Journal of the Acoustical Society of America最新文献

The OSIRIS-REx sample return capsule's hypersonic re-entry into the atmosphere is a rare opportunity to test a variety of sonic boom source models since the projectile dimensions are well characterized. While the as-flown flight path is unknown, the predicted flight path enables a rough approximation of the source Mach number and location. Six infrasound microphones deployed in the boom carpet along the predicted flight path recorded impulsive signals from the OSIRIS-REx re-entry. Using a suite of atmosphere profiles and the geometric acoustics approximation, we estimate locations with uncertainty estimates along the flight path from which the signals were emitted. Acoustic overpressure and signal duration predictions from Whitham's far field theory, Carlson's simplified sonic boom prediction method, and a drag-dominated hypersonic model are analyzed with uncertainty estimates from the location estimate. While the Carlson simplified sonic boom prediction method could be accurate, our preference is for the drag-dominated source model. Using this source model with an inviscid Burgers's equation solver for propagation, we obtained an excellent match to the recorded data. These results will help better inform future sample return capsule re-entry observation campaigns as well as contribute to a better understanding of high altitude infrasonic sources.

A physics-informed machine learning (ML) framework for ocean acoustic source localization using matched field processing (MFP) is presented. A physics-informed neural network (PINN) predicts complex acoustic pressure fields from sparse pressure measurements and a known sound speed profile (SSP). These PINN-predicted replica fields are integrated into the MFP scheme, enabling fine-resolution source-receiver range estimation without requiring detailed geoacoustic bottom parameters. Validation with experimental data from the Shallow Water Evaluation Cell Experiment 1996 (SWellEx-96) demonstrates accurate range estimation, including in the challenging closest point of approach region. The method maintains performance when localizing from array element depths excluded during PINN training and under sparse-array configurations and moderate SSP mismatch. Compared to conventional model-based MFP, the method avoids full environmental characterization and mitigates environmental mismatch effects. Unlike purely data-driven ML methods, it incorporates the governing wave physics, producing physically consistent replicas and improving interpolation/extrapolation to ranges and array element depths that were not used in training. These results highlight the advantages of a physics-informed data-driven approach for ocean acoustic localization in realistic, data-limited environments.

A high-efficiency Terfenol-D magnetic circuit with large-section permanent magnets is proposed to improve the electroacoustic efficiency of magnetostrictive transducers. This design adopts long Terfenol-D rods with an integral drive instead of the conventional multiple short Terfenol-D rods with segmented drive, eliminating the influence of the high magnetic resistance of permanent magnets in the magnetic circuit. The static magnetic field distribution of the long Terfenol-D rod is reconstructed through an increase in the cross-sectional area of the permanent magnet using active magnetic field compensation technology, effectively compensating for the weak magnetic field in the middle of the long Terfenol-D rod. The electroacoustic efficiency of the transducer improves by increasing the output sound power without reducing the input electrical power. Then, a high-efficiency Terfenol-D magnetic circuit with large-section permanent magnets and a transducer driven by it are designed and fabricated and compared to a segmented driving magnetic circuit. The test results show that the response and electroacoustic efficiency of the transducer driven by the high-efficiency magnetic circuit at 950 Hz are 187.6 dB and 38.6%, respectively, which are approximately 2 dB and 4% higher than those of the conventional segmented magnetic circuit.

Underwater communication has garnered significant attention due to its pivotal role in the offshore development, with recent efforts particularly focused on enhancing information capacity. Here, we investigate the mode-division multiplexing and demultiplexing of acoustic waves in a coupled waveguide array. First, we theoretically derive the two-dimensional field distribution required for generating supermode propagation. The results demonstrate that the number of acoustic supermodes corresponds precisely to the number of waveguides. Subsequently, we design a 3 × 2 waveguide array and validate the plane wave-like propagation characteristics of acoustic supermodes. Remarkably, supermode propagation exhibits broadband stability, which can be effectively multiplexed with the excitation frequency to significantly enhance communication information capacity. Furthermore, by leveraging the multimode interference effect, we simultaneously achieve the demultiplexing and multiplexing of acoustic supermodes through the introduction of a phase onto the excitation mode. Finally, we demonstrate the data transmission capability by encoding four English letters, and the encoded information can be directly recognized in the output sound intensity without additional decoding operations. Capitalizing on the benefits of mode diversity and the high fidelity of the supermode propagation, our proposed approach holds great potential for advancing high-fidelity signal transmission in complex acoustic networks and underwater communication systems.

Source localization is an important structural health monitoring task, yet traditional localization techniques struggle due to complex geometries, dispersive wave propagation, and structure-medium coupling. This study applied matched field processing (MFP), a source localization technique developed for underwater acoustics, to localize impact sources on metal plates using remote acoustic measurements of airborne sound in conjunction with a physics-based acoustic-wave propagation model. A linear array of 14 microphones recorded radiated sound from a stainless-steel ball bearing dropped onto a 0.64 cm-thick, 91.4 cm-diameter aluminum plate in the nominal 5-20 kHz bandwidth. Physics-based finite element models were developed for both infinite and finite plates. The infinite plate model emphasized generic sound radiation with proper time-windowing, while the finite plate model included edge reflections specific to the plate studied. Both models achieved localization errors within 0.5 cm when data were temporally trimmed to accommodate model constraints. In environments with additive Gaussian noise, the finite plate model maintained greater than 80% localization accuracy down to a signal-to-noise ratio of -7.5 dB. Results further showed that MFP is robust to moderate mismatches in source characterization, but deviations in sensor location approaching a half-wavelength and deviations in plate thickness approaching 10% can reduce localization accuracy.

A single chamber concentric tube silencer (muffler) is modeled and studied for its fluid dynamic and acoustic properties. The silencer has also been studied for its ability to reduce noise and transfer heat to a thermoelectric generator surrounding it. Two silencer geometries are considered, one with round and the other with square cross-sections. The heat transfer to the walls of a concentric tube silencer affects the frequency bands of the silencer's overall transmission loss characteristics. The sound reduction study considers both a broadband signal source and a single pure tone source. Voltage generation is predicted with this model for a given cooling condition, which agrees with known engineering data and previous studies using these particular thermoelectric generators. The silencer wall-cooling is coupled to the general flow, heat transfer, and its acoustic performance.

Passive acoustic data can be used to estimate animal density. A key step is quantifying the range-specific detection probability for vocalizations from the target species. A method developed to estimate cetacean density from single hydrophones was applied to pygmy blue whale (Balaenoptera musculus brevicauda) "Sri Lankan" song recorded near Diego Garcia in the Indian Ocean during May 2002. Detection probability was estimated using a Monte Carlo simulation using information about transmission loss, ambient noise levels, song source levels, and the efficiency of the automatic detection process. The effect of varying source levels was explored. Song density estimates were 0.14 song units/1000 km2 h-1 [coefficient of variation (CV), 0.16; mean source level: 179 dB re 1 μPa @ 1 m] and 0.024 song units/1000 km2 h-1 (CV, 0.12; mean source level, 189 dB re 1 μPa @ 1 m). Estimating whale density additionally requires an estimate of the song production rate, which was not available. Nevertheless, estimating song unit density enables different datasets to be compared in a standardized framework. This simulation method is useful for data collected by sparsely distributed instruments, where wide instrument spacing may exclude the use of standard density estimation methods such as spatial capture-recapture and distance sampling.

Fast and accurate underwater acoustic charting is crucial for downstream tasks such as environment-aware sensor placement optimization and autonomous vehicle path planning. Conventional methods rely on computationally expensive although accurate numerical solvers, which are not scalable for large-scale or real-time applications. Although deep learning-based surrogate models can accelerate these computations, they often suffer from limitations such as fixed-resolution constraints or dependence on explicit partial differential equation formulations. These issues hinder their applicability and generalization across diverse environments. We propose Hankel-FNO, a Fourier Neural Operator (FNO)-based model for efficient and accurate acoustic charting. By incorporating sound propagation knowledge and bathymetry, our method has high accuracy while maintaining high computational speed. Results demonstrate that the Hankel-FNO outperforms traditional solvers in speed and surpasses data-driven alternatives in accuracy, especially in long-range predictions. Experiments show the model's adaptability to diverse environments and sound source settings with minimal fine-tuning.

In spoken language, iconicity, referring to the resemblance between the sound structure of words and their meaning, is often studied using pseudowords. Previously, we showed that representational dissimilarity matrices (RDMs) of the shape ratings of pseudowords correlated significantly with RDMs of acoustic parameters reflecting spectro-temporal variations; the ratings also correlated significantly with voice quality parameters. Here, we examined how perceptual ratings relate to these parameters of pseudowords across eight meaning domains. We largely replicated our previous findings for shape, while observing different patterns for other domains. Using a k-nearest-neighbor (KNN) machine-learning algorithm, we compared 4095 combinations of 12 acoustic parameters (three spectro-temporal and nine characterizing vocal quality) to determine the optimal combination associated with iconicity ratings in each domain. We found that iconic mappings were linked to domain-specific combinations of acoustic parameters. One spectro-temporal parameter, the fast Fourier transform, contributed to all domains, indicating the importance of time-varying spectral properties for iconicity judgments. We applied the KNN approach to generate shape ratings for 160 real words. These generated ratings strongly correlated with perceptual ratings of real words, indicating the value of the KNN approach to assess iconic mapping in natural languages. Our findings support the relevance of iconicity to language.