Wide-angle and high-efficiency acoustic retroreflectors enabled by many-objective optimization algorithm and deep learning models

Du, Zhanhang; Mei, Jun

doi:10.1103/physrevmaterials.7.115201

Phys. Rev. Materials

2023

DOI: 10.1103/physrevmaterials.7.115201

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Wide-angle and high-efficiency acoustic retroreflectors enabled by many-objective optimization algorithm and deep learning models

Zhanhang Du,

Jun Mei

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2024

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Simultaneous manipulation of longitudinal and transverse elastic waves with a sharp focusing effect and customizable energy splitting ratios

Peng,

Fan,

Mei

2024

Journal of Applied Physics

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Mode coupling and conversion between longitudinal and transverse modes are ubiquitous and universal in elastic waves, presenting a challenge in realistic applications such as nondestructive evaluation and geological exploration, where independent and separate manipulation of each mode is demanded. In this article, we propose a design of elastic metalens that can realize a high-efficiency focusing for the longitudinal wave and a V-shaped converging pattern for the transverse wave at the same time. The metalens is constructed from a metagrating, where each meta-atom has a simple configuration and renders high diffraction efficiency even for large steering angles, enabled by concurrent utilization of grating diffraction theory and advanced optimization algorithms. Interestingly, an arbitrary energy splitting ratio between the reflected longitudinal and transverse waves can be obtained by precisely controlling the coupling strength and conversion efficiency between them, providing improved flexibility and adaptability to various application environments. Two illustrative examples with a sharp focusing effect and tailored conversion efficiency are explicitly demonstrated, with a 50/50 energy splitting ratio between the longitudinal and transverse waves in the first case, and a 70/30 ratio in the second one.

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Simultaneous manipulation of longitudinal and transverse elastic waves with a sharp focusing effect and customizable energy splitting ratios

Peng,

Fan,

Mei

2024

Journal of Applied Physics

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Deep-subwavelength manipulation of waterborne acoustic waves using homogeneous membrane-type reflective metasurfaces

Hsu,

Alwi,

Lin

et al. 2024

AIP Advances

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In this study, a homogeneous membrane-type reflective metasurface is proposed to achieve the efficient manipulation of waterborne sounds. The metasurface is single-phase and constitutes only a stiff metal material (stainless steel); furthermore, its thickness can reach the deep-subwavelength range. Each metasurface unit is designed to comprise a cavity capped with a vibrating mass-loaded membrane. We adopt the finite-element method for the dynamic analysis of the fluid–structure interaction problem to simulate the vibroacoustic coupling fields induced by the waterborne acoustic metasurface. We demonstrate that the metasurface units can exhibit high reflectivity for waterborne sound and that vibroacoustic coupling effects are sensitive to variations in the width of the load mass. This enables phase shifts covering the entire 2π range. We reveal the working mechanisms of the membrane structure by examining the vibration modes. The examination reveals that mass–spring resonance and continuum bending vibrations cause steep phase variations in the short- and long-mass regimes, respectively. By adapting the phase profiles, we demonstrate functional manipulation by achieving anomalous reflections, retroreflections, acoustic focusing, and self-bending beams. Our results can potentially facilitate the practical application of metasurfaces in sustainable, lightweight, and high-performance waterborne acoustic devices.

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Application of machine learning on the design of acoustic metamaterials and phonon crystals: a review

Chen,

Huang,

et al. 2024

Smart Mater. Struct.

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This comprehensive review explores the design and applications of machine learning techniques to acoustic metamaterials (AMs) and phononic crystals (PnCs), with a particular focus on deep learning. AMs and PnCs, characterized by artificially designed microstructures and geometries, offer unique acoustic properties for precise control and manipulation of sound waves. Machine learning, including deep learning, in combination with traditional artificial design have promoted the design process, enabling data-driven approaches for feature identification, design optimization, and intelligent parameter search. Machine learning algorithms process extensive acoustic metamaterial data to discover novel structures and properties, enhancing overall acoustic performance. This review presents an in-depth exploration of applications associated with machine learning techniques in AMs and PnCs, highlighting specific advantages, challenges and potential solutions of applying of using machine learning algorithms associated with machine learning techniques. By bridging acoustic engineering and machine learning, this review paves the way for future breakthroughs in acoustic research and engineering.

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Wide-angle and high-efficiency acoustic retroreflectors enabled by many-objective optimization algorithm and deep learning models

Cited by 5 publications

References 50 publications

Simultaneous manipulation of longitudinal and transverse elastic waves with a sharp focusing effect and customizable energy splitting ratios

Simultaneous manipulation of longitudinal and transverse elastic waves with a sharp focusing effect and customizable energy splitting ratios

Deep-subwavelength manipulation of waterborne acoustic waves using homogeneous membrane-type reflective metasurfaces

Application of machine learning on the design of acoustic metamaterials and phonon crystals: a review

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