Acoustic metamtaterial and metasurface composed of meta-atoms and meta-molecules

Acoustic metamaterials (AMMs) and acoustic metasurfaces (AMSs) are artificially structured materials with the unique properties not found in natural materials. We reviewed herein the properties of AMM and AMS that have been designed using the meta-atoms of split hollow spheres (SHSs) and hollow tubes (HTs) or meta-molecules of split hollow tubes (SHTs) with local resonance. AMMs composed of SHSs or HTs display a transmission dip with negative modulus or negative mass density. AMMs composited with SHSs and HTs present a transmission peak and a phase fluctuation in the overlapping resonant frequency region, indicating that they simultaneously have a negative modulus and a negative mass density. Furthermore, the meta-molecule AMMs with SHTs also exhibit double-negative properties. Moreover, the acoustic meta-atoms or meta-molecules can be used to fabricate acoustic topological metamaterials with topologically protected edge states propagation. These meta-atoms and meta-molecules can also attain phase discontinuity near the resonant frequency, and thus they can be used to design AMSs with the anomalous manipulation for acoustic waves. The various tunability of the meta-molecules provides a feasible path to achieve broadband AMS.

Low-Frequency Bandgaps of the Lightweight Single-Phase Acoustic Metamaterials with Locally Resonant Archimedean Spirals

In order to achieve the dual needs of single-phase vibration reduction and lightweight, a square honeycomb acoustic metamaterials with local resonant Archimedean spirals (SHAMLRAS) is proposed. The independent geometry parameters of SHAMLRAS structures are acquired by changing the spiral control equation. The mechanism of low-frequency bandgap generation and the directional attenuation mechanism of in-plane elastic waves are both explored through mode shapes, dispersion surfaces, and group velocities. Meanwhile, the effect of the spiral arrangement and the adjustment of the equation parameters on the width and position of the low-frequency bandgap are discussed separately. In addition, a rational period design of the SHAMLRAS plate structure is used to analyze the filtering performance with transmission loss experiments and numerical simulations. The results show that the design of acoustic metamaterials with multiple Archimedean spirals has good local resonance properties, and forms multiple low-frequency bandgaps below 500 Hz by reasonable parameter control. The spectrograms calculated from the excitation and response data of acceleration sensors are found to be in good agreement with the band structure. The work provides effective design ideas and a low-cost solution for low-frequency noise and vibration control in the aeronautic and astronautic industries.

Intelligent on-demand design of phononic metamaterials

With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions.