两相流有限穿越可视图演化动力学研究

针对小管径两相流流动特性, 全新优化设计弧形对壁式电导传感器. 通过动态实验在获取传感器测量信号的基础上, 采用有限穿越可视图理论构建对应于不同流型的两相流复杂网络. 通过分析发现, 有限穿越可视图网络异速生长指数和网络平均度值的联合分布可实现对小管径两相流的流型辨识; 有限穿越可视图度分布曲线峰值可有效刻画与泡径大小分布相关的流动物理结构细节特征; 网络平均度值可表征流动结构的宏观特性; 网络异速生长指数对流体动力学复杂性十分敏感, 可揭示不同流型演化过程中的细节演化动力学特性. 两相流测量信号的有限穿越可视图分析为揭示两相流流型的形成及演化动力学机理提供了新途径. 关键词： 两相流 复杂网络 有限穿越可视图 网络异速生长指数     

Characterization of chaotic dynamic behavior in the gas–liquid slug flow using directed weighted complex network analysis

Understanding the nonlinear and complex dynamics underlying the gas–liquid slug flow is a significant but challenging problem. We systematically carried out gas–liquid two-phase flow experiments for measuring the time series of flow signals, which is studied in terms of the mapping from time series to complex networks. In particular, we construct directed weighted complex networks (DWCN) from time series and then associate different aspects of chaotic dynamics with the topological indices of the DWCN and further demonstrate that the DWCN can be exploited to detect unstable periodic orbits of low periods. Examples using time series from classical chaotic systems are provided to demonstrate the effectiveness of our approach. We construct and analyze numbers of DWCNs for different gas–liquid flow patterns and find that our approach can quantitatively distinguish different experimental gas–liquid flow patterns. Furthermore, the DWCN analysis indicates that slug flow shows obvious chaotic behavior and its unstable periodic orbits reflect the intermittent quasi-periodic oscillation behavior between liquid slug and large gas slug. These interesting and significant findings suggest that the directed weighted complex network can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of the gas–liquid slug flow.     

Markov transition probability-based network from time series for characterizing experimental two-phase flow

We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.     

多元时间序列复杂网络流型动力学分析

针对气液两相流流动特性,利用有限元分析方法设计变曲率对壁式电导传感器.采用设计加工的传感器在多相流装置上进行气液两相流动态实验,并测得多组对应于不同流型的电导波动信号. 基于测量数据,采用多元时间序列复杂网络构建算法构建对应于不同流型的复杂网络.在此基础上, 对网络的社团特性进行了分析, 研究发现,不同的社团结构对应于不同的流型,而社团内部网络特征可有效刻画不同流型内在动力学特性.多元时间序列复杂网络分析可为两相流流型演化动力学特性研究及流型识别提供新理论、开拓新途经.     

两相流流型动力学特征多尺度递归定量分析

基于垂直上升管中测取的气液两相流电导波动信号,采用递归定量分析方法,从多尺度角度研究了气液两相流泡状流、段塞流及混状流三种典型流型的动力学运动特征.研究结果表明,低频泡状流及混状流在递归图表现为沿对角线方向比较发育的混沌递归线条纹理特征,表明了低频运动的泡状流及混状流具有较好的确定性运动行为,而随着泡状流及混状流运动频率增加,混沌递归特征变差,其运动特征逐渐向随机方向发展.对于段塞流,在混沌递归图上逐渐呈现间歇的矩形块纹理结构,且段塞流中液塞与气塞的间歇运动特征出现在高频段,而段塞流中的泡状流运动则出现在低频段上,且随着泡状流运动频率增加,泡状流逐渐失去确定性运动行为.表明了基于电导波动信号的多尺度非线性分析方法是理解与表征气液两相流动力学特性的有效途径. 关键词： 两相流 流动特性 多尺度分析 递归分析     

Complex network analysis in inclined oil--water two-phase flow

Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling many complex natural and artificial systems. Oil--water two-phase flow is one of the most complex systems. In this paper, we use complex networks to study the inclined oil--water two-phase flow. Two different complex network construction methods are proposed to build two types of networks, i.e. the flow pattern complex network (FPCN) and fluid dynamic complex network (FDCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K-means clustering, useful and interesting results are found which can be used for identifying three inclined oil--water flow patterns. To investigate the dynamic characteristics of the inclined oil--water two-phase flow, we construct 48 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of the inclined oil--water two-phase flow. In this paper, from a new perspective, we not only introduce a complex network theory into the study of the oil--water two-phase flow but also indicate that the complex network may be a powerful tool for exploring nonlinear time series in practice.     

Cross over of recurrence networks to random graphs and random geometric graphs

Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.     

Public channel cryptography by synchronization of neural networks and chaotic maps

Two different kinds of synchronization have been applied to cryptography: synchronization of chaotic maps by one common external signal and synchronization of neural networks by mutual learning. By combining these two mechanisms, where the external signal to the chaotic maps is synchronized by the nets, we construct a hybrid network which allows a secure generation of secret encryption keys over a public channel. The security with respect to attacks, recently proposed by Shamir et al., is increased by chaotic synchronization.     

Robust dynamical effects in traffic and chaotic maps on trees

In the dynamic processes on networks collective effects emerge due to the couplings between nodes, where the network structure may play an important role. Interaction along many network links in the nonlinear dynamics may lead to a kind of chaotic collective behavior. Here we study two types of well-defined diffusive dynamics on scale-free trees: traffic of packets as navigated random walks, and chaotic standard maps coupled along the network links. We show that in both cases robust collective dynamic effects appear, which can be measured statistically and related to non-ergodicity of the dynamics on the network. Specifically, we find universal features in the fluctuations of time series and appropriately defined return-time statistics.      

Isochronal synchronization in networks and chaos-based TDMA communication

Pairs of delay-coupled chaotic systems were shown to be able to achieve isochronal synchronization under bidirectional coupling and self-feedback. Such identical-in-time behavior was demonstrated to be stable under a set of conditions and to support simultaneous bidirectional communication between pairs of chaotic oscillators coupled with time-delay. More recently, it was shown that isochronal synchronization can emerge in networks with several hundreds of oscillators, which allows its exploitation for communication in distributed systems. In this paper, we introduce a conceptual framework for the application of isochronal synchronization to TDMA communication in networks of delay-coupled chaotic oscillators. On the basis of the stable and identical-in-time behavior of delay-coupled chaotic systems, the chaotic dynamics of distributed oscillators is used to support and sustain coordinate communication among nodes over the network. On the basis of the unique features of chaotic systems in isochronal synchronization, the chaotic signals are used to timestamp clock readings at the physical layer such that logical clock synchronization among the nodes (a prerequisite for TDMA) can be exploited using the same basic structure. The result is a standalone network communication scheme that can be advantageously applied in the context of ad-hoc networks or alike, especially short-ranged ones that yield low values of time-delay. As explored to its depths in practical implementations, this conceptual framework is argued to have potential to provide gain in simplicity, security and efficiency in communication schemes for autonomous/standalone network applications.     

Network dynamics and its relationships to topology and coupling structure in excitable complex networks

All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.     

Complex network from pseudoperiodic time series: topology versus dynamics

We construct complex networks from pseudoperiodic time series, with each cycle represented by a single node in the network. We investigate the statistical properties of these networks for various time series and find that time series with different dynamics exhibit distinct topological structures. Specifically, noisy periodic signals correspond to random networks, and chaotic time series generate networks that exhibit small world and scale free features. We show that this distinction in topological structure results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Standard measures of structure in complex networks can therefore be applied to distinguish different dynamic regimes in time series. Application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.     

Geometric and dynamic perspectives on phase-coherent and noncoherent chaos

Statistically distinguishing between phase-coherent and noncoherent chaotic dynamics from time series is a contemporary problem in nonlinear sciences. In this work, we propose different measures based on recurrence properties of recorded trajectories, which characterize the underlying systems from both geometric and dynamic viewpoints. The potentials of the individual measures for discriminating phase-coherent and noncoherent chaotic oscillations are discussed. A detailed numerical analysis is performed for the chaotic Ro?ssler system, which displays both types of chaos as one control parameter is varied, and the Mackey-Glass system as an example of a time-delay system with noncoherent chaos. Our results demonstrate that especially geometric measures from recurrence network analysis are well suited for tracing transitions between spiral- and screw-type chaos, a common route from phase-coherent to noncoherent chaos also found in other nonlinear oscillators. A detailed explanation of the observed behavior in terms of attractor geometry is given.     

Dynamical motifs: building blocks of complex dynamics in sparsely connected random networks

Spatiotemporal network dynamics is an emergent property of many complex systems that remains poorly understood. We suggest a new approach to its study based on the analysis of dynamical motifs-small subnetworks with periodic and chaotic dynamics. We simulate randomly connected neural networks and, with increasing density of connections, observe the transition from quiescence to periodic and chaotic dynamics. This transition is explained by the appearance of dynamical motifs in the structure of these networks. We also observe domination of periodic dynamics in simulations of spatially distributed networks with local connectivity and explain it by the absence of chaotic and the presence of periodic motifs in their structure.     

A decentralised topology control to regulate global properties of complex networks

In the last decade much research effort has been devoted to the investigation of the interplay between properties (i.e. synchronization, clustering, resilience to node fault) and topology of complex networks. Many algorithms have been proposed to construct a network topology with a given properties or to optimize them. These algorithms are static, off-line implemented and may require global network knowledge. In this paper we propose a simple decentralized topology control algorithm that by local actions carried out at the node allows to regulate network global properties. Additionally the algorithm is dynamic coping with both node and link faults and can be on-line implemented.     

Quantifying information loss on chaotic attractors through recurrence networks

We propose an entropy measure for the analysis of chaotic attractors through recurrence networks which are un-weighted and un-directed complex networks constructed from time series of dynamical systems using specific criteria. We show that the proposed measure converges to a constant value with increase in the number of data points on the attractor (or the number of nodes on the network) and the embedding dimension used for the construction of the network, and clearly distinguishes between the recurrence network from chaotic time series and white noise. Since the measure is characteristic to the network topology, it can be used to quantify the information loss associated with the structural change of a chaotic attractor in terms of the difference in the link density of the corresponding recurrence networks. We also indicate some practical applications of the proposed measure in the recurrence analysis of chaotic attractors as well as the relevance of the proposed measure in the context of the general theory of complex networks.     

Route to hyperchaos and chimera states in a network of modified Hindmarsh-Rose neuron model with electromagnetic flux and external excitation

Analyzing the chaos and bursting phenomenon of neurons has been of interest in the past decade. In this paper, we discuss an extended Hindmarsh-Rose neuron model by taking into consideration the slowly interacting cell phenomenon due to the calcium ions. In the extended model, we consider the effect of an external forcing current, and the electromagnetic coupling between the magnetic flux and the membrane potential of the neuron. We analyze the modified neuron model in the presence of periodic and quasi-periodic excitations. A more complex chaotic behavior (hyperchaos) is identified in the neuron model. The results also demonstrate the multistable nature, which was not explored earlier. To discuss the dynamical behavior of the modified neuron in a network, we construct a ring network of neurons and capture the spatiotemporal patterns of the neuron in the network, in the presence of different excitations.

     

Traffic optimization in transport networks based on local routing

Congestion in transport networks is a topic of theoretical interest and practical importance. In this paper we study the flow of vehicles in urban street networks. In particular, we use a cellular automata model on a complex network to simulate the motion of vehicles along streets, coupled with a congestion-aware routing at street crossings. Such routing makes use of the knowledge of agents about traffic in nearby roads and allows the vehicles to dynamically update the routes towards their destinations. By implementing the model in real urban street patterns of various cities, we show that it is possible to achieve a global traffic optimization based on local agent decisions.     

短时交通流复杂动力学特性分析及预测

为揭示短时交通流的内在动态特性,利用非线性方法对交通流混沌特性进行识别,为短时交通流的预测提供基础.基于混沌理论对交通流时间序列进行相空间重构,利用C-C算法计算时间延迟和嵌入维数,采用Grassberger-Procaccia算法计算吸引子关联维数,通过改进小数据量法计算最大Lyapunov指数来判别交通流时间序列的混沌特性.针对局域自适应预测方法在交通流多步预测中预测器系数无法调节的问题,提出了交通流多步自适应预测方法.通过实测数据计算,结果表明:2,4和5 min三种统计尺度的交通流时间序列均具有混沌特性;改进的小数据量法能够准确地计算出最大Lyapunov指数;构建的交通流多步自适应预测模型能够有效地预测交通流量的变化.为智能交通系统诱导和控制提供了依据.     

A Novel Measure Inspired by Lyapunov Exponents for the Characterization of Dynamics in State-Transition Networks

The combination of network sciences, nonlinear dynamics and time series analysis provides novel insights and analogies between the different approaches to complex systems. By combining the considerations behind the Lyapunov exponent of dynamical systems and the average entropy of transition probabilities for Markov chains, we introduce a network measure for characterizing the dynamics on state-transition networks with special focus on differentiating between chaotic and cyclic modes. One important property of this Lyapunov measure consists of its non-monotonous dependence on the cylicity of the dynamics. Motivated by providing proper use cases for studying the new measure, we also lay out a method for mapping time series to state transition networks by phase space coarse graining. Using both discrete time and continuous time dynamical systems the Lyapunov measure extracted from the corresponding state-transition networks exhibits similar behavior to that of the Lyapunov exponent. In addition, it demonstrates a strong sensitivity to boundary crisis suggesting applicability in predicting the collapse of chaos.