Search Results (1,110)

This study investigates the dynamics of dust acoustic periodic waves in a three-component, unmagnetized dusty plasma system using generalized $(r^{\star },q^{\star })$ distributions. First, boundary conditions are applied to reduce the model to a second-order nonlinear ordinary differential equation. The Galilean transformation is subsequently applied to reformulate the second-order ordinary differential equation into an unperturbed dynamical system. Next, phase portraits of the system are examined under all possible conditions of the discriminant of the associated cubic polynomial, identifying regions of stability and instability. The Runge–Kutta method is employed to construct the phase portraits of the system. The Hamiltonian function of the unperturbed system is subsequently derived and used to analyze energy levels and verify the phase portraits. Under the influence of an external periodic perturbation, the quasi-periodic and chaotic dynamics of dust ion acoustic waves are explored. Chaos detection tools confirm the presence of quasi-periodic and chaotic patterns using Basin of attraction, Lyapunov exponents, Fractal Dimension, Bifurcation diagram, Poincaré map, Time analysis, Multi-stability analysis, Chaotic attractor, Return map, Power spectrum, and 3D and 2D phase portraits. In addition, the model’s response to different initial conditions was examined through sensitivity analysis. Full article

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

This paper presents the design, fabrication, and experimental evaluation of a capacitive micromachined ultrasonic transducer (CMUT) linear array for non-contact thickness measurement of marine engineering structures. A 16-element CMUT array was fabricated using a silicon–silicon wafer bonding process, and encapsulated in polyurethane to ensure acoustic impedance matching and environmental protection in underwater conditions. The acoustic performance of the encapsulated CMUT was characterized using standard piezoelectric transducers as reference. The array achieved a transmitting sensitivity of 146.82 dB and a receiving sensitivity of −229.55 dB at 1 MHz. A complete thickness detection system was developed by integrating the CMUT array with a custom transceiver circuit and implementing a time-of-flight (ToF) measurement algorithm. To evaluate environmental robustness, systematic experiments were conducted under varying water temperatures and salinity levels. The results demonstrate that the absolute thickness measurement error remains within ±0.1 mm under all tested conditions, satisfying the accuracy requirements for marine structural health monitoring. The results validate the feasibility of CMUT-based systems for precise and stable thickness measurement in underwater environments, and support their application in non-destructive evaluation of marine infrastructure. Full article

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article

Interferometric fiber optic hydrophones (IFOHs) are highly sensitive for underwater acoustic detection but face challenges owing to the trade-off between laser monochromaticity and coherence length. In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. Furthermore, our research findings demonstrate that while reducing the laser coherence length effectively mitigates parasitic interference noise in IFOHs, this reduction also leads to elevated background noise caused by diminished interference visibility. Consequently, the modulation of coherence length requires a balanced optimization approach that not only suppresses parasitic noise but also minimizes visibility-introduced background noise, thereby determining the system-specific optimal coherence length. Through theoretical modeling and experimental validation, we determined that for IFOH systems with a 500 ns delay, the optimal coherence lengths for link fibers of 3.3 km and 10 km are 0.93 m and 0.78 m, respectively. At the optimal coherence length, the background noise level in the 3.3 km system reaches −84.5 dB (re: rad/√Hz @1 kHz), representing an additional noise suppression of 4.5 dB beyond the original suppression. This study provides a comprehensive theoretical and experimental solution to the long-standing contradiction between high laser monochromaticity, stability and appropriate coherence length, establishing a coherence modulation noise suppression framework for hydrophones, gyroscopes, distributed acoustic sensing (DAS), and other fields. Full article

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article

The impact of soundscapes in religious architecture on public psychology has garnered increasing attention in both research and policy domains. However, the mechanisms by which temple soundscapes influence public emotions remain scientifically unclear. This paper aims to explore how soundscapes in temple architectures designated as World Natural and Cultural Heritage sites affect visitors’ experiences. Considering visitors with diverse social and demographic backgrounds, the research design includes subjective soundscape evaluations and EEG measurements from 193 visitors at two World Heritage temples. The results indicate that visitors’ religious beliefs primarily affect their soundscape perception, while their soundscape preferences show specific correlations with chanting and human voices. Furthermore, compared to males, females exhibit greater sensitivity to emotional variations induced by soundscape experiences. Urban architects can enhance visitors’ positive emotional experiences by integrating soundscape design into the planning of future religious architectures, thereby creating pleasant acoustic environments. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

The presented article analyzes the impact of internal defects on the modal responses of polyamide parts subjected to bending. Samples with defects of various sizes (0, 3, 5, 7, and 10 mm) located at the neutral bending line were tested. Modal properties were measured via an acoustic and a vibration sensor, using impulse excitation and fast Fourier transform (FFT) analysis. Modal properties include peak frequency, damping and amplitude. Non-defective samples show lower peak frequency and stronger amplitude for both detectors. Moreover, defects larger than 3 mm have minimal impact on peak frequency. The vibration detector is more sensitive to delamination presented at 7 and 10 mm defects. In addition, elevated peak frequency at 3 mm is the result of local hardening at the defect edge. Moreover, a neutral line position reduces damping when the defect size approaches 5 mm. Conversely, acoustic detectors ignore delamination and reveal lower damping and amplitude at 7 and 10 mm defects. Furthermore, internal sound diffusion from 3 and 5 mm defects enhances air losses and damping. Acoustic detectors only evaluate fault size and position, whereas vibrational detectors may detect local reinforcement and delamination more easily. These results highlight the importance of choosing the right detector according to the location, size, and specific modal characteristics of defects. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article

Void defects, manifested as distributed porosity, are common in metal additive manufacturing (AM) and can significantly degrade the mechanical performance and reliability of fabricated components. To enable real-time quality control during fabrication, this study proposes a grating laser ultrasonic method for the online evaluation of porosity in AM parts. Based on the theoretical relationship between surface acoustic wave (SAW) velocity and material porosity, a non-contact detection approach is developed, allowing the direct inference of porosity from the measured SAW velocities without requiring knowledge of the exact source–detector distance. Numerical simulations are conducted to analyze SAW propagation under varying porosity conditions and to validate the inversion model. Experimental measurements on aluminum alloy specimens with different porosity levels further confirm the sensitivity of SAW signals to internal voids. The results show consistent waveform and spectral trends between the simulation and experiment, supporting the feasibility of the proposed method for practical applications. Overall, the findings demonstrate the potential of this approach for the accurate online monitoring of void defects in metal AM components. Full article

Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling. Due to the complex structural environment, diverse lithofacies types, and differences in logging data and core data recording standards, there is significant overlap in the logging responses between different lithologies in the second member of the Lucaogou Formation in the Santanghu Basin. Machine learning methods have demonstrated powerful nonlinear capabilities that have a strong advantage in addressing complex nonlinear relationships between data. In this paper, based on felsic content, the lithologies in the study area are classified into four categories from high to low: tuff, dolomitic tuff, tuffaceous dolomite, and dolomite. We also study select logging attributes that are sensitive to lithology, such as natural gamma, acoustic travel time, neutron, and compensated density. Using machine learning methods, XGBoost, random forest, and support vector regression were selected to conduct lithology identification and favorable reservoir prediction in the study. The prediction results show that when trained with 80% of the predictors, the prediction performance of all three models has improved to varying degrees. Among them, Random Forest performed best in predicting felsic content, with an MAE of 0.11, an MSE of 0.020, an RMSE of 0.14, and a R² of 0.43. XGBoost ranked second, with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R² of 0.42. SVR performed the poorest. By comparing the actual core data with the predicted data, it was found that the results are relatively close to the XRD results, indicating that the prediction accuracy is high. Full article

To enable the highly sensitive detection of acetylene (C₂H₂) dissolved in transformer oil, a high-power quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing system is proposed. A standard 32.7 kHz quartz tuning fork (QTF) was employed as an acoustic transducer, coupled with an optimized acoustic resonator to enhance the acoustic signal. The laser power was boosted to 150 mW using a C-band erbium-doped fiber amplifier (EDFA), achieving a detection limit of 469 ppb for C₂H₂ with an integration time of 1 s. The headspace degassing method was utilized to extract dissolved gases from the transformer oil, and the equilibrium process for the release of dissolved C₂H₂ was successfully monitored using the developed high-power QEPAS system. This approach provides reliable technical support for the real-time monitoring of the operational safety of power transformers. Full article