Machine Learning Extreme Acoustic Non-reciprocity in a Linear Waveguide
with Multiple Nonlinear Asymmetric Gates
- URL: http://arxiv.org/abs/2302.01746v1
- Date: Thu, 2 Feb 2023 17:28:04 GMT
- Title: Machine Learning Extreme Acoustic Non-reciprocity in a Linear Waveguide
with Multiple Nonlinear Asymmetric Gates
- Authors: Anargyros Michaloliakos, Chongan Wang, Alexander F. Vakakis
- Abstract summary: This work is a study of acoustic non-reciprocity exhibited by a passive one-dimensional linear waveguide incorporating two local strongly nonlinear, asymmetric gates.
The maximum transmissibility reaches as much as 40%, and the transmitted energy from upstream to downstream varies up to nine orders of magnitude, depending on the direction of wave propagation.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This work is a study of acoustic non-reciprocity exhibited by a passive
one-dimensional linear waveguide incorporating two local strongly nonlinear,
asymmetric gates. 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. 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 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 to downstream varies 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.
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