Search

The targeted concentration of acoustic waves has significant implications for industrial nondestructive testing, ultrasound diagnosis, and medical treatment. Most conventional sound-focusing metasurfaces suffer from an untunable focus, narrow bandwidth, and fixed geometric configurations, which severely constrain their practical utility. In this paper, we propose a reconfigurable acoustic coding metasurface composed of two coding units with high transmittance and transmitted phases of 0 and π for realizing tunable and broadband sound focusing. Through the straightforward manipulation of each unit structure and alterations in the coding sequences, precise control of the focus position across the entire working plane is attainable, enabling both tunable axial-axis and off-axis sound-focusing effects. Moreover, the sound-focusing performance of the proposed metasurface is excellent within a broad frequency range from 3000 to 5500 Hz. The experimental results are consistent with theoretical expectations and numerical simulations. This work lays a practical foundation for the design of acoustic devices for tunable and broadband sound focusing. [ABSTRACT FROM AUTHOR]

The mode manipulation of Lamb waves plays an important role in damage detection and identification of damage types, location, and size. In this paper, we propose a composite phononic crystal (PC) plate with antisymmetric and symmetric PCs for realizing mode conversion from A0 to S0 mode of Lamb waves. The theoretical analysis, numerical simulations, and experimental validation are introduced and the mode conversion mechanism of the composite PC plate is systematically investigated. The effect of geometrical parameters on band structures of antisymmetric and symmetric PCs is first discussed. Then multi-physics field simulation models are developed and in-plane displacement fields are obtained in numerical simulations, which shows that the mode conversion is enhanced when the period number of the antisymmetric PC decreases and that of symmetric the PC increases. The composite PC plate specimens are fabricated with precision wire cutting technology for experimental measurements and the self-designed polyvinylidene fluoride (PVDF) comb transducer is used to stimulate the Lamb waves. The experimental results are consistent with the numerical simulations, which demonstrate that the proposed composite PC plate can achieve the mode conversion from A0 to S0 mode of Lamb waves. Our proposed structures have applicable values for the mode manipulation of Lamb waves in damage detection. [ABSTRACT FROM AUTHOR]

Detection and evaluation of micro-damages in the early stages of engineering failure are crucial for various industrial structures to ensure their safety and prevent further catastrophic accidents. The nonlinear ultrasonic technique (NUT) has gained increasing popularity and recognition for breaking through the detection sensitivity limit upon micro-damages that usually are invisible to conventional linear techniques. However, it remains an ongoing challenge to quantitatively characterize micro-damages using NUT due to great difficulties in fully modeling the complicated interaction mechanism between the nonlinear ultrasonic waves and micro-damages. This work presents a data-driven perspective for solving multiparameter underdetermined inverse problems that are at the core of NUT, while allowing by-passing the creation of high-fidelity physics-based models. Nonlinear Lamb wave measurements with group-velocity mismatching are conducted to introduce both size and localization information of damages to the assembled dataset. A nonlinearity-aware discrete wavelet transform-bidirectional long short-term memory network is proposed to directly process nonlinear ultrasonic responses to automatically model latent nonlinear dynamics, thus establishing the complex mapping between the nonlinear ultrasonic signals and the multi-dimensional damage features. In particular, an attempt is made to augment the physical explainability of the proposed deep learning approach through a frequency component importance analysis. The trained network enables accurate and explainable predictions of length and localization of closed cracks and robustness against varying degrees of noise. Our work paves a promising and practical way to promote the transformation of NUT from the qualitative analysis for accurate and efficient quantitative prediction. [ABSTRACT FROM AUTHOR]