基于数据的建模,由于非线性共振而导致的亚波强制反应
Joar Axås1, Bastian Bäuerlein2,3, Kerstin Avila2,3
1Institute for Mechanical Systems, ETH Zürich, Leonhardstrasse 21, 8092, Zürich, Switzerland. jgoeransson@ethz.ch.
Scientific reports
|October 30, 2024
概括
研究人员开发了一种数据驱动的方法来检测和建模动态系统中的非线性共振. 这种方法揭示了复杂系统中隐藏的亚和声响应,即使在线性分析失败时也是如此.
科学领域:
- 非线性动力学是一种非线性动力学.
- 流体力学 流体力学 流体力学
- 复杂系统分析 复杂系统分析
背景情况:
- 在非线性动态系统中,复杂的行为往往源于共振,使能量在模式之间转移.
- 经典的线性分析可以检测线性化模式之间的共振,但非线性模态相互作用仍然不太了解.
- 现有的方法缺乏系统的方法来直接从数据中检测和建模非线性共振相互作用.
研究的目的:
- 开发一种数据驱动的方法来识别和建模非线性共振相互作用.
- 专门针对强制非线性系统中的亚和声响应,特别是那些缺乏线性共振的系统.
- 为各种非线性动态系统提供可通用的方法.
主要方法:
- 开发了一种数据驱动的方法来识别低维光谱次元组 (SSM) 上的非线性共振响应.
- 采用分析方法来证明从保守的极限中非线性共振的亚声响应的起源.
- 通过隔离和建模在流体冲洗实验中无法解释的响应模式来验证方法.
主要成果:
- 从实验和数值数据中成功识别和建模非线性共振相互作用.
- 证明了检测线性分析无法预测的亚和声响应的能力.
- 孤立和解释了流体流动动力学中以前无法解释的现象.
结论:
- 开发的数据驱动方法提供了一种系统的方法来检测和建模非线性共振.
- 这种方法增强了对由非线性模态相互作用产生的复杂行为的理解.
- 这些发现对分析和预测各种非线性系统 (包括流体动力学) 中的反应具有重要意义.
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