Search Results (2,814)

Reversible pump turbines (RPTs) play a key role in pumped hydro energy storage systems, where increasing grid flexibility requires frequent operation under off-design conditions. In turbine mode, deep partial load and no-load operation are often associated with severe flow instabilities, rotating stall, and strong rotor–stator interactions, which can limit operational flexibility and increase mechanical stress. Previous studies have shown that blade lean can influence hydrodynamic stability; however, its effect under no-load conditions remains insufficiently understood. In this work, the influence of runner blade lean on flow instabilities and rotor–stator interaction in a reversible pump turbine is numerically investigated. Two runner configurations, featuring a 0${}^{\circ }$ and a $-{15}^{\circ }$ blade lean angle, are analyzed through unsteady CFD simulations during the transition from deep partial load to no-load operation. The analysis focuses on flow field characteristics, blade loading, and the spectral content of pressure, torque, and radial forces. The results show that the negatively leaned runner significantly mitigates flow recirculation near the hub, reduces pressure and torque fluctuations, and strongly suppresses higher-order harmonic components associated with rotor–stator interaction. In particular, radial force amplitudes at blade-passing harmonics are substantially reduced under no-load conditions. These findings demonstrate that a negative blade lean improves hydrodynamic stability and reduces vibratory loads, contributing to the enhanced operational reliability of reversible pump turbines. Full article

Higher turbine inlet temperatures improve cycle efficiency but intensify blade thermal loading, so internal passages rely on turbulators that raise convection within coolant pressure budgets. Streamwise sinusoidal ribs introduce curvature and spanwise phasing beyond straight transverse bars, yet reconciled multi-row thermal–hydraulic data for such layouts in high-aspect-ratio blade-cooling analogues remain scarce. Steady three-dimensional computational fluid dynamics (CFD) of turbulent airflow in a 4:1 rectangular channel with uniform heat flux on one ribbed wall are applied to compare nine parametric sinusoidal-rib layouts and one transverse baseline at bulk Reynolds numbers from 20,000 to 90,000. The normalized Nusselt number ($Nu/N{u}_0$), Fanning friction factor ($f/f_0$), and composite thermal–hydraulic performance indices quantify the trade-off. Several layouts outperform the transverse baseline; a streamwise-increasing rib-height schedule achieves the highest pressure-drop-weighted index, whereas a large-amplitude uniform waviness gives the best heat-transfer-dominated index. The parametric matrix indicates when streamwise waviness merits further study in ribbed passage design. Full article

Turbine engine discs operate at high speeds with heavy loads. Any failure may result in the engine stopping or being destroyed. Therefore, it is necessary to check the normal modes and determine the rotational speeds at which they may occur. The aim of this article is to present a method of non-contact measurement of normal modes using the single and three-dimensional modes. The test element is the isolated first compressor stage of the DGEN-380 miniature jet engine (minijet). The disc has the shape of a hollow truncated cone with large blades. Vibration measurements were carried out in a non-contact manner using a scanning Doppler vibrometer. The measurement was made in 1D and 3D mode. The 1D mode is simpler and easier to prepare. In 3D mode, the calibration of three scanning heads significantly complicates the measurement preparation, but allows researchers to obtain the deformation in three-dimensional space The summary shows the measured frequencies using both modes. The shapes of deformation are also summarized. It is described how close the 1D measurement is to the 3D mode and in what frequency range. Finally, it is shown to what extent it is possible to describe the nature of structural oscillations in the 1D measurement mode. Full article

This perspective focuses on the field of solid waste recovery and resource utilization for end-of-life (EoL) wind turbine blades. Wind energy plays a central role in the global transition toward low-carbon energy systems owing to its technological maturity, scalability, and widespread resource availability. As global installed wind power capacity exceeded 1000 GW in 2024, improving the life-cycle energy efficiency and resource productivity of wind energy systems has become increasingly important. In this context, wind turbine blades (WTBs), the most material-intensive components with high embodied energy, are approaching large-scale end-of-life replacement, with global EoL blade waste projected to reach 2–4 million tons by 2030. Although blades may reach the end of their structural service life, they contain substantial quantities of reinforcing fibers and polymeric matrices that embody significant material and manufacturing energy. Integrating blade recycling into the wind energy value chain represents a critical opportunity to reduce dependence on energy-intensive virgin materials and lower life-cycle energy consumption and associated carbon emissions. However, the realization of energy-efficient circular utilization remains constrained by several challenges, including inefficient heat and mass transfer during blade depolymerization, limited valorization of resin-derived products, and performance degradation of recovered fibers. This perspective examines the material characteristics of blades from a life-cycle energy utilization standpoint, assesses existing recycling pathways, and identifies key technological and system-level bottlenecks. Emphasis is placed on process intensification, product upgrading, and design-for-circularity strategies to support the long-term sustainability of wind power systems. Full article

Wake interactions between wind turbines have a significant impact on the performance of downstream turbines and the overall efficiency of wind farms. In this study, wind tunnel experiments were carried out to investigate the wake characteristics of multiple wind turbines under different inflow conditions, upstream yaw angles, and turbine arrangements. The applicability of a previously proposed blade optimization method for reduced-scale wind turbine wake experiments was further assessed, and several wake velocity superposition models were evaluated. The results indicate that inflow turbulence intensity has a greater influence on wake recovery than inflow velocity and that increased turbulence intensity accelerates wake mixing and velocity recovery. Moreover, an appropriate upstream yaw angle and a staggered turbine arrangement can alleviate the wake deficit experienced by the downstream turbine. Additionally, the experimental data confirm that the optimized blade design method is effective for multi-turbine wake experiments. Among the models considered, the geometric sum model shows the best agreement with the experimental data under non-yaw conditions with small turbine spacing. The present study provides useful reference data for wind farm layout optimization and wake model development. Full article

This study examines the effects of jet Reynolds number (Re) and jet hole diameter (d) on flow and heat transfer in the leading-edge full-impingement cooling channel of a gas turbine nozzle guide vanes (NGV). Experiments via transient liquid crystal and numerical simulations were conducted. Results reveal that the peak Nusselt number (Nu) initially increases and then reaches a fixed value from root to tip in the spanwise direction. The area-averaged Nu presents the descending trend of the shower-head surface, pressure surface, and suction surface. In addition, the bleeding from film holes causes significant local flow acceleration and Turbulence Kinetic Energy (TKE) enhancement of 10.69%, resulting in local heat transfer elevation. The heat transfer enhancement region on both pressure and suction surfaces is inclined towards the shower-head at a 5% span region. Increasing the jet hole diameter (d) results in a decrease in both averaged Nu and TKE on the target surface. Simultaneously, the Nu gradient increases. When d = 1.6 mm, there is a recirculation zone near the hub on the suction surface and a strong crossflow near the hub on the pressure surface. The jet flow on the target surface is bending towards the shower-head. When d = 0.8 mm, the overall heat transfer is highest. However, considering heat transfer uniformity, a jet hole diameter of d = 1.2 mm offers better application. Full article

Small surface defects on large structures such as wind turbine blades, bridges, and pipelines pose significant safety risks if left undetected. Therefore, a laser triangulation system is designed for contactless surface geometry inspection from a flying drone at a working distance of 2 m. To enable single-shot triangulation measurements in dynamic aerial environments, a single-shot-capable approach is realized by means of a laser and a diffractive optical element for creating a dot-matrix illumination pattern and a camera for image recording. The setup, with 101 × 101 measurement points, is calibrated by using an interferometer as a reference, which shows a sub-pixel resolution capability. As a result, the depth resolution capability for each point amounts to 126 µm, while the lateral resolution capability is determined by the laser spots’ size of 0.6 mm and the spots’ interspacing of 1.75 mm. With the present configuration, unambiguous depth detection is possible for local surface gradients of up to 2.3 times the interspot distance between adjacent measurement points, and the field of view is 17.56 cm × 17.56 cm. Finally, surface defects with lateral sizes on the order of 1 cm and 0.5 cm are currently detectable, as is demonstrated by experimental results from in-flight measurements. Thus, the potential and challenges of single-shot laser triangulation for drone-based inspection in real-world scenarios are presented. Full article

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article

Radar technology in the microwave and millimeter-wave frequency range is the subject of current research for structural health monitoring of composite materials, e.g., damage detection in wind turbine blades. Performance assessment, enabling widespread practical application of this promising and non-contact sensing approach, can be realized via probability of detection (POD) theory, which is a statistical method for determining the detectability of damage through response metrics as a function of flaw size. This paper deals with the experimental investigation of a delamination model represented by two parallel glass fiber reinforced polymer plates separated from each other from $0\mathrm{mm}$ to $1\mathrm{mm}$ in steps of $0.01\mathrm{mm}$. Experimental studies with a frequency modulated continuous wave radar are performed under laboratory conditions in the frequency range from $57GHz$ to $65GHz$. The signal response is represented by two damage indicators (DIs), according to the root mean square deviation and Mahalanobis distance. Since the reflection of electromagnetic waves exhibits a nonlinear behavior, this also implies a nonlinear response in the DI characteristic. The novelties in this work are the successful implementation of a nonlinear regression model, combined with an optimal threshold decision through receiver operating characteristic curves for a high-resolution POD representation. The POD with $95\%$ confidence bounds indicates the flaw size at which the delamination can be detected reliably. Depending on the radar distance in experimental studies, the binary structural condition (damaged or undamaged) was correctly assessed from $95\%$ to $100\%$. The minimum detectable size ranges from $0.01\mathrm{mm}$ to $0.08\mathrm{mm}$. Full article

Pump-as-turbine (PAT) units have been widely used for energy recovery in water-supply networks, petrochemical systems, and small hydropower applications; however, their turbine-mode performance is often limited because most commercial pumps are originally designed for pumping conditions. To improve the hydraulic performance of a low-specific-speed PAT, this study developed a surrogate-assisted multi-objective optimization framework combining three-dimensional computational fluid dynamics (CFD), design of experiments, a Kriging surrogate model, and a multi-objective genetic algorithm. Five key impeller geometric parameters, including blade inlet angles, blade wrap angles, and impeller outlet diameter, were selected as design variables, and turbine-mode efficiency was maximized under a head constraint of H ≥ 24 m at the rated condition of 1450 r/min. The results showed that the optimized design increased efficiency from 72.34% to 84.42% while satisfying the head requirement. Comparative analyses of pressure and velocity fields in the impeller and volute further revealed that the performance improvement was mainly associated with enhanced flow-field uniformity and reduced local hydraulic losses. A dedicated PAT test rig was finally established to experimentally validate the optimized design. Full article

Effective fault diagnosis of wind turbine blades and rotating machinery is critical for ensuring operational reliability and reducing maintenance costs. This study introduces a healthy-reference modeling framework that combines physics-informed neural network (PINN) with deviation-based dynamic regime features for systematic fault detection. At first, healthy and faulty data are normalized, then PINN is trained solely on healthy data, creating a reference model that predicts normal behavior. Deviations between measured signals and the healthy-reference predictions are then analyzed to extract key dynamic regime features, including energy, stability, drift, intermittency, and persistence, capturing subtle variations caused by faults. An interpretable Support Vector Machine (SVM) classifier uses these features to identify fault types such as ball, inner race, outer race, crack, erosion, and unbalance. Classification is performed using dynamic feature combinations while energy is often used as the base feature. The result shows energy with persistence combination performance is better than other feature combinations, and fused features achieved higher accuracy for both datasets. The approach is validated on both bearing data and an experimental blade dataset, demonstrating strong performance across different mechanical systems. Comparative evaluation with three different approaches, including Cross-load Scalogram-based CNN, Spectrogram-based CNN, and Hybrid SVM, highlights that the proposed healthy reference framework offers a data-efficient, interpretable, and robust solution for fault detection. This work highlights the importance of modeling healthy dynamics before classification, capturing both how strong a fault is and how it behaves over time, which offers a practical approach for wind turbine condition monitoring with limited data. Full article

Wind turbine blades serve as the core components of wind energy conversion systems, and their safe and stable operation is pivotal to the operational efficiency and reliability of wind farms. However, prolonged operation in harsh environmental conditions such as strong winds, heavy rainfall, ultraviolet radiation, and temperature fluctuations renders wind turbine blades susceptible to fatigue damage and structural failure. Aiming at the drawbacks of traditional electromagnetic sensors, including their vulnerability to lightning strikes and poor corrosion resistance, as well as the elastic modulus mismatch between existing fiber Bragg grating (FBG)-encapsulated sensors and wind turbine blade structures, this study selects the ethylene–propylene–diene monomer (EPDM) as the encapsulation material to develop EPDM-FBG strain sensors. The effectiveness of the proposed sensor in blade strain monitoring is ultimately verified via static load model tests on small-scale wind turbine blades. Test results demonstrate that the EPDM-FBG strain sensor exhibits excellent static strain sensing performance, with its test results being highly consistent with those of bare FBG sensors and a relative error of less than 5%, which can fully meet the practical requirements of static strain monitoring for wind turbine blades. This research provides a novel and reliable monitoring method for the health monitoring of wind turbine blades. Full article

An efficient hybrid modeling framework is developed for the dynamic analysis of offshore wind turbines (OWTs) by coupling an aero–servo–elastic rotor–nacelle superelement with a hydroelastic substructure. The complex rotor–nacelle dynamics are condensed into a reduced-order 14-DOF representation through a modal-based multibody formulation, while retaining blade deformation, spinning effects, nonlinear aerodynamic loading, and active servo controls. Its interface compatibility at the nacelle enables the coupling with either numerical or physical substructures, establishing a unified basis for system hybrid formulation, co-simulations, and real-time hybrid simulations. The validity of the superelement is verified by comparing the resulting fully coupled modal model against OpenFAST, demonstrating high consistency in time-domain responses. As a demonstration, the verified superelement is further coupled with a 1D finite element model of the supporting structure (tower–monopile substructure) to form a hybrid model, enabling accurate force analysis of the OWT structure. Dynamic analyses of the IEA 10 MW OWT reveal that while the blade flapwise responses and the operation-related edgewise responses are 1P-dominated, tower side–side responses and idling-related tower fore–aft and blade edgewise responses manifest at their corresponding resonance frequencies. The maximum displacement and maximum bending moment envelopes vary monotonically with height. Instead, the maximum stress envelope possesses high values in the mid-lower sections of the tower. This high-stress region undergoes a spatial shift driven by the blade feathering mechanism. Full article

Wind turbine blades are prone to multiscale and weak-feature damage in complex natural environments. Accurate and efficient detection is crucial for ensuring the safe operation of wind turbine units. However, existing models struggle to balance detection precision, robustness, and lightweight deployment requirements. In this paper, we propose a lightweight model, Wind-YOLO, for wind turbine blade defect detection based on YOLOv11, with three core innovations: (1) We design a DynamicC3k2 that adaptively adjusts the convolutional receptive field for feature extraction, enhancing fine-grained feature capture of micro-cracks and weak-texture defects. (2) We construct a Cross-Stage Partial with Focused Linear Attention (C2FLA) that precisely focuses on defect regions via a linear attention mechanism, effectively mitigating complex background and noise interference. (3) We propose a Spatially Guided Gated Feature Pyramid Network (SGG-FPN) that optimizes multiscale feature transmission and aggregation through a gated fusion mechanism, improving adaptability to cross-scale defects from millimeter-level cracks to meter-level spalling. Extensive experiments on a dedicated wind turbine defect dataset show that Wind-YOLO achieves an mAP@0.5 of 80.9% and an mAP@0.5:0.95 of 37.1%, achieving an increase of 3.9 percentage points and 2.4 percentage points, respectively, compared with the baseline YOLOv11. Meanwhile, the model has only 2.34 million parameters (2.34 M) and a computational complexity of 6.0 GFLOPs. It delivers dual improvements in precision and lightweight performance, with superior environmental adaptability for real-time wind turbine inspection. Full article

As thermal power units in China shift toward serving as flexible regulation sources in new-type power systems, accurately assessing the axial thrust of steam turbine regulating stages under off-design conditions has become critical. This paper employs numerical methods to investigate the axial thrust characteristics and nozzle structural optimization of the regulating stage under off-design conditions (VWO, THA, 75% THA, 50% THA). Steady-state results reveal significant deviations in the interstage hub forces predicted by 3D simulations compared with those from the conventional 1D formula under partial admission, prompting a correction. Unsteady results show that reducing the partial admission degree intensifies flow unsteadiness, increasing rotor blade axial force fluctuation from 1175 N (VWO) to 2057 N (50% THA). In terms of structural optimization, compared with not increasing the nozzle angle, increasing the nozzle angle by 2° reduces the total axial force on the regulating stage by 7.3%; compared with not extending the inlet guide arc segment, extending its length by 40 mm increases the axial force on the rotor blade by 1.6%, but decreases the maximum amplitude from 323.9 to 249.9. Based on these findings, the optimization direction for the nozzle structure is proposed. Full article