Exploring localization in nonlinear oscillator systems through network-based predictions.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-05-01 DOI:10.1063/5.0265366
Charlotte Geier, Norbert Hoffmann
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引用次数: 0

Abstract

Localized vibrations, arising from nonlinearities or symmetry breaking, pose a challenge in engineering, as the resulting high-amplitude vibrations may result in component failure due to fatigue. During operation, the emergence of localization is difficult to predict, partly because of changing parameters over the life cycle of a system. This work proposes a novel, network-based approach to detect an imminent localized vibration. Synthetic measurement data are used to generate a functional network, which captures the dynamic interplay of the machine parts, complementary to their geometric coupling. Analysis of these functional networks reveals an impending localized vibration and its location. The method is demonstrated using a model system for a bladed disk, a ring composed of coupled nonlinear Duffing oscillators. Results indicate that the proposed method is robust against small parameter uncertainties, added measurement noise, and the length of the measurement data samples. The source code for this work is available at C. Geier [(2024). "Code for paper Exploring localization in nonlinear oscillator systems through network-based predictions," Zenodo. https://doi.org/10.5281/zenodo.12611988].

通过基于网络的预测探索非线性振荡器系统的局部化。
局部振动是由非线性或对称破缺引起的,在工程上是一个挑战,因为由此产生的高振幅振动可能导致部件因疲劳而失效。在运行过程中,局部化的出现很难预测,部分原因是系统生命周期中参数的变化。这项工作提出了一种新颖的,基于网络的方法来检测迫在眉睫的局部振动。综合测量数据用于生成一个功能网络,该网络捕获机器部件的动态相互作用,补充了它们的几何耦合。对这些功能网络的分析揭示了即将发生的局部振动及其位置。用一个由耦合非线性Duffing振子组成的叶片盘模型系统对该方法进行了验证。结果表明,该方法对小参数不确定性、附加测量噪声和测量数据样本长度具有较强的鲁棒性。此工作的源代码可在C. Geier[(2024)]获得。“通过基于网络的预测探索非线性振荡器系统的定位”,Zenodo。https://doi.org/10.5281/zenodo.12611988]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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