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arXiv 2602.13899physics.flu-dynnlin.CD
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湍流动力系统中的因果一致结构

Causally coherent structures in turbulent dynamical systems

Massachusetts Institute of Technology(麻省理工学院);The University of Manchester(曼彻斯特大学);Friedrich–Alexander–Universität Erlangen–Nürnberg (FAU)(埃尔朗根-纽伦堡大学)

Daniele Massaro, Saleh Rezaeiravesh, Philipp Schlatter

AI总结:

本研究利用香农转移熵识别湍流边界层中的因果一致结构,揭示了不同层间的能量级联机制,并拓展了混沌动力系统分析方法。

摘要:

在高维、混沌、非线性动力系统中提取时空相干性仍然是物理、数学和工程领域的一个基本挑战,例如湍流流动。在本工作中,我们采用香农转移熵(TE)来识别零压力梯度湍流边界层(TBL)中的因果一致运动。这种因果度量基于信息论,能够利用相应的时间序列识别动力系统中的源和目标。然而,TE需要对各种超参数进行复杂的调整,如源的马尔可夫阶(m),这在壁湍流中可能在空间上变化。在这里,我们提出了自适应调整,并讨论了m在不同TBL中的影响。我们引入了因果一致结构(CCS)的概念,即一致结构被解释为因果的时空模式。此外,净转移熵流量也被用来识别作用于边界层的源或目标位置。标准粘性层、对数层和外层通过信息流量特征化,例如突出显示内层和外层之间主导的自上而下的相互作用,类似于经典的能量级联。这项工作扩展了文献中已使用的技术,如相关性和谱分析,并提出了一种本质上通用且适用于广泛混沌动力系统的方法,应用于认知科学、系统生物学和金融领域。

英文摘要:

The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathematics and engineering. In this work, we employ Shannon transfer entropy (TE) to identify causally coherent motions in a zero-pressure-gradient turbulent boundary layer (TBL). This causality metric, rooted in information theory, enables the identification of sources and targets in dynamical systems using the corresponding time series. However, TE requires sophisticated tuning of various hyperparameters, such as the Markovian order of the source ($m$), which can spatially vary in wall-bounded turbulent flow. Here, we present an adaptive tuning and discuss the influence of $m$ across different TBLs. We introduce the concept of causally coherent structures (CCS), i.e. coherent structures interpreted as spatio-temporal patterns of causality. Moreover, the net transfer entropy flux is also utilised to identify boundary layer locations acting either as sources or targets. The standard viscous, logarithmic, and outer layers are characterised by information fluxes, highlighting, for example, dominant top-down interactions between the inner and outer layers, analogously to the classical energy cascade. This work extends techniques previously employed in the literature, such as correlation and spectral analysis, and presents an approach that is inherently general and applicable to a wide range of chaotic dynamical systems, with applications in cognitive sciences, systems biology and finance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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