WeChat Mini Program
Old Version Features

    • Digital Programming of Reciprocity Breaking in Resonant Piezoelectric Metamaterials

    Mustafa Alshaqaq,Christopher Sugino,Alper Erturk

    PHYSICAL REVIEW RESEARCH（2023）

    Georgia Inst Technol

    Cited 4|Views9
    Abstract
    We demonstrate a digitally controlled piezoelectric metamaterial waveguide leveraging resonant, spatiotem-porally modulated synthetic impedance circuits for programmable reciprocity breaking. Piezoelectric metamaterials have effective stiffness that depends on the shunt circuitry connected to each unit cell, offering greatly increased design freedom over their purely mechanical counterparts. By connecting a digitally controlled synthetic impedance shunt circuit to each unit cell of the metamaterial domain, the effective stiffness is externally programmed according to a desired profile in space and time. Specifically, we present threefold capabilities in this electromechanical system: (1) smooth parameter modulation (no abrupt switching) through synthetic impedance circuits that eliminate cumbersome analog electrical components, (2) resonant electromechanical modulation in space and time so that one does not have to operate near the Bragg band gap, and (3) precise digital programming by numerically entering the space and time properties of the domain. We also demonstrate the frequency conversion in narrow-band excitation centered at a directional band gap. The experimental results are compared against high-fidelity multiphysics finite-element simulations, yielding excellent agreement for this class of digitally programmable nonreciprocal elastic metamaterials.
    More
    Translated text
    Key words
    Metamaterials,Acoustic Metamaterials,Terahertz Metamaterials,Piezoelectric Materials
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    收藏
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined