Search Results (51)

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance. Full article

This study presents the experimental results of an energy harvesting system comprising a cylindrical bluff body coupled with a cantilever beam. A piezoelectric sensor was installed on the beam to generate electrical voltage during the object’s vibrations at the beam’s free end. The research aimed to evaluate the impact of the bluff body’s mass and diameter on the efficiency of the piezoelectric energy harvesting system. Vibrations of the test object were induced by airflow within a chamber of a closed-loop wind tunnel. Five different bluff body masses were analyzed for each of three cylindrical diameters across an airflow velocity range of 1 m/s to 10 m/s. These experiments allowed for the recording of a series of voltage signals over time. The signals were then subjected to Fast Fourier Transform (FFT) analysis. Subsequently, the relationship between vibration frequency and airflow velocity was examined. The peak-to-peak voltage value was also analyzed to provide an overall assessment of the energy harvesting efficiency of the system under investigation. Finally, the 0–1 test for chaos was additionally employed as a diagnostic tool to assess the complexity of system dynamics based on time series data. This test allowed for distinguishing between oscillatory behavior and cases where the system became trapped in a potential well, revealing key transitions in dynamic regimes. Full article

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment. Full article

To address the challenge of low diagnostic accuracy in rolling bearing fault diagnosis under varying operating conditions, this paper proposes a novel method integrating the synchronized wavelet transform (SWT) with an enhanced Vision Transformer architecture, referred to as ResCAA-ViT. The SWT is first applied to process raw vibration signals, generating high-resolution time–frequency maps as input for the network model. By compressing and reordering wavelet transform coefficients in the frequency domain, the SWT enhances time–frequency resolution, enabling the clear capture of instantaneous changes and local features in the signals. Transfer learning further leverages pre-trained ResNet50 parameters to initialize the convolutional and residual layers of the ResCAA-ViT model, facilitating efficient feature extraction. The extracted features are processed by a dual-branch architecture: the left branch employs a residual network module with a CAA attention mechanism, improving sensitivity to critical fault characteristics through strip convolution and adaptive channel weighting. The right branch utilizes a Vision Transformer to capture global features via the self-attention mechanism. The outputs of both branches are fused through addition, and the diagnostic results are obtained using a Softmax classifier. This hybrid architecture combines the strengths of convolutional neural networks and Transformers while leveraging the CAA attention mechanism to enhance feature representation, resulting in robust fault diagnosis. To further enhance generalization, the model combines cross-entropy and mean squared error loss functions. The experimental results show that the proposed method achieves average accuracy rates of 99.96% and 96.51% under constant and varying load conditions, respectively, on the Case Western Reserve University bearing fault dataset, outperforming other methods. Additionally, it achieves an average diagnostic accuracy of 99.25% on a real-world dataset of generator non-drive end bearings in wind turbines, surpassing competing approaches. These findings highlight the effectiveness of the SWT and ResCAA-ViT-based approach in addressing complex variations in operating conditions, demonstrating its significant practical applicability. Full article

Microrobots, characterized by their small size, flexibility, and portability, have a diverse range of potential applications. However, microrobots’ actuation (piezoelectric ceramics, dielectric elastomers, ion winds, etc.) often requires a high voltage, typically hundreds of volts. The lithium niobate transformer (LNT), a piezoelectric voltage transformer, presents a promising solution for miniaturizing high-voltage power supplies due to its compact size, low weight, and high step-up ratio. This study explores the effects of structural parameters and external circuits on the resonant frequency and step-up ratio of the LNT through numerical simulations and experiments. The results indicate the following: (1) the second-order longitudinal vibration frequency of the lithium niobate (LN) plate inversely correlates with its length; (2) the thickness and width of the plate have minimal impact on the frequency; (3) the step-up ratio increases as the plate thickness decreases. The experimental results suggest that LN plates with a thickness of 1 mm are preferable due to the fragility of 0.5 mm plates, especially at the output end. Additionally, optimizing the input circuit enhances voltage amplification, allowing the LNT to generate sufficient output voltage for corona discharge. These findings highlight the potential of LNTs for efficiently and reliably powering small-scale devices. Full article

When pouring concrete overhead, a pump truck boom’s vibration has a big effect on how accurately the concrete is poured. This is especially true during fixed-point pouring, where the boom’s vibration is likely to cause the pouring position to deviate, which lowers the quality of the construction. It is difficult to forecast the dynamic reaction of the pump truck boom in a construction setting because of the constantly shifting external factors (wind speed, pipeline stress during pumping, etc.), which makes it difficult to guarantee casting accuracy. This study suggests a non-random vibration analysis technique for pump truck booms based on the interval process theory in order to address this issue. A dynamic excitation analysis method based on rigid–discrete coupling is proposed, taking into account the response influence of the material characteristics in the transportation process. The pumping process of concrete materials in the conveying pipeline is simulated using discrete element simulation technology to determine the system’s stress conditions under pumping conditions. The dynamic response characteristics of the pump truck boom under operating conditions are revealed by using non-random vibration analysis with the mathematical model that has been created based on the particular specifications of the pump truck boom. This study employs the Newmark-β technique for numerical computation to solve the dynamic equations and characterize the displacement response envelope under uncertain system parameter settings. The experimental findings demonstrate that the suggested approach may accurately capture the upper and lower bounds of the boom dynamic response, offering a trustworthy way to assess the dynamic behavior while pumping. The technique can reliably predict the dynamic displacement boundary and control the casting position deviation within a predefined range by accurately predicting the dynamic displacement range of the pump truck’s boom end and efficiently constructing the displacement envelope under uncertain dynamic excitation. For numerical computation, use the Newmark-β algorithm. This outcome confirms the substantial enhancement of the proposed method regarding pouring precision in construction settings, offering a novel solution and technical guidance for vibration control in engineering projects. Full article

A composite hydrogen storage vessel (CHSV) is one key component of the hydrogen fuel cell vehicle, which always suffers random vibration during transportation, resulting in fatigue failure and a reduction in service life. In this paper, firstly, the free and constrained modes of CHSV are experimentally studied and numerically simulated. Subsequently, the random vibration simulation of CHSV is carried out to predict the stress distribution, while Steinberg’s method and Dirlik’s method are used to predict the fatigue life of CHSV based on the results of stress distribution. In the end, the optimization of ply parameters of the composite winding layer was conducted to improve the stress distribution and fatigue life of CHSV. The results show that the vibration pattern and frequency of the free and constrained modes of CHSV obtained from the experiment tests and the numerical predictions show a good agreement. The maximum difference in the value of the vibration frequency of the free and constrained modes of CHSV from the FEA and experiment tests are, respectively, 8.9% and 8.0%, verifying the accuracy of the finite element model of CHSV. There is no obvious difference between the fatigue life of the winding layer and the inner liner calculated by Steinberg’s method and Dirlik’s method, indicating the accuracy of FEA of fatigue life in the software Fe-safe. Without the optimization, the maximum stresses of the winding layer and the inner liner are found to be near the head section by 469.4 MPa and 173.0 MPa, respectively, and the numbers of life cycles of the winding layer and the inner liner obtained based on the Dirlik’s method are around 1.66 × 10⁶ and 3.06 × 10⁶, respectively. Through the optimization of ply parameters of the composite winding layer, the maximum stresses of the winding layer and the inner liner are reduced by 66% and 85%, respectively, while the numbers of life cycles of the winding layer and the inner liner both are increased to 1 × 10⁷ (high cycle fatigue life standard). The results of the study provide theoretical guidance for the design and optimization of CHSV under random vibration. Full article

In areas where there is high humidity and freezing rain, there is a tendency of blade icing on wind turbines. It results in energy dissipation and mechanical abrasion and also creates a safety concern due to the risk of having falling ice. Real-time online detection of icing is crucial in the enhancement of power generation efficiency and in the safety of wind turbines. The current methods of icing detection that use ultrasound, optics, vibration, and electromagnetics are already studied. But these methods have their drawbacks, including small detection ranges, low accuracy, large size, and challenges in distributed installation, making it hard to capture the real-time dynamics of the icing and de-icing processes on the wind turbine blades. To this end, this paper presents a new blade surface icing detection technique using microstrip lines. This approach uses the impact of icing state and thickness on the effective dielectric constant of the microstrip line surface. This paper presents the analysis of time-domain features of microwave signals, which facilitates the identification of both the icing state and the corresponding thickness. Simulation and experimental measurement of linear and S-shaped microstrip sensors are used in this research in order to compare the response of the sensors to the variation in the thickness of the icing layer. It is seen that for icing thickness ranging from 0 mm to 6 mm, the imaginary part of the S21 parameter of the S-shaped microstrip line has a more significant change than that of the linear microstrip line. The above experiments also confirm that the phase shift value of the S-shaped microstrip line is always higher than that of the linear microstrip line for the same variation of icing thickness, which proves that the S-shaped microstrip line is more sensitive than the linear one. Also, it was possible to establish the relationship between the phase shift values and icing thickness, which makes it possible to predict the icing thickness. The developed microwave microstrip detection technology is intended for usage in the wind turbine blade icing and similar surface detection areas. This method saves the size and thickness of icing sensors, which makes it possible to conduct measurements at various points. This is especially beneficial for usage in wind turbine blades and can be further applied in aerospace, automotive, and construction, especially the bridges. Full article

The continuous worsening of the natural surroundings requires accelerating the exploration of green energy technology. Utilising ambient vibration to power electronic equipment constitutes an important measure to address the power crisis. Vibration power is widely dispersed in the surroundings, such as mechanical vibration, acoustic vibration, wind vibration, and water wave vibration. Collecting vibration energy is one of the research hotspots in the field of energy. Meanwhile, it is also an important way to solve the energy crisis. This paper illustrates the working principles and recent research progress of five known methods of vibrational energy harvesting, namely, electromagnetic, piezoelectric, friction electric, electrostatic, and magnetostrictive vibrational energy harvesters. The strengths and weaknesses of each method are summarised. At the end of the article, the future trends of micro-nano vibrational energy collectors are envisioned. Full article

In this work, we present the first three-dimensional (3D) computational investigation of wind turbine airfoils over ${360}^{°}$ angles of attack to predict unsteady aerodynamic loads and vortex-shedding characteristics. To this end, static–airfoil simulations are performed for the FFA-W3 airfoil family at a Reynolds number of ${10}^7$ with the Improved Delayed Detached Eddy Simulation turbulence model. Aerodynamic forces reveal that the onset of boundary-layer instabilities and flow separation does not necessarily coincide with the onset of stall. In addition, a comparison with two-dimensional simulation data and flat plate theory extension of airfoil polars, suggest that, in the deep stall regime, 3D effects remain critical for predicting both the unsteady loads and the vortex-shedding dynamics. For all airfoils, the vortex-shedding frequencies are found to be inversely proportional to the wake width. In the case of slender airfoils, the frequencies are nearly independent of the airfoil thickness, and their corresponding Strouhal number $St$ is approximately $0.15$. Based on the calculated $St$, the potential for shedding frequencies to coincide with the natural frequencies of the International Energy Agency 15 MW reference wind turbine blades is investigated. The analysis shows that vortex-induced vibrations occur primarily at angles of attack of around $\pm {90}^{°}$ for all airfoils. Full article

In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way. Full article

Offshore substations are important sustainable power infrastructures subjected to strong vibrations induced by complex environmental excitations such as wind, waves, and currents. To protect the structures and expensive facilities, lightweight vibration control devices are highly desirable in offshore substations. With a high-performance energy dissipation device, the inerter, the conventional Tuned Mass Damper (TMD) is upgraded for lightweight vibration control. The optimal parametric design and performance evaluation of single- and double-tuned vibration control devices is performed based on the H-norm criteria. The corresponding equivalent mass ratios of both single- and double-tuned vibration control devices are summarized and formulated in a systematical manner. Finally, the presented optimal design formulas, equivalent mass ratios, and control performances are validated by vibration control analyses on a practical offshore substation. The results show that inerter-based vibration control devices can be effectively equivalent to a TMD, with the equivalent mass ratio. The double-tuned inerter-based device could save 25% mass compared to a TMD. With a Tuned Mass Damper Inerter (TMDI), the responsibility for the mass could be shared with dual-end connected inerters. Meanwhile, the Tuned Viscous Mass Damper (TVMD) completely replaces the mass block with an inerter, which has a superior lightweight vibration control performance. Full article

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1–20 Hz. Full article

Numerical calculations were performed to study the vortex-induced vibration (VIV) of a circular cylinder, which was elastically supported by springs of linear and cubic terms. These simulations were conducted at high Reynolds numbers ranging from 4200 to 42,000. To simulate the cylinder’s motion and the associated aerodynamic forces, Computational Fluid Dynamics were employed in conjunction with dynamic mesh capabilities. The numerical method was initially verified by testing it with various grid resolutions and time steps, and subsequently, it was validated using experimental data. The response of cubic nonlinearities was investigated using insights gained from a conventional linear vortex-induced vibration (VIV) system. This 2D study revealed that both the amplitude and frequency of vibrations are contingent on the flow velocity. The highest output was achieved within the frequency lock-in region, where internal resonance occurs. In the case of a hardening spring, the beating response was observed from the lower end of the initial branch to the upper end of the initial branch. The response displacement amplitude obtained for the linear spring case was 27 mm, whereas in the cubic nonlinear case, the value was 31.8 mm. More importantly, the results indicate that the inclusion of nonlinear springs can substantially extend the range of wind velocities in which significant energy extraction through vortex-induced vibration (VIV) is achievable. Full article