两相流流型复杂网络社团结构及其统计特性

利用气液两相流电导波动信号构建了流型复杂网络. 基于K均值聚类的社团探寻算法对该网络的社团结构进行了分析,发现该网络存在分别对应于泡状流、段塞流及混状流的三个社团,并且两个社团间联系紧密的点分别对应于相应的过渡流型. 基于复杂网络理论从全新的角度探讨了两相流流型复杂网络社团结构及统计特性问题,并取得了满意的流型识别效果,与此同时,在对该网络特性进一步分析的基础上,发现了对两相流流动参数变化敏感的相关复杂网络统计量,为更好地理解两相流流型动力学特性提供了参考. 关键词： 两相流流型 复杂网络 社团探寻算法 网络统计特性     

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     

两相流相空间多元图重心轨迹动力学特征

本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法. 在确定了最佳嵌入维数和延迟时间后, 将相空间中高维矢量点映射到二维平面的雷达图上, 相应地将相空间中高维矢量点变换为对应的几何多边形. 通过提取几何多边形的重心位置得到重心轨迹动力学演化特性, 并利用重心轨迹矩特征量区分不同性质的混沌时间序列. 在此基础上, 处理分析了气液两相流电导传感器动态信号, 发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流, 而且为流型动力学演化机理提供了新的分析途径.     

两相流流型多尺度熵及动力学特性分析

研究了几种典型非线性时间序列的多尺度熵特征,在此基础上分析了由插入式阵列电导传感器采集的144种流动条件下的垂直上升气液两相流电导波动信号.研究结果表明：利用小尺度下样本熵的变化速率特征可以分辨三种典型流型（泡状流、段塞流、混状流）,而大尺度下样本熵的波动特征可以反映各种流型的动力学特性.泡状流随机可变特性表现为大尺度下样本熵的高值及振荡特征;段塞流气塞与液塞的间歇性运动表现为大尺度下样本熵的低值及平稳性;混状流极不稳定的振荡运动特性表现为介于泡状流及段塞流之间的熵值特点,并在更大尺度时熵值逐渐接近泡状流 关键词： 样本熵 多尺度熵 气液两相流 动力学特性     

基于混沌吸引子形态特性的两相流流型分类方法研究

利用高灵敏度差压传感器，在垂直上升管中采集到了80组气液两相流差压波动信号.利用放置参考截面的方法，建立了描述混沌吸引子形态的一般方法，在此基础上，提出了不同维数下的吸引子形态特征量进行组合的气液两相流流型分类新方法.研究结果表明：该方法对包括复杂过渡流型在内的气液两相流流型有很好分类效果，预示着混沌吸引子形态描述是研究非线性时间序列的实用有效途径. 关键词： 气液两相流 流型分类 吸引子形态 混合维     

基于非接触电导的气液两相流参数检测系统

研究了电容耦合式非接触电导检测技术(C~4D)应用于气液两相流测量的可行性,建立了一套基于非接触电导的毫米级气液两相流参数检测系统。利用所建立的系统,在内径为5 mm,9 mm的水平管上进行了气液两相流实验,获得了不同流型下的电导测量信号,并对段塞流、波状流、泡状流和环状流四种典型流型下的电导信号进行了分析与比较。研究结果表明,所建立的基于非接触电导的气液两相流参数检测系统是有效的,C~4D技术应用于气液两相流参数测量是可行的。不同流型下所获的电导信号特征均表现出明显差异,有望用于流型辨识。     

基于随机子空间结合稳定图的气液两相流型分析

研究了几种典型气液两相流流型的稳定图特征,并应用随机子空间方法对47种流动条件下的流型 信号进行了识别.研究结果表明:稳定图特征能够反映出复杂时间序列的内部特征,利用其提取的直线度特征 值可以对时间序列的特征进行量化分析,三种典型流型的稳定图特征差异较明显,泡状流的稳定图特征最为 混乱,雾状流次之,段塞流最为规整.应用随机子空间方法对气液两相流图像灰度波动序列进行特征向量提取 和辨识,通过幅值以及相角的分布特征能够对不同工况下的流型样本进行准确分类,为多相流的分类提供了 一条新路径.同时基于稳定图的分析方法为进一步揭示多相流的流动机理提供有益的探索.     

气液两相流相含率测量新方法研究

本文基于电容耦合式非接触电导检测(Capacitively Coupled Contactless Conductivity Detection,C~4D)技术,提出了一种气液两相流相含率测量新方法。该方法基于新型六电极阵列式C~4D传感器,首先获取气液两相流电导信号,然后利用所获电导信号,结合LS-SVM回归方法分别建立三种典型流型(泡状流、环状流和层状流)的相含率测量模型。实际测量时根据流型选择相应的相含率测量模型,计算获得相含率。在内径为47.5 mm管径下进行相含率测量静态实验,研究结果表明,所提出的气液两相流相含率测量新方法是可行、有效的。在三种典型流型下的相含率测量最大绝对误差均小于9%。     

油-气-水三相流超声传感器持气率测量*

为探索油-气-水三相流持气率测量难题,该文开展了脉冲透射式超声传感器持气率测量动态实验研究。首先,利用超声传感器与光纤传感器组合,测取了油-气-水三相流中段塞流、混状流、泡状流的响应信号;其次,提取了超声脉冲信号的最大值序列来反映不同流型时超声传感器响应特性,同时,借助双头光纤传感器与相关测速法,计算得到了流体中气泡弦长序列;最后,结合流型与泡径信息,利用超声传感器测量了不同流型下持气率,并分析了不同流型持气率预测的误差来源,为其他油-气-水三相流持气率测量传感器设计提供了借鉴。     

螺杆螺旋通道内气液两相流流动特性研究

本文以水和空气为工质对螺杆螺旋通道内气液两相流流动特性进行研究,采用可视化的方法对螺杆螺旋通道内流型结构进行观察,观察到通道内不同转角位置处的气液两相流型及其特征。根据实验观测结果,绘制了三个转角位置的流型图,并对各流型特征和流型之间的转换界限进行分析,给出了泡状流向间歇流转变、环状流向间歇流转变的准则关系式。     

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.     

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.     

Recurrence network analysis of experimental signals from bubbly oil-in-water flows

Based on the signals from oil–water two-phase flow experiment, we construct and analyze recurrence networks to characterize the dynamic behavior of different flow patterns. We first take a chaotic time series as an example to demonstrate that the local property of recurrence network allows characterizing chaotic dynamics. Then we construct recurrence networks for different oil-in-water flow patterns and investigate the local property of each constructed network, respectively. The results indicate that the local topological statistic of recurrence network is very sensitive to the transitions of flow patterns and allows uncovering the dynamic flow behavior associated with chaotic unstable periodic orbits.     

气液两相流电导波动信号复杂性测度分析及其流型表征

为了考察从时间序列提取的复杂性测度与气液两相流流型变化之间的关系，本文首先讨论了三种复杂性测度(Lempel-Ziv复杂性、功率谱熵和近似熵)对周期信号、随机信号、混合随机信号和混沌信号的识别能力，然后分析了时间序列长度对复杂性计算的影响.在此基础上，从实际测量的80种垂直上升管中气液两相流电导波动信号中提取了这三种复杂性测度，结果表明：三种复杂度对两相流流型变化是敏感的，通过对三种复杂度随两相流流动参数变化规律分析，可以得到气液两相流动力学结构反演特征，为揭示气液两相流流型转化机理提供了一种有效的辅助诊断工具. 关键词： 气液两相流 Lempel和Ziv复杂性 功率谱熵 近似熵     

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.     

气液两相流波动信号的时频谱分析研究

为了研究气液两相流不同流型的动态特性,通过小波变换、希尔伯特-黄变换及自适应最优核三种时频方法对气液两相流动态差压信号进行处理.通过对时频谱的分析,可以清晰看出当流型从泡状流向弹状流、塞状流的转化过程中,信号的主要能量由15—35 Hz之间的频带向0—8 Hz频带转移,在弹状流时出现了两个谱峰.实验结果表明:希尔伯特-黄变换及自适应最优核方法的时频分辨率比小波分析高.基于自适应最优核方法的脊信息的提取,克服了模糊平面加窗效应的影响,对气液两相流动态信号表现出更高的时频分辨率,并增强了时频平面信息的可读性. 关键词： 气液两相流 流型识别 希尔伯特-黄变换 自适应最优核     

Uncovering dynamic behaviors underlying experimental oil–water two-phase flow based on dynamic segmentation algorithm

Characterizing complex dynamic behaviors arising from various inclined oil–water two-phase flow patterns is a challenging problem in the fields of nonlinear dynamics and fluid mechanics. We systematically carried out inclined oil–water two-phase flow experiments for measuring the time series conductance fluctuating signals of different flow patterns. We using the dynamic segmentation algorithm incorporating with phase space reconstruction analyze the measured experimental signals to uncover the dynamic behaviors underlying different flow patterns. Specifically, given a time series from a two-phase flow, we move a sliding pointer over the time series and for each position of the pointer we calculate the dynamic difference measure of the phase space orbits generated from the segment to the left and to the right of the pointer. A number of experimental signals under different flow conditions are investigated in order to reveal the dynamical characteristics of inclined oil–water flows. The results indicate that the heterogeneity of dynamic difference measure series is sensitive to the transition among different flow patterns and the standard deviation of dynamic difference measure series can yield quantitative insights into the nonlinear dynamics of the two-phase flow. These properties render the dynamic segmentation algorithm-based approach particularly useful for uncovering the dynamic behaviors of inclined oil–water two-phase flows.