Search Results (157)

TC17 titanium alloy is widely used in the aviation industry for dual-performance blades, and linear friction welding (LFW) is a key technology for its manufacturing and repair. However, accurate evaluation of the mechanical properties of TC17−LFW joints and research on their joint fracture behavior are still not clear. Therefore, this paper used the finite element numerical simulation method (FEM) to investigate the mechanical behavior of the TC17−LFW joint with a complex micro−structure during the tensile processing, and predicted its mechanical properties and fracture behavior. The results indicate that the simulated elastic modulus of the joint is 108.5 GPa, the yield strength is 1023.2 MPa, the tensile strength is 1067.5 MPa, and the elongation is 1.98%. The deviations from measured results between simulated results are less than 2%. The stress and strain field studies during the processing show that the material located at the upper and lower edges of the joint in the WZ experiences stress and strain concentration, followed by the extending of the stress and strain concentration zone toward the center of the WZ. And finally, the strain concentration zone covered the entire WZ. The fracture behavior studies show that the material necking occurs in the TMAZ of TC17(α + β) and WZ, while cracks first appear in the WZ. Subsequently, joint cracks propagate along the TC17(α + β) side of the WZ until fracture occurs. There are obvious tearing edges formed by the partial tearing of the WZ structure in the simulated fracture surface, and there are fracture surfaces with different height differences at the center of the joint crack, indicating that the joint has mixed fracture characteristics. Full article

Single crystal nickel-based superalloys are extensively used in turbine blade applications due to their superior creep resistance compared to their polycrystalline counterparts. With the high creep resistance, high cycle fatigue (HCF) and low cycle fatigue (LCF) become primary failure mechanisms for such applications. This study investigates the fatigue life prediction of CMSX-4 using a combination of crystal plasticity and lifetime assessment models. The constitutive crystal plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while the modified Chaboche damage model is used for lifetime assessment, focusing on cleavage stresses on active slip planes to include anisotropy. Both qualitative and quantitative data obtained from HCF experiments on single crystal superalloys with notched geometry were used for validation of the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy of lifetime predictions across varying temperatures by analyzing the fatigue curves obtained from the damage model. The integration of crystal plasticity, damage mechanics, and ANNs resulted in an accurate prediction of fatigue life and crack initiation points under complex loading conditions of single crystals superalloys. Full article

The shift towards renewable energy, particularly wind energy, is rapidly advancing globally, with Southeastern Europe and Croatia, in particular, experiencing a notable increase in wind turbine construction. The frequent exposure of wind turbine blades to environmental stressors and operational forces requires regular inspections to identify defects, such as erosion, cracks, and lightning damage, in order to minimize maintenance costs and operational downtime. This study aims to develop a machine learning model using convolutional neural networks to simplify the defect detection process for wind turbine blades, enhancing the efficiency and accuracy of inspections conducted by drones. The model leverages transfer learning on the YOLOv7 architecture and is trained on a dataset of 231 images with 246 annotated defects across eight categories, achieving a mean average precision of 0.76 at an intersection over the union threshold of 0.5. This research not only presents a robust framework for automated defect detection but also proposes a methodological approach for future studies in deep learning for structural inspections, highlighting significant economic benefits and improvements in inspection quality and speed. Full article

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller. Full article

With the growing demand for wind energy, the development of advanced materials for wind turbine support structures and blades has garnered significant attention in both industry and academia. In previous research, the authors investigated the incorporation of graphene platelets (GPLs) into wind turbine blades, focusing on the structural performance and cost-effectiveness relative to conventional fiberglass composites. These studies successfully demonstrated the potential advantages of GPL reinforcement in improving blade performance and reducing the blade’s weight and costs. Building upon prior work, the present study conducts a detailed investigation into the static bending behavior of GPL-reinforced wind turbine blades, specifically examining the impact of crack location and length. A finite element model of the SNL 61.5 m wind turbine blade was rigorously developed and validated through comparison with the existing literature to ensure its accuracy. Comprehensive parametric analyses were performed to assess deflection under various crack lengths and positions, considering both flapwise and edgewise bending deformations. The findings indicate that GPL-reinforced blades exhibit reduced sensitivity to crack propagation compared to traditional fiberglass blades. Furthermore, the paper presents a thorough parametric analysis of the effects of crack location and length on blade performance. Full article

This study investigates the design and potential failure modes of a marine propeller shaft using computational and analytical methods. The aim is to assess the structural integrity of the existing design and propose modifications for improved reliability and service life. Analytical calculations based on classification society rules determined acceptable shaft diameter ranges, considering torsional shear stress limits for SAE 1030 steel. A Campbell diagram analysis identified potential resonance issues at propeller blade excitation frequencies, leading to a recommended operating speed reduction for a safety margin. Support spacing was determined using both the Ship Vibration Design Guide and an empirical method, with the former yielding more conservative results. Finite element analysis, focusing on the keyway area, revealed stress concentrations approaching the material’s ultimate strength. A mesh sensitivity analysis ensured accurate stress predictions. A round-ended rectangular key geometry modification showed a significant stress reduction. Fatigue life analysis using the Goodman equation, incorporating various factors, predicted infinite life under different loading conditions, but varying safety factors highlighted the impact of these conditions. The FEA revealed that the original keyway design led to stress concentrations exceeding allowable limits, correlating with potential shaft failure. The proposed round-ended rectangular key geometry significantly reduced stress, mitigating the risk of fatigue crack initiation. This research contributes to the development of more reliable marine propulsion systems by demonstrating the efficacy of integrating analytical methods, finite element simulations, and fatigue life predictions in the design process. Full article

A high-temperature, high-cycle fatigue test was conducted on a nickel-based single-crystal superalloy with a pore structure. Optical and scanning electron microscopy were utilized to examine the crack propagation paths and fatigue fracture surfaces at the macro and micro scales. The analysis of crack initiation and propagation related to the pore structure facilitated the development of a crack shape factor reflecting these distinct fracture behaviors. Predictions about the high-cycle fatigue stress experienced by the specimen were made, accompanied by an error analysis, providing critical insights for precise stress calculations and structural optimization in engine blade design. The results reveal that high-cycle fatigue cracks originate from corner cracks at pore edges, with the initial propagation displaying smooth crystallographic plane features. Subsequent stages show clear fatigue arc patterns in the propagation zones. The fracture surface exhibits the significant layering of oxide layers, primarily composed of NiO, with traces of CoO displaying columnar growth. AL₂O₃ is predominantly found at the interfaces between the matrix and oxide layers. Short and straight dislocations near the oxide layers and within the matrix suggest that dislocation multiplication and planar slip dominate the slip mechanisms in this alloy. The orientation of the fracture surface is mainly perpendicular to the load direction, with minor inclined facets in localized areas. Correlations were established between the plastic zone dimensions at the crack tips and the corresponding fatigue stresses. Without grain boundaries in single-crystal alloys, these dimensions are easily derived as parameters for fatigue stress analysis. The selected crack shape factor, “elliptical corner crack at pore edges”, captures the initiation and propagation traits relevant to porous structures. Subsequent calculations, accounting for the impact of oxide layers on stress assessments, indicated an error ratio ranging from 1.00 to 1.21 compared to nominal stress values. Full article

Surface angled cracks on critical components in high-speed machinery can lead to fractures under stress and pressure, posing a significant threat to the operational safety of equipment. To detect surface angled cracks on critical components, this paper proposes a “Quantitative Detection Method for Surface Angled Cracks Based on Full-field Scanning Data”. By analyzing different ultrasonic signals in the full-field scanning data from laser ultrasonics, the width, angle, and length of surface angled cracks can be determined. This study investigates the propagation behavior of ultrasonic waves and their interaction with surface angled cracks through theoretical calculations. The crack width is solved by analyzing the distribution of Rayleigh waves in the full-field scanning data. This paper also discusses the differences in ultrasonic wave propagation between near-field and far-field detection and identifies the critical point between these regions. Different computational methods are employed to calculate the inclination angle and the crack endpoint at various scan positions. Four sets of experiments were conducted to validate the proposed method, with results showing that the errors in determining the width, angle, and length of the surface angled cracks were all within 5%. This confirms the feasibility of the method for detecting surface angled cracks. The quantitative detection of surface angled cracks on critical components using this method allows for a comprehensive assessment of the component’s condition, aiding in the prediction of service life and the mitigation of operational risks. This method shows promising application potential in areas such as aircraft engine blade inspection and gear inspection. Full article

Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events. Full article

Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. To further enhance the efficacy of feature extraction and classification accuracy, a projection head is introduced in this paper to systematically map all sample features into a normalized space, thereby improving the model’s capacity to distinguish between distinct fault types. Finally, through the optimization of a supervised contrastive learning (SCL) strategy, the model can better capture the subtle differences between different fault types. Experimental results show that the proposed method has an accuracy of 99.61%, 97.48%, and 96.22% in the classification task of multiple crack fault types at three speeds, which is significantly better than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), and Transformer methods. Full article

The operation of an aero-engine involves various non-stationary processes of acceleration and deceleration, with rotational speed varying in response to changing working conditions to meet different power requirements. To investigate the nonlinear dynamic behaviour of cracked blades under variable rotational speed conditions, this study constructed a rotating blade model with edge-penetrating cracks and proposes a component modal synthesis method that accounts for time-varying rotational speed. The nonlinear response behaviours of cracked blades were examined under three distinct operating conditions: spinless, steady speed, and non-constant speed. The findings indicated a competitive relationship between the effects of rotational speed fluctuations and unbalanced excitation on crack nonlinearity. Variations in rotational speed dominated when rotational speed perturbation was minimal; conversely, aerodynamic forces dominated when the effects of rotational speed were pronounced. An increase in rotational speed perturbation enhanced the super-harmonic nonlinearity induced by cracks, elevated the nonlinear damage index (NDI), and accentuated the crack breathing effect. As the perturbation coefficient increased, the super-harmonic nonlinearity of the crack intensified, resulting in a more complex vibration form and phase diagram. Full article

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the [email protected] metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous [email protected]–0.95 metric, it has also seen an increase from 68.9% to 71.2%. Full article

A pump-turbine may generate high-amplitude hydraulic excitations during operation, wherein the flow-induced response of the spiral case and concrete is a key factor affecting the stable and safe operation of the unit. The preloading spiral case can enhance the combined bearing capacity of the entire structure, yet there is still limited research on the impact of the preloading pressure on the hydrodynamic response. In this study, the pressure fluctuation characteristics and dynamic behaviors of preloading a steel spiral case and concrete under different preloading pressures at rated operating conditions are analyzed based on fluid–structure interaction theory and contact model. The results show that the dominant frequency of pressure fluctuations in the spiral case is 15 $f_n$, which is influenced by the rotor–stator interaction with a runner rotation of short and long blades. Under preloading pressures of 0.5, 0.7, and 1 times the maximum static head, higher preloading pressures reduce the contact regions, leading to uneven deformation and stress distributions with a near-positive linear correlation. The maximum deformation of the PSSC can reach 2.6 mm, and the stress is within the allowable range. The preloading pressure has little effect on the dominant frequency of the dynamic behaviors in the spiral case (15 $f_n$), but both the maximum and amplitudes of deformation and stress increase with higher preloading pressure. The high-amplitude regions of deformation and stress along the axial direction are located near the nose vane, with maximum values of 0.003 mm and 0.082 MPa, respectively. The contact of concrete is at risk of stress concentrations and cracking under high preloading pressure. The results can provide references for optimizing the structural design and the selection of preloading pressure, which improves operation reliability. Full article

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications. Full article

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy. Full article