具有局部非线性非对称门的线性波导的机器学习非互易性:强耦合的情况

IF 1.9 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Chongan Wang, Alireza Mojahed, Sameh Tawfick, Alexander F. Vakakis
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引用次数: 1

摘要

摘要研究了局部非对称、耗散和强非线性栅极增强的无源线性波导的非互易性。假设波导各组成振荡器之间存在强耦合,从而产生宽带传输能力。栅极处的局部非线性和不对称性可以产生强烈的全局非互反声学,即根据波导的哪一侧施加谐波激励而产生截然不同的声学响应。观察到两种类型的高度非互反响应:(i)与应用谐波激励相比没有频率畸变的单色响应,以及(ii)具有强烈频率畸变的强调制响应(SMRs)。应用复化平均(CX-A)方法对该强非线性问题的单色解进行了解析预测,并对控制分岔进行了稳定性分析。此外,我们建立了一个机器学习框架,其中神经网络(NN)模拟器被训练以根据某些传输率和非互易性度量来预测门控波导的性能度量。神经网络大大减少了所需的仿真时间,能够在高维参数空间中确定所需性能的参数范围。在预测的非互易理想参数空间中,最大透射率达到40%,传输能量随波传播方向的变化可达三个数量级。机器学习工具以及这项工作的分析方法可以为包含局部非线性门的实用非互易波导和声学超材料的预测设计提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Non-Reciprocity of a Linear Waveguide With a Local Nonlinear, Asymmetric Gate: Case of Strong Coupling
Abstract We study nonreciprocity in a passive linear waveguide augmented with a local asymmetric, dissipative, and strongly nonlinear gate. Strong coupling between the constituent oscillators of the waveguide is assumed, resulting in broadband capacity for wave transmission. The local nonlinearity and asymmetry at the gate can yield strong global nonreciprocal acoustics, in the sense of drastically different acoustical responses depending on which side of the waveguide a harmonic excitation is applied. Two types of highly nonreciprocal responses are observed: (i) Monochromatic responses without frequency distortion compared to the applied harmonic excitation, and (ii) strongly modulated responses (SMRs) with strong frequency distortion. The complexification averaging (CX-A) method is applied to analytically predict the monochromatic solutions of this strongly nonlinear problem, and a stability analysis is performed to study the governing bifurcations. In addition, we build a machine learning framework where neural net (NN) simulators are trained to predict the performance measures of the gated waveguide in terms of certain transmissibility and nonreciprocity measures. The NN drastically reduces the required simulation time, enabling the determination of parameter ranges for desired performance in a high-dimensional parameter space. In the predicted desirable parameter space for nonreciprocity, the maximum transmissibility reaches 40%, and the transmitted energy varies by up to three orders of magnitude depending on the direction of wave transmission. The machine learning tools along with the analytical methods of this work can inform predictive designs of practical nonreciprocal waveguides and acoustic metamaterials that incorporate local nonlinear gates.
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来源期刊
CiteScore
4.00
自引率
10.00%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The purpose of the Journal of Computational and Nonlinear Dynamics is to provide a medium for rapid dissemination of original research results in theoretical as well as applied computational and nonlinear dynamics. The journal serves as a forum for the exchange of new ideas and applications in computational, rigid and flexible multi-body system dynamics and all aspects (analytical, numerical, and experimental) of dynamics associated with nonlinear systems. The broad scope of the journal encompasses all computational and nonlinear problems occurring in aeronautical, biological, electrical, mechanical, physical, and structural systems.
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