Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310027, China.
School of Information and Electrical Engineering, Zhejiang University City College, Zhejiang, 310015, China.
Sci Rep. 2022 Jul 14;12(1):12037. doi: 10.1038/s41598-022-16312-1.
Underwater acoustic metasurfaces have broad application prospects for the stealth of underwater objects. However, problems such as a narrow operating frequency band, poor operating performance, and considerable thickness at low frequencies remain. In this study a reverse design method for ultra-thin underwater acoustic metasurfaces for low-frequency broadband is proposed using a tandem fully connected deep neural network. The tandem neural network consists of a pre-trained forward neural network and a reverse neural network, based on which a set of elements with flat phase variation and an almost equal phase shift interval in the range of 700-1150 Hz is designed. A diffuse underwater acoustic metasurface with 60 elements was designed, showing that the energy loss of the metasurface in the echo direction was greater than 10 dB. Our work opens a novel pathway for realising low-frequency wideband underwater acoustic devices, which will enable various applications in the future.
水下声超表面在水下目标的隐身方面具有广阔的应用前景。然而，在低频段仍然存在工作频带窄、工作性能差、厚度大等问题。在这项研究中，提出了一种使用串联全连接深度神经网络的低频宽带超薄水下声超表面的反设计方法。串联神经网络由一个预训练的前向神经网络和一个反向神经网络组成，基于该网络设计了一组在 700-1150 Hz 范围内具有平坦相位变化和几乎相等相位间隔的单元。设计了一个具有 60 个单元的漫射水下声超表面，结果表明超表面在回声方向的能量损耗大于 10 dB。我们的工作为实现低频宽带水下声设备开辟了一条新途径，这将为未来的各种应用提供可能。
