Search Results (403)

Marine noise pollution is a significant threat to global marine ecosystems and human activities. Most underwater sound-absorbing materials operate in the mid-to high-frequency bands (typically 1–10 kHz for mid-frequency and above 10 kHz for high-frequency), and current underwater low-frequency sound absorption performance remains unsatisfactory, with large structural sizes. To address these issues, a novel metasurface composed of a hexagonal Helmholtz resonator structure made of rubber and metal, combined with an embedded rough neck, is proposed. By introducing roughness into the neck of the Helmholtz resonator, this structure effectively provides the necessary acoustic impedance for low-frequency sound absorption without changing the overall size, thus lowering the resonance frequency. The finite element method is used for simulation, and theoretical validation is performed. The results show that the Helmholtz resonator with the rough neck achieves near-perfect acoustic absorption at a deep subwavelength scale at 81 Hz. At the absorption peak, the wavelength of the sound wave is 370 times the thickness of the resonator. By coupling seven absorption units and optimizing the parameters using a genetic algorithm, the metasurface achieves an average absorption coefficient greater than 0.9 in the 60 Hz to 260 Hz range. The complementary sound absorption coefficients of the unit cells at different frequency bands effectively broaden the absorption bandwidth. Full article

The paper addresses the phenomena of negative effective mass and negative effective density emerging in systems driven by entropic elastic forces. The elasticity of polymers is, at least partially, of entropic origin, and it represents the tendency of a polymer to evolve into a more probable state, rather than into one of lower potential energy. Entropy forces are temperature-dependent; thus, the temperature dependence of the effective mass and effective density arises. The effect of the negative effective mass is a resonance effect, emerging in core–shell mechanical systems, which takes place when the frequency of the harmonic external force acting on a core–shell system connected by an ideal spring approaches from above to the eigen-frequency of the system. We address the situation when the ideal spring connecting the core to the shell is made from a polymer material, and its elasticity is of an entropic origin. The effective mass is calculated, and it is temperature-dependent. The chain of core–shell units connected with a polymer spring is studied. The effective density of the spring is temperature-dependent. Optical and acoustical branches of vibrations are elucidated. The negative mass and density become attainable under the variation of the temperature of the system. In the situation when only one of the springs demonstrates temperature dependence, entropic behavior is investigated. Exemplifications of the effect are addressed. Full article

Traditional sound-absorbing materials, which are intended to address the issue of low-frequency noise control in automobile air-conditioning duct mufflers, have limited noise reduction effects in small spaces. Because of their straightforward structure and excellent controllability, acoustic metamaterials—particularly Helmholtz resonators—have emerged as a research hotspot in low-frequency noise reduction. However, existing technologies have issues such as restricted structural scale, narrow absorption frequency bands, and conflicts with ventilation requirements. To address these, this paper proposes a new type of Helmholtz perforated and tortuous-characteristic duct muffler for the unit cell of acoustic metamaterials. Through the innovative structural design combining a perforated panel with a multi-channel tortuous cavity, the length of the channel is changed in a limited space, thereby extending the sound wave propagation path and enhancing the dissipation of sound wave energy. Meanwhile, for the muffler, acoustic theoretical modeling, finite element simulation, and parametric optimization methods are adopted to systematically analyze the influence of its key structural parameters on the sound transmission loss (STL) of the muffler. Compared with the traditional folded-channel metamaterial, the two differ in resonance frequency by 38 Hz, in transmission loss by 1.157 dB, and in effective bandwidth by 1 Hz. This research provides theoretical support and design basis for solving the problem of low-frequency noise control in ventilation ducts, improves low-frequency broadband sound absorption performance, and promotes the engineering application of high-efficiency noise reduction devices. Full article

This study presents a systematic classification of acoustic agglomeration systems, developed on the basis of an extensive review of experimental and numerical studies, specifically addressing fine particles. The classification framework encompasses wave type, geometric orientation, level of functional integration, chamber composition, and auxiliary enhancement mechanisms. By organizing the diverse configurations into consistent categories, this study enables a comparative analysis of system performance and suitability for practical applications. This review highlights typical design features, operational ranges, and implementation contexts, while identifying key advantages and limitations of each system type. Strengths such as scalability, compatibility with filtration units, and enhancement of particle capture are contrasted with challenges including acoustic intensity requirements, resonance sensitivity, and integration constraints. The proposed classification serves as a practical tool for guiding future design, optimization, and application of acoustic agglomeration technologies in air pollution control. Full article

Nowadays, guided acoustic waves (GAW) are used for many sensor and actuator applications. The use of numerical methods can facilitate the development and optimization process enormously. In this work, a universally applicable finite element method (FEM)-based model is introduced to determine the dispersion relations of guided acoustic waves. A 2-dimensional unit cell model with Floquet periodicity is used to calculate the corresponding band structure diagrams. Starting from a free plate the model is expanded to encompass single-sided fluid loading. Followed by a straightforward algorithm for post-processing, the data is presented. Additionally, a parametric optimizer is used to adapt the simulations to experimental data measured by a laser Doppler vibrometer on an aluminum plate. Finally, the accuracy of the FEM model is compared to two reference models, achieving good consistency. In the case of the fluid-loaded model, the behavior of critical interactions between the dispersion curves and model-based artifacts is discussed. This approach can be used to model 2D structures like phononic crystals, which cannot be simulated by common GAW models. Moreover, this method can be used as input for advanced multiphysics simulations, including acoustic streaming applications. Full article

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units. Full article

This study investigates whether specific acoustic features can reliably indicate phrase boundaries for automatic detection. It includes (1) an analysis of acoustic markers at the end of prosodic units—intonational phrases, intermediate phrases, and words—and (2) the evaluation of these features in an automatic boundary detection algorithm. Data were drawn from professionally and expressively read speech (893 words), news broadcasts (732 words), and interviews (361 words). Key features analyzed were pause duration, final sound lengthening, intensity, and F0 changes. Findings show that pauses and their duration are the most consistent indicators of phrase boundaries, especially at intonational phrase ends. Final sound lengthening and reductions in intensity and F0 also contribute but are less reliable for intermediate phrases. In automatic detection phonetic cues can be used to predict boundaries assigned by phoneticians 69% of the time. Read speech yielded better results than spontaneous speech. Among the features, pause presence and length were the most reliable, while F0 and intensity changes played a minor role. Full article

The acoustics of St Paul’s Cathedral, London, have been discussed in previous studies as a space of historical, cultural, societal, and architectural interest in the capital city of the United Kingdom. This paper presents the results from recent acoustic measurements carried out within the space, making use of state-of-the-art measurement techniques and equipment. The results from these measurements provide a new perspective on the acoustic properties of different and distinct spaces within the cathedral, including coupling effects between the main areas, and the whispering gallery effect that can be heard around the walkway at the base of the dome. The discussion includes the analysis of room acoustic parameters included in the international standards and speech intelligibility parameters, and an indirect comparison between the techniques used here and those used in previous studies of this space. Full article

As an important regulating resource in power systems, pumped storage units frequently switch operating conditions due to peak shaving and frequency regulation, making the condition transitions complex. Traditional methods struggle to achieve high-precision classification. This paper proposes a hierarchical cascade deep learning model based on noise signals, which integrates a convolutional neural network (CNN) with a multi-head attention long short-term memory network (MHA-LSTM) to address the differentiated recognition of steady-state and transitional conditions. The CNN efficiently extracts multi-scale spatial features from sound spectrograms, enabling fast classification under steady-state conditions. The MHA-LSTM combines attention mechanisms with time-series modeling. This enhances its ability to capture long-range dependencies in the signals. And it significantly improves classification accuracy in ambiguous boundaries and transitional scenarios. Testing on 3413 noise samples shows that the proposed method achieves an overall accuracy of 92.22%, with steady-state condition recognition exceeding 98%, and recall and F1 score above 90% for major categories. Compared with other approaches, this model provides a high-precision classification tool for unit health monitoring, supporting the intelligent operation and maintenance of power plants. Full article

The expansion of offshore renewable energy development in Portugal necessitates accurate geological and geotechnical site characterization, especially in regions with limited baseline information. This study focuses on the S. Pedro de Moel area (NW central Portugal), which is characterized by sandy sediments, with the aim of supporting the preliminary design of anchoring and foundation systems for renewable energy structures. An integrated methodology was applied, combining multibeam bathymetry, acoustic backscatter data, high-resolution seismic reflection profiling, sediment sampling, and onshore laboratory testing. Seismic interpretation identified three subsurface units: (1) a deformed carbonated sandstone serving as the acoustic basement; (2) a well-graded sandy gravel layer, up to 8 m thick, interpreted as a marginal marine deposit; and (3) a modern sandy deposit, up to 7 m thick, with variable silt content. Geotechnical analyses yielded effective friction angles for sandy sediments ranging from 39 to 44°, and deformation moduli between 22 MPa and 54 MPa. The sedimentary succession exhibits marked lateral and vertical heterogeneity, which must be considered in engineering design. This cost-effective methodology offers a viable alternative to offshore in situ testing, enabling medium-scale site characterization and providing essential information to support the development of offshore renewable energy infrastructure. Full article

Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, enabling outstanding sound-insulation performance within a deep-subwavelength thickness. Finite-element and transfer-matrix analyses show that increasing the fractal order from one to three raises the number of bandgaps from three to five and expands total stop-band coverage from 17% to over 40% within a deep-subwavelength thickness. Four-microphone impedance-tube measurements on the third-order sample validate a peak transmission loss of 75 dB at 495 Hz, in excellent agreement with simulations. Compared to conventional zigzag and Hilbert-maze designs, this curve fractal architecture delivers enhanced low-frequency broadband insulation, structural lightweighting, and ease of fabrication, making it a promising solution for noise control in machine rooms, ducting systems, and traffic environments. The method proposed in this paper can be applied to noise reduction of transmission parts for ceramic automation production. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

With the global transition towards renewable energy, pumped storage has become a pivotal technology for large-scale energy storage, playing an essential role in peak load regulation, frequency control, and ensuring the stability of modern power systems. As the core equipment of pumped storage power stations, pump-turbines operate under complex and frequently changing conditions. These units are required to switch repeatedly between pumping, generating, and transitional modes, giving rise to significant fluid–structure interactions (FSIs). Such interactions have a profound impact on the operational performance and stability of the units. This review provides a comprehensive summary of current research on FSIs in pump-turbines, encompassing both experimental investigations and numerical simulations. Key topics discussed include internal flow dynamics, vibration and acoustic characteristics, and structural responses such as runner deformation and stress distribution. Various numerical coupling strategies for FSI modeling are also examined in detail. Despite progress in this field, several challenges remain, including the complexity of multidisciplinary coupling, the difficulty in developing and solving accurate models, and limitations in predictive capabilities. This review highlights the critical requirements for advancing FSI research in pump-turbines and identifies gaps in the current literature that warrant further investigation. 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