Search Results (85)

Acoustic propagation in the ice cover of the Polar Ocean is of increasing interest from both scientific and engineering perspectives. The low-frequency elastic waves propagating in floating ice are primarily governed by waveguides stemming from the layered structure of the medium. For shallow water areas, experimental observation indicates that two Scholte-like waves are observed at low frequencies, i.e., the quasi-Scholte (QS) and Scholte–Stoneley (SS) waves, which are different from deep-sea cases. Due to the finite depths of ice, water, and sediment layers, both waves are dispersive. By modeling the waveguide of an ice-covered shallow-water (ICSW) system, the dispersion characteristics of both waves are derived, validated through numerical simulation, and analyzed with respect to layer structure for both soft and hard sediment. Results indicate a consistent conclusion; the QS wave exhibits a unique sensitivity to ice thickness, whereas the SS wave shows marginal sensitivity to ice thickness, and is controlled by the ratio of water depth to sediment depth, regardless of their absolute values. Based on these dispersion characteristics, a two-step inversion procedure is developed and applied to the synthetic signals from a numerical simulation. The conditional observability of the SS wave at the ice surface is also investigated and discussed. Full article

Reducing the consumption of energy-intensive cement and promoting the resource utilization of industrial waste are two critical challenges that should be urgently addressed to achieve the goals of carbon neutrality and green sustainable development in the building materials field. Among these, the massive stockpiling of industrial waste such as fly ash and silica fume poses serious threats to the environment and human health, making their efficient utilization an urgent need to alleviate environmental pressure. This study employs the ice-template method to incorporate fly ash and silica fume as functional components into a cement-based system, fabricating a novel composite material. This material features a layered porous structure, which not only reduces cement usage but also results in a lighter weight. The introduction of the ice-templating method successfully constructed an anisotropic lamellar structure, leading to significant enhancements in flexural strength and toughness—by approximately 26.6% and 30%, respectively, vertical to the lamellae compared to conventional dense cement. Meanwhile, the hybrid blend of silica fume and fly ash effectively improved the deformability of the material, as evidenced by a notable increase in compressive failure strain. These excellent behaviors of mechanical properties are attributed to the formation of a multi-scale microstructure characterized by “macroscopically continuous and microscopically dense” features. Moreover, this specific microstructure offers greater advantages in sound absorption performance. The acoustic impedance tube tests demonstrate that the noise reduction coefficient of the novel cement-based material incorporating fly ash and silica fume is improved by 82%, holding promising applications in noise reduction for the construction and transportation fields. This research provides a feasible pathway for the high-value application of industrial solid waste in low-carbon materials. Full article

This study investigates the aerodynamic and aeroacoustics behavior of automotive cooling modules in both conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), with a particular focus on installation effects. Numerical simulations based on the Lattice Boltzmann Method (LBM) are conducted to analyze noise generation mechanisms and flow characteristics across four configurations. The study highlights the challenges of adapting classical cooling module components to EV setups, emphasizing the influence of heat exchanger (HE) placement and duct geometry on noise levels and flow dynamics. The results show that the presence of the HE smooths the upstream flow, improves rotor loading distribution and disrupts long, coherent vortical structures, thereby reducing tonal noise. However, the additional resistance introduced by the HE leads to increased rotor loading and enhanced leakage flow through the shroud-rotor gap. Despite these effects, the overall sound pressure level (OASPL) remains largely unchanged, maintaining a similar magnitude and dipolar directivity pattern as the configuration without the HE. In EV modules, the inclusion of ducts introduces significant flow disturbances and localized pressure fluctuations, leading to regions of high flow rate and rotor loading. These non-uniform flow conditions excite duct modes, resulting in troughs and humps in the acoustic spectrum and potentially causing resonance at the blade-passing frequency, which increases the amplitude in the lower frequency range. Analysis of the loading force components reveals that rotor loading is primarily driven by thrust forces, while duct loading is dominated by lateral forces. Across all configurations, fluctuations at the leading and trailing edges of the rotor are observed, originating from the blade tip and extending to approximately mid-span. These fluctuations are more pronounced in the EV module, identifying it as the dominant source of pressure disturbances. The numerical results are validated against experimental data obtained in the anechoic chamber at the University of Sherbrooke and show good agreement. The relative trends are accurately predicted at lower frequencies, with slight over-prediction, and closely match the experimental data at mid-frequencies. Full article

Acoustic emission (AE) and electromagnetic radiation (EMR) arise because of material destruction and are used for the monitoring of materials and structures. This article presents an overview of AE and EMI studies related to physical processes in ice and their relationship to practically significant problems of ice mechanics and remote sensing. The paper provides a review of the properties of AE and EMI in experiments on compression and bending of ice, as well as original materials in tests of beams with fixed ends, carried out in laboratory and natural conditions. Methods and results of AE and EMR measurements in rock and ice failure processes are compared and discussed in the paper. It was found that the EMI signal spectra measured in the 0.5–10 MHz range in laboratory tests with fixed-end beams were in a higher frequency range compared to the EMR properties measured in earlier uniaxial compression tests. The obtained EMR spectra correspond to eigen frequencies of Rayleigh waves trapped near ice cracks with diameter of ~1 mm and smaller. Full article

Microelectronic packaging is crucial for protecting, powering, and interconnecting semiconductor chips, playing a critical role in the functionality and reliability of electronic devices. With the growth in complexity and miniaturization of these products, the implementation of efficient inspection techniques becomes crucial in preventing failures that may result in device malfunctions. This review paper examines the progress made in utilizing Scanning Acoustic Microscopy (SAM) to assess the structural integrity of microelectronic systems within the broader field of Nondestructive Evaluation/Testing (NDE/T) methods. With an exclusive emphasis on SAM, we point out SAM technological advancements in multi-die stacking, Through Silicon Vias (TSV), and hybrid bonding inspection that improve inspection sensitivity and resolution required to be prepared for upcoming challenges accompanying 3D- and heterogeneous integration architectures. Some of these approaches compromise the depth of inspection for the benefit of lateral resolution, while others do not sacrifice the in-depth range of evaluation. These developments are of the utmost importance in addressing the substantial obstacles associated with examining microelectronic packages, facilitating the early detection of potential failures, and enhancing the reliability and robustness of semiconductor devices. Furthermore, our discussion consists of the fundamental principles and practical approaches of SAM. It also examines recent investigations that integrate SAM with machine learning concepts and the application of deep learning models in order to automate defect detection and characterization, thus substantially augmenting the efficiency of microelectronic package assessments. Full article

Road transport in the European Union is responsible for approximately 60% of PM10 emissions and 45% of PM2.5 emissions. Acoustic agglomeration is researched to be the most effective after-treatment method to control particle pollution. Recent experimental research suggests that at a frequency of around 20 kHz and a sound pressure level of 140 dB, particles can be agglomerated. The kinetic energy of the particles is influenced by the presence of acoustics, and this enhances the collision efficiency between the particles. These collided fine particles increase in size and can be easily filtered through conventional filters. Additionally, clean burning biofuels produce comparatively fewer particles; hence RME is used for experiments along with its two blends of isopropanol (RME95I5 and RME90I10). The results are then compared to those of standard diesel fuel. With an increase in load, an average reduction of 20% in fine particles is observed along with an increase in large-sized particles. The aggregation of smaller particles is observed in a range of 0–50% in almost all tested conditions. With the increase in isopropanol from 5 to 10%, oxygen content in the fuel increased by 7%, a 1% reduction in carbon and a 2% reduction in C/H ratio is observed which led to a 6 and 9% reduction in particle emissions at 60 Nm and 90 Nm, respectively. At higher loads, D100, RME95I5 and RME90I10 recorded an agglomeration of 10%, 111% and 189%, respectively. Similar results are observed for the tendency for agglomeration at lower loads. Full article

Transport is a major contributor to anthropogenic greenhouse gases, making accurate assessment of vehicle emissions essential for climate change mitigation. This study develops a comparative machine learning framework to predict CO₂ emissions from internal combustion engines (ICEs) and hybrid electric vehicles (HEVs), using data from the UK Vehicle Certification Agency. In addition to standard technical variables, the study considers noise level, a factor seldom integrated into emission modeling, reflecting potential interactions between acoustic conditions and vehicular emission patterns. Explainable machine learning techniques, including accumulated local effects, are employed to clarify how engine capacity, fuel consumption and pollutant indicators influence CO₂ outputs under different driving conditions. Results show that medium- and high-speed driving dominate ICE emissions, whereas HEVs maintain lower emissions except under high power demand. By combining predictive modeling with interpretability, the study advances environmental informatics and provides actionable insights for low-carbon vehicle design, emission standards and sustainable transportation policies aligned with global climate goals. Full article

A previous analysis of Antarctic acoustic data relevant to quantifying frazil contributions to sea ice accretion is reconsidered to address inconsistencies with river frazil results acquired with similar instrumentation but augmented to suppress instrument icing. It was found that sound attenuation by consequent icing limited credible Antarctic acoustic frazil measurements to afternoon and early evening periods, which are shown to encompass daily minimums in frazil production. This reality was masked by use of an unvalidated liquid oblate spheroidal frazil characterization model, which greatly overestimated frazil concentrations. Much lower frazil contents were derived for these periods using a robust 2-frequency characterization algorithm, which incorporated a validated, alternative theory of scattering by elastic solid spheres. Physical arguments based on these results and instrument depth data were strongly suggestive of maximal but, currently, unquantified frazil presences during unanalyzed heavily iced late evening and morning time periods. Full article

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

To achieve the goal of climate neutrality set by the European Union, it is important to find an efficient strategy to simultaneously lower nitrogen oxide, carbon monoxide, and particle emissions. When a portion of exhaust gas is reintroduced back into the combustion chamber, it reduces the combustion temperature. This reduces NO_X emissions but has a negative impact on CO and particle emissions due to the lower concentration of O₂. Reducing the combustion temperature can also indirectly influence particle formation. By including an oxygen-rich alternative fuel, CO emissions are reduced by 28% and 33% at 60 and 90 Nm, respectively. To further reduce particle emissions, which have significant health risks, acoustic waves are introduced to achieve better filtration through conventional DPFs that filter particles with larger diameters. With 21 kHz of acoustic frequency and 0% EGR, a 6% increase in large particles is observed. With moderate rise in the recirculation percentage, a higher combined efficiency of EGR and acoustic waves is observed. With 21 kHz acoustic frequency and 10% EGR, a 73% increase in larger particles is observed at lower loads and a 32% increase at higher loads is observed. Simultaneous emission reduction can be achieved by combining the benefits of using oxygen-rich fuel, acoustics, and EGR at a moderate rate. Full article

With the advancement of scientific research, understanding the physical parameters governing acoustic wave propagation in sea ice has become increasingly important. Among these parameters, shear wave velocity plays a crucial role. However, as measurements progressed, it became apparent that there was a large discrepancy between measured values of shear waves and predictions based on empirical formulas or existing models. These inconsistencies stem primarily from the complex internal structure of natural sea ice, which significantly influences its physical behavior. Research reveals that shear wave velocity is not only influenced by bulk properties such as density, temperature, and stress state but is also sensitive to microstructural features, including air bubbles, inclusions, and ice crystal orientation. Compared to longitudinal wave velocity, the characterization of shear wave velocity is far more challenging due to these inherent complexities, underscoring the need for more precise measurement and modeling techniques. To address the challenges posed by the complex internal structure of natural sea ice and improve prediction accuracy, this study introduces a novel, integrated approach combining simulation, measurement, and inversion intelligent learning model. First, a laboratory-based method for generating sea ice layers under controlled formation conditions is developed. The produced sea ice layers align closely with measured values for Poisson’s ratio, multi-year sea ice density, and uniaxial compression modulus, particularly in the high-temperature range. Second, enhancements to shear wave velocity measurement equipment have been implemented. The improved device achieves measurement accuracy exceeding 1%, offers portability, and meets the demands of high-precision experiments conducted in harsh polar environments. Finally, according to the characteristics of small sample data. The ANN neural network was improved to a deep residual neural network with the addition of Squeeze-and-Excitation Attention (SE-ResNet) to predict longitudinal and transverse wave velocities. This prediction method improves the accuracy of shear and longitudinal wave velocity prediction by 24.87% and 39.59%, respectively, compared to the ANN neural network. Full article

This study aimed to compare the efficiency of four extraction methods, hot water (HW), hot alkaline (HA), ultrasound-assisted water (UW), and ultrasound-assisted alkaline (UA), for extracting crude β-glucan from Lentinula edodes, focusing on yield, functionality, and antidiabetic potential. The response surface methodology was used to optimize extraction conditions. Among all methods, UW yielded the highest β-glucan content (34.51 ± 0.82 g/100 g dry extract), indicating enhanced extraction efficiency through acoustic cavitation. However, HW demonstrated the most preserved structural integrity, exhibiting superior and consistent swelling power across all tested pH conditions, which indicated an excellent water-holding capacity. The ability of HA to scavenge antioxidants was significantly higher than that of other methods, likely due to the enhanced release of phenolic residues under alkaline conditions. UA showed the most potent inhibition against α-amylase (IC₅₀ = 1.46 mg/mL) and α-glucosidase (IC₅₀ = 1.21 mg/mL), demonstrating the potential for type 2 diabetes management. These results highlight that while UW is optimal for yield, HW preserves functional integrity, HA enhances antioxidant properties, and UA is promising for enzyme inhibition. The findings provide insights into tailoring extraction strategies for targeted functional or nutraceutical applications. Full article

Fiber optic distributed acoustic sensing (DAS) technology can monitor vibrational strain of vast areas with fine spatial resolution at high sampling rates. The fiber optic cable portion of DAS may directly monitor, measure, and map potentially unsafe areas such as thin ice sheets. Once the fiber optic cable is emplaced, DAS can provide “rapid-response” information along the cable’s length through remote sampling. A field campaign was performed to test the sensitivity of DAS to spatial variations within thin ice sheets. A pilot field study was conducted in the northeastern United States in which fiber-optic cable was deployed on the surface of a freshwater pond. Phase velocity transformations were used to analyze the DAS response to strike testing on the thin ice sheet. The study results indicated that the ice sheet was about 5 cm thick generally, tapering to about 3.5 cm within 2 m of the pond’s edge and then disappearing at the margins. After validation of the pilot study’s methodology, a follow-up experiment using DAS to collect on a rapidly deployed, surface-laid cable atop a larger freshwater pond was conducted. Using phase velocity transformations, the ice thickness along the fiber optic cable was estimated to be between 25.5 and 28 cm and confirmed via ice auger measurements along the fiber optic cable. This field campaign demonstrates the feasibility of employing DAS systems to remotely assess spatially variable properties on thin freshwater ice sheets. Full article

The environmental drivers of fin whale (Balaenoptera physalus) acoustic presence in Eastern Antarctic waters were investigated based on passive acoustic recordings from four sites, 2013–2019. Fin whale 20 Hz pulses were detected from late austral summer to early winter. Daily values of sea-ice concentration (SIC) were compared with the number of days with fin whale 20 Hz acoustic presence using a generalized additive model approach. At the Southern Kerguelen Plateau, Casey, and Dumont d’Urville sites, SIC correlated with fin whale calling activity, but less so at the Prydz site. Changes in SIC between sites resulted in variation in acoustic presence: Earlier sea-ice formation at Dumont d’Urville resulted in less acoustic presence in comparison to the Southern Kerguelen Plateau, where sea ice formed later in the season. Interannual variability in SIC impacted yearly acoustic presence, with a later onset of high SIC resulting in greater acoustic presence and later departure (migration timing) of the animals. Identifying the environmental drivers of fin whale presence is key to informing how this migratory species may be affected by environmental variability resulting from climate change. Full article

As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial interest in mapping the marine ecosystem in this remote and until now largely inaccessible ocean. We used the R/V Kronprins Haakon during surveys in the Central Arctic Ocean (CAO) in 2022 and 2023 to record the marine ecosystem using modern fisheries acoustics and net sampling. The 2022 survey reached all the way to the North Pole. In a first, principally manually based post-processing of these acoustic recordings using the Large-Scale Survey Post-processing System (LSSS), much effort was used to remove segments of noise due to icebreaking operations. In a second, more sophisticated post-processing, the KORONA module of LSSS with elements of machine learning was applied for further noise reduction and to allocate the area back-scattering recordings to taxonomic groups as order, families and even species of fish and plankton organisms. These results highlight the need for further advances in post-processing systems to enable the direct allocation of back-scattered acoustic energy to taxonomic categories, including species-level classifications. Full article