两相流流动结构多尺度复杂熵因果关系平面特征

采用一种新的信息熵理论分析了气液两相流流动结构动力学特征。首先考察了典型混沌时间序列的多尺度复杂熵因果关系平面特征,然后将该方法应用于气液两相流3种典型流型(泡状流、段塞流及混状流)的电导传感器波动信号处理,结果表明单尺度复杂熵因果关系平面能够对不同流型线性识别。此外发现多尺度复杂熵因果关系平面能够描述流动结构信息随尺度增加的连续丢失过程,反映了气液两相流流动结构稳定性及复杂性,是理解两相流流动结构非线性动力学特性的一种有用分析工具。

Two-phase flow structure in multi-scale complexity entropy causality plane

The flow structure dynamics of gas-liquid two-phase was analyzed with a novel approach, multi-scale complexity entropy causality plane (MS-CECP), derived from the information entropy theory. Firstly, the method was used to investigate typical chaotic time series and then to process the conductance fluctuating signals of three typical gas-liquid flow patterns, i.e., bubble flow, slug flow, and churn flow. The single scale CECP could discriminate the different flow patterns linearly. Furthermore, MS-CECP could describe the continuous information loss of flow structures with the increase of scale, which reflected flow structure stability and complexity of the gas-liquid two-phase flow system. The proposed approach (MS-CECP) was proven to be a useful analysis tool for understanding the nonlinear dynamics of flow structure in two-phase flows.