We report a kind of sound absorbing metastructure based on micro-perforated panel (MPP) and impedance matching coiled-up cavity (IMCC), which can broaden the low-frequency working bandwidth tremendously using single unit (e.g., 3.13 times compared to conventional designs) within a deep sub-wavelength thickness (e.g., ∼λ/20 at 322 Hz). Physically, impedance matching stemming from the dramatic cross-sectional area contrast of coiled-up channels reduces the acoustic reactance variation with respect to frequency, leading to broader low-frequency bandwidth. Theoretical predictions of sound absorption coefficients of the metastructure coincide well with the numerical and experimental results. The band broadening characteristics of the proposed IMCC are confirmed in relatively wide range of MPP parameters and partition panel positions. This work contributes to expanding the designing degrees of freedom for coiled-up metastructures to manipulate low-frequency waves in broadband.

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

We report a kind of coiled-up meta-liners with nonuniform cross sections (CMNC), which can efficiently attenuate low-frequency sound waves under grazing flow with a deep subwavelength thickness (e.g., ∼λ/17 at 500 Hz). At a grazing flow Mach number of 0.26, the average transmission loss of the meta-liner is 12.6 dB at 500–1000 Hz, which is twice as much as that of a double-degree-of-freedom acoustic liner of the same size. Physically, the nonuniform cross-sectional distribution and significant cross-sectional area ratio enhances vortex shedding, thus resulting in severe acoustic energy dissipation. The excellent low-frequency acoustic attenuation performance of CMNC is investigated thoroughly with experimental, theoretical, and numerical methods. This work provides an avenue for low-frequency noise reduction in grazing flow scenarios (e.g., in a high bypass ratio turbofan engine).

Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy.