Parametric spectral submanifolds across Hopf bifurcations with applications to fluid dynamics.

IF 3.3 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2026-10-01 DOI:10.1063/5.0334774
James King, Bálint Kaszás, Gergely Buza, William Jussiau, George Haller
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引用次数: 0

Abstract

We investigate the persistence and regularity of spectral submanifolds (SSMs) in high-dimensional parametric dynamical systems undergoing a Hopf bifurcation. By analyzing how resonances in the linearized spectrum near bifurcation points limit the existence and smoothness of SSMs, a phenomenon that has been mostly overlooked, we show that low-order Taylor coefficients of the SSM expansion and the associated reduced dynamics persist smoothly through the bifurcation. This analysis generalizes to any local bifurcation and provides a clear estimate of the parameter ranges over which a parametric SSM model can be justified, thus illustrating how globally the model can be extended despite the presence of resonances near criticality. We demonstrate these findings on multiple examples, including a data-driven SSM approach to the lid-driven cavity flow. For that problem, we construct a parametric SSM-reduced model that accurately captures the full transition to periodic dynamics and the critical Reynolds number. These results provide a mathematical foundation for robust data- and equation-driven model reduction of fluid flows across bifurcations, enabling an accurate prediction of nonlinear dynamics across critical parameter regimes.

Hopf分岔的参数谱子流形及其在流体动力学中的应用。
研究了高维参数动力系统Hopf分岔下谱子流形的持续性和规律性。通过分析在分岔点附近线性化谱中的共振如何限制SSM的存在和平滑性,这是一种通常被忽视的现象,我们表明SSM展开的低阶泰勒系数和相关的简化动力学在分岔中平滑地持续存在。这种分析可以推广到任何局部分岔,并提供了参数化SSM模型可以证明的参数范围的清晰估计,从而说明了尽管在临界附近存在共振,但该模型如何在全局范围内扩展。我们在多个例子中展示了这些发现,包括数据驱动的SSM方法,用于盖子驱动的空腔流动。对于这个问题,我们构建了一个参数ssm简化模型,该模型准确地捕获了向周期动力学和临界雷诺数的完整过渡。这些结果为跨分支流体流动的稳健数据和方程驱动模型简化提供了数学基础,从而能够准确预测跨关键参数体系的非线性动力学。
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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