Search Results (778)

Accurate wind turbine noise (WTN) measurements are essential for environmental compliance and noise impact assessments. However, these measurements are often polluted by background biological noise, especially from insects. Insect noise is typically assumed to be irrelevant due to frequency separation. This study challenges this assumption by demonstrating that insect sounds, specifically those of the cricket Oecanthus pellucens, can overlap with turbine noise in the 2.5 kHz band and introduce significant measurement bias at low wind speeds. The featured application is a machine learning-based methodology to filter confounding biological sounds (e.g., insect calls) from wind turbine noise measurements. By correcting for these acoustic contaminants, which typically lead to an overestimation of turbine noise at low wind speeds, the method enables more accurate environmental noise impact assessments. This directly supports the development of evidence-based regulatory policies and guidelines. Using long-term acoustic monitoring and an unsupervised Gaussian Mixture Model (GMM) clustering approach, we classified and excluded insect noise from recorded data. We found that the presence of cricket calls can increase measured wind turbine sound power levels (WTSPL) by more than 3 dBA at wind speeds below 6 m/s, with peak deviations reaching up to 10 dBA. These findings have significant implications for rural or low-wind regions where turbine operation at partial load is frequent. Our results underscore the importance of insect noise filtering when performing WTN assessments to ensure regulatory accuracy, particularly when long-term average noise modeling is used for compliance. The presented methodology provides a robust framework for distinguishing insect noise and can improve the consistency and credibility of WTN measurements under real-world environmental conditions. Full article

Dynamic state estimation of floating offshore wind turbines (FOWTs) in complex marine environments is a core challenge for digital twin systems. This study proposes a joint estimation framework that integrates windowed dynamic mode decomposition (W-DMD) and an adaptive strong tracking Kalman filter (ASTKF). W-DMD extracts dominant modes under stochastic excitations through a sliding-window strategy and constructs an interpretable reduced-order state-space model. ASTKF is then employed to enhance estimation robustness against environmental uncertainties and noise. The framework is validated through numerical simulations under turbulent wind and wave conditions, demonstrating high estimation accuracy and strong robustness against sudden environmental disturbances. The results indicate that the proposed method provides a computationally efficient and interpretable tool for FOWT digital twins, laying the foundation for predictive maintenance and optimal control. Full article

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

Accurate reactive power optimization in wind farms (WFs) is critical for optimizing operations and ensuring grid stability, yet it faces challenges from noisy, nonlinear, and dynamic Supervisory Control and Data Acquisition (SCADA) data. This study proposes an innovative framework, WBS-BiGRU, integrating three novel components to address these issues. Firstly, the Wavelet-DBSCAN (WDBSCAN) method combines wavelet transform’s time–frequency analysis with density-based spatial clustering of applications with noise (DBSCAN)’s density-based clustering to effectively remove noise and outliers from complex WF datasets, leveraging multi-scale features for enhanced adaptability to non-stationary signals. Subsequently, a Boomerang Evolutionary Optimization (BAEO) with the Seasonal Decomposition Improved Process (SDIP) synergistically decomposes time series into trend, seasonal, and residual components, generating diverse candidate solutions to optimize data inputs. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with an attention mechanism captures long-term dependencies in temporal data and dynamically focuses on key features, improving reactive power forecasting precision. The WBS-BiGRU framework significantly enhances forecasting accuracy and robustness, offering a reliable solution for WF operation optimization and equipment health management. Full article

In low-speed flow (Ma < 0.3), pressure-sensitive paint (PSP) technology encounters a significant bottleneck in micro-pressure measurements due to the coupled interference of light source instability, camera noise, and paint photodegradation. This study introduces a hardware–algorithm collaborative decoupling framework to address the light noise–degradation coupling issue. The framework integrates real-time light source fluctuation monitoring using a photomultiplier tube (PMT), a combined histogram–wavelet denoising algorithm, and a dynamic photodegradation compensation model. A high-precision static calibration system with a pressure control error of 3.4 Pa was constructed to validate the proposed framework. The experimental results indicate that light source fluctuations contribute an error of 42.61 Pa, accounting for 33% of the total error. After collaborative optimization, the PSP measurement error was reduced to below 50 Pa, representing a 50% improvement compared to previous results (100 Pa). This study provides reliable technical support for micro-pressure measurement applications, such as low-speed wind tunnel testing of aerospace vehicles and microfluidic diagnostics. Full article

This paper presents a strategy to mitigate electromagnetic noise in induction motors for electric vehicles by optimizing the rotor slot count and skewing distance. Initially, the magnetomotive forces (MMF) of the stator and rotor windings, air-gap permeance, and the predominant radial electromagnetic force waves in the air-gap magnetic field were analytically determined and compiled. A finite element model of the original 36/42 straight-slot configuration was established for simulation validation. Subsequently, a preliminary optimization scheme for rotor slot number was proposed. A systematic analysis was conducted of the circumferential distribution of radial force waves and their harmonic components in both temporal and spatial orders by comparing electromagnetic vibration characteristics across different rotor slot configurations (42 versus 53 slots) using two-dimensional Fourier decomposition. Furthermore, building upon the mechanism of tooth harmonic suppression via rotor skewing, an advanced optimization strategy for skewing distance was developed. Comparative analysis of harmonic content in air-gap flux density under three configurations (straight slot, 1.0× skewing, and 1.2× skewing) revealed the optimal solution. Experimental vibration tests demonstrated significant improvements: the optimized 53-slot rotor with 1.2× skewing reduced vibration amplitudes by 5 dB·Hz at the 2nd-order natural frequency, 5 dB·Hz at the 3rd-order natural frequency, and 18 dB·Hz at the 3rd-order resonance peak compared to the original 42-slot straight-slot design. These results confirm that coordinated optimization of rotor slot number and skewing distance effectively mitigates electromagnetic vibration and noise in traction motors. Full article

Precise wind power forecasting has several benefits, such as optimized peak regulation in power systems, enhanced safety analysis, and improved energy efficiency. Considering the substantial influence of meteorological data, such as wind speed and temperature, on wind power generation, and to minimize the impact of fluctuations and complexity of wind power data on the forecast results, this paper proposes a combined wind power forecasting method. This approach is based on the long short-term memory network (LSTM) model, using the maximal information coefficient (MIC) method to select numerical weather prediction (NWP) and combining the efficiency of complete EEMD with the adaptive noise (CEEMDAN) method for nonlinear signal decomposition. Results indicate that the accuracy of the forecast results is supported by NWP. Moreover, wind power data are decomposed by the CEEMDAN algorithm and converted into relatively regular sub-sequences with small fluctuations. The MIC algorithm effectively reduces the redundant information in NWP data, and the LSTM algorithm addresses the uncertainty of wind power data. Finally, the wind power of multiple wind farms is forecasted. Comparison of the forecast results of different methods revealed that the NWP-CEEMDAN-LSTM method proposed in this paper, which considers feature extraction using MIC, effectively tracks power fluctuations and improves forecast performance, thereby reducing the forecast error of wind power. Full article

Urban trees are subjected to stressful conditions caused by anthropogenic, biotic, and abiotic factors. These stressors can cause structural changes, increasing the risks of branch failure or even complete uprooting. To mitigate the risks to people’s safety, administrators must assess and evaluate the health and structural stability of trees. Risk analysis typically takes into account environmental vulnerability and tree characteristics, assessed at a specific point in time. However, although dynamic tests play a crucial role in risk assessment in urban environments, the high cost of the sensors significantly limits their widespread application across large tree populations. For this reason, the present study aims to evaluate the effectiveness of low-cost sensors in monitoring tree dynamics. A low-cost micro-electro-mechanical systems (MEMS) sensor is tested in the laboratory and the field using a pull-and-release test, and its performance is compared with that of seismic reference accelerometers. The collected data are analyzed and compared in terms of both the frequency and time domains. To obtain reliable measurements, the accelerations must be generated by substantial dynamic excitations, such as high wind events or abrupt changes in loading conditions. The results show that the MEMS sensor has lower accuracy and higher noise compared to the seismic sensor; however, the MEMS can still identify the main peaks in the frequency domain compared to the seismic sensor, provided that the input amplitude is sufficiently high. Full article

Urban Air Mobility (UAM) has the potential to revolutionize urban transportation, largely with the deployment of Unmanned Aerial Vehicles (UAVs), commonly known as drones. After an initial stage focused on technology requirements, research is now shifting toward investigating operational requirements, which are unavoidably affected by urban characteristics. This study aims to explore the implementation of UAM services within urban environments by mapping the current scientific landscape from a city-focused perspective. Following a systematic search procedure, a bibliometric analysis was conducted on studies published between 2010 and 2024, examining over 350 articles that address UAM and urban-related topics. Trends in publication volume and scientific impact were analysed, along with influential manuscripts, collaborations, and leading countries in the field. Through a keyword co-occurrence analysis, five main research themes were identified: air traffic management, risk assessment, environmental factors (wind and noise), and vertiport location. These themes were further explored through a scoping review to assess current research and emerging directions. The findings highlight that urban characteristics are not just operational constraints but also fundamental elements that shape UAM strategies, influencing UAV path planning, safety, environmental constraints, and infrastructure design. Future research directions include the development of urban digital twins, comprehensive urban spatial databases, and multi-objective optimization frameworks to support the effective implementation of UAM into cities. Full article

This paper introduces a digital adaptive control framework for large-scale multivariable systems, integrating matrix linear Diophantine equations with block pole placement. The main innovation lies in adaptively relocating the full eigenstructure using matrix polynomial representations and a recursive identification algorithm for real-time parameter estimation. The proposed method achieves accurate eigenvalue placement, strong disturbance rejection, and fast regulation under model uncertainty. Its effectiveness is demonstrated through simulations on a large-scale winding process, showing precise tracking, low steady-state error, and robust decoupling. Compared with traditional non-adaptive designs, the approach ensures superior performance against parameter variations and noise, highlighting its potential for high-performance industrial applications. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

In this numerical and experimental study, the effects of the width, length, and height parameters of a mirror arm on the drag coefficients of two side-view mirror models were investigated. The analyses were performed according to fractional factorial Taguchi L9 experiment plans. In the wind tunnel, a constant-temperature hot-wire anemometer and a pressure scanner system were used to measure velocity and static pressures, respectively. A realizable k-ε turbulence model with a scalable wall function was applied in the simulations, and the velocity was kept constant at 30 m/s. Means of the drag coefficient, signal/noise values, and analysis of variance were used to evaluate the parameters’ effects. The results showed that the drag coefficients increased with arm height. The increase in arm width decreased the drag coefficient to a limited extent, while the aspect ratio (width/height) showed a strong negative correlation with the drag coefficient. The high aspect ratios resulted in streamlined geometries around the mirror arm and delayed flow separations. The numerical analysis results showed good agreement with the experimental values for both mirror models. Full article

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article

This study examines the effects of partial wakes caused by upstream turbine yaw control on the trailing-edge noise of a downstream turbine under stable and neutral atmospheric conditions. Using a combined model coupling the unsteady vortex lattice method (UVLM) with the Curl wake model, calibrated with large eddy simulation data, wake behavior and noise characteristics were analyzed for yaw angles from −30° to +30°. Results show that partial wakes slightly raise overall noise levels and lateral asymmetry of trailing-edge noise, while amplitude modulation (AM) strength is more strongly influenced by yaw control. AM varies linearly with wake deflection at moderate yaw angles but behaves nonlinearly beyond a threshold due to large wake deflection and deformation. Findings reveal that yaw control can significantly increase the lateral asymmetry in the AM strength directivity pattern of the downstream turbine, and that AM characteristics depend on the complex interplay between inflow distribution and convective amplification effects, highlighting the importance of accurate wake prediction, along with appropriate consideration of observer point location and blade rotation, for evaluating AM characteristics of a wind turbine influenced by a partial wake. Full article