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用于刻画气液两相流空间耦合行为的关联序模式复杂网络

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flow.

机构信息

School of Electrical Engineering and Automation, Tianjin University of Science and Technology, Tianjin 300222, China.

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.

出版信息

Chaos. 2023 Jun 1;33(6). doi: 10.1063/5.0146259.

Abstract

The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas-liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas-liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (I) and the global subnetwork clustering coefficient (C), to quantitatively measure the spatial coupling strength of different gas-liquid flow patterns. The gas-liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

摘要

两相流的复杂相位相互作用是理解流动模式演化机制的关键因素,但这些复杂的流动行为尚未得到很好的理解。在本文中,我们采用了一系列气-液两相流多元脉动信号作为观测值,并提出了一种新的互联有序模式网络来研究气-液两相流模式的空间耦合行为。此外,我们使用了两个网络指标,即全局子网互信息(I)和全局子网聚类系数(C),来定量测量不同气-液流动模式的空间耦合强度。通过在不同流动条件下计算这两个提出的耦合指标,进一步描述了气-液两相流模式的演化行为。所提出的互联有序模式网络为深入了解多相流系统的演化机制提供了一种新的工具,也可用于研究具有多个观测值的其他复杂系统的耦合行为。

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