torque requirements, while maintaining seal integrity. Cost analysis revealed a 50% reduction in material costs and simplification of manufacturing. The results confirm that the introduction of curvature significantly improves structural strength and hydraulic efficiency, thus providing a reproducible framework for the design of lighter and more economical valves in hydropower, municipal and industrial applications. Full article

To address the complex surface curvature, massive dimensions, and variable pitch angles of wind turbine blades, this paper proposes a climbing robot design based on a variable-cell mechanism. By dynamically adjusting the support span and body posture, the robot adapts to the geometric features of different blade regions, enabling stable and efficient non-destructive inspection operations. Two reconfigurable configurations—a planar quadrilateral and a regular hexagon—are proposed based on the geometric characteristics of different blade regions. The configuration switching conditions and multi-leg cooperative control mechanisms are investigated. Through static stability margin analysis, the stable gait space and maximum stride length for each configuration are determined, optimizing the robot’s motion performance on surfaces with varying curvature. Simulation and experimental results demonstrate that the proposed multi-configuration gait planning strategy exhibits excellent adaptability and climbing stability across segments of varying curvature. This provides a theoretical foundation and methodological support for the engineering application of robots in wind turbine blade maintenance. Full article