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

Limited penetrable visibility graph from two-phase flow for investigating flow pattern dynamics

We optimize and design a new half-ring conductance sensor for measuring two-phase flow in a small diameter pipe. Based on the experimental signals measured from the designed sensor, we using the limited penetrable visibility graph we proposed construct complex networks for different flow patterns. Through analyzing the constructed networks, we find that the joint distribution of the allometric scaling exponent and the average degree of the network allows distinguishing different gas-liquid flow patterns in a small diameter pipe. The curve peak of the degree distribution allows uncovering the detailed features of the flow structure associated with the size of gas bubbles, the average degree of the network can reflect the macroscopic property of the flow behavior, The allometric scaling exponent is very sensitive to the complexity of fluid dynamics and allows characterizing the dynamic behaviors in the evolution of different flow patterns. In this regard, limited penetrable visibility graph analysis of fluid signals can provide a new perspective and a novel tool for uncovering the dynamical mechanisms governing the formation and evolution of different flow patterns.
Keywords： two-phase flow complex network limited penetrable visibility graph allometric scaling exponent