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Machine learning extreme acoustic non-reciprocity in a linear waveguide with multiple nonlinear asymmetric gates

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Abstract

This work is a study of acoustic non-reciprocity exhibited by a passive (i.e., with no active or semi-active feedback) one-dimensional (1D) linear waveguide incorporating two local strongly nonlinear, asymmetric gates. Strong coupling between the constituent oscillators of the linear waveguide is assumed, resulting in broadband capacity for wave transmission in its passband. Two local nonlinear gates break the symmetry and linearity of the waveguide, yielding strong global non-reciprocal acoustics, in the way that extremely different acoustical responses occur depending on the side of application of harmonic excitation, that is, for left-to-right (L–R) or right-to-left (R–L) wave propagation. To the authors’ best knowledge that the present two-gated waveguide is capable of extremely high acoustic non-reciprocity, at a much higher level to what is reported by active or passive devices in the current literature; moreover, this extreme performance combines with acceptable levels of transmissibility in the desired (preferred) direction of wave propagation. Machine learning is utilized for predictive design of this gated waveguide in terms of the measures of transmissibility and non-reciprocity, with the aim of reducing the required computational time for high-dimensional parameter space analysis. The study sheds new light into the physics of these media and considers the advantages and limitations of using neural networks to analyze this type of physical problems. In the predicted desirable parameter space for intense non-reciprocity, the maximum transmissibility reaches as much as 40%, and the transmitted energy from upstream (i.e., the part of the waveguide where the excitation is applied) to downstream (i.e., in the part of the waveguide after the two nonlinear gates) varies by up to nine orders of magnitude, depending on the direction of wave transmission. The machine learning tools along with the numerical methods of this work can inform predictive designs of practical non-reciprocal waveguides and acoustic metamaterials that incorporate local nonlinear gates. The current paper shows that combinations of nonlinear gates can lead to extremely high non-reciprocity while maintaining desired levels of transmissibility. This could lead to future investigation of how multiple nonlinear gates can be used as building blocks of designs incorporating robust passive acoustic non-reciprocity.

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Authors and Affiliations

  1. Department of Mechanical Science and Engineering, University of Illinois, Urbana-Champaign, Champaign, USA

    Anargyros Michaloliakos & Alexander F. Vakakis

  2. Department of Mechanical and Aerospace Engineering, University of California, San Diego, USA

    Chongan Wang

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  1. Anargyros Michaloliakos
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  2. Chongan Wang
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  3. Alexander F. Vakakis
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Correspondence to Anargyros Michaloliakos.

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Michaloliakos, A., Wang, C. & Vakakis, A.F. Machine learning extreme acoustic non-reciprocity in a linear waveguide with multiple nonlinear asymmetric gates. Nonlinear Dyn 111, 17277–17297 (2023). https://doi.org/10.1007/s11071-023-08765-4

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  • DOI: https://doi.org/10.1007/s11071-023-08765-4

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