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.

Dynamic characteristics of multivariate graph centrobaric trajectory in phase space of two-phase flow

We propose a multivariate graph centrobaric trajectory-based method for characterizing nonlinear dynamics from high-dimensional chaotic time series. After the optimal selecting of the embedding dimension and time delay, we map the high-dimensional vector point into the two-dimensional radial plane graph, i.e., the high-dimensional vector point is transformed correspondingly to a geometric polygon. By extracting the geometric location of the polygon barycenters, we can obtain the evolving feature of the barycenter dynamical trajectory. Then we use the moment quantity of the barycenter trajectory to distinguish different chaotic time series. Finally, we apply our method to the fluctuating signals measured from gas-liquid two-phase flow experiments. The results suggest that our method can be a powerful tool for not only distinguishing the different flow patterns but also investigating the dynamical evolving mechanism of flow patterns.