Refined composite multivariate multiscale complexity-entropy causality plane analysis for gas-liquid two-phase flow

Xingran Li, Chunling Fan, Jiangfan Qin, Rui Yang
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Abstract

Abstract This paper presents a refined composite multivariate multiscale complexity-entropy causality plane (RCMMCECP) to explore the dynamics features of gas–liquid two-phase flow. Firstly, we employ a series of typical nonlinear time series to confirm the effectiveness of the RCMMCECP, including seven chaotic systems, two random processes, and one periodic process. The comparison results of the proposed method and conventional multivariate multiscale complexity-entropy causality plane (MMCECP) confirm the stability performance of the proposed RCMMCECP. Above all, the RCMMCECP enhances the reliability of the statistical complexity measure over large time scales and exhibits good continuity and noise-resistant ability in multiscale analysis. Then, we employ the RCMMCECP to analyze the upstream and downstream conductance signals. The experimental results demonstrate that the RCMMCECP can characterize the change of complexity and structural stability in the gas-liquid two-phase flow evolution process, effectively revealing its dynamics features.
气液两相流精细复合多元多尺度复杂熵因果平面分析
摘要提出了一种改进的多元复合多尺度复杂熵因果关系平面(RCMMCECP),用于研究气液两相流的动力学特征。首先,我们采用一系列典型的非线性时间序列来验证RCMMCECP的有效性,包括7个混沌系统、2个随机过程和1个周期过程。将该方法与传统的多元多尺度复杂熵因果关系平面(MMCECP)进行了比较,验证了该方法的稳定性。总之,RCMMCECP提高了统计复杂性测度在大时间尺度上的可靠性,并在多尺度分析中表现出良好的连续性和抗噪声能力。然后,我们使用RCMMCECP对上下游电导信号进行分析。实验结果表明,RCMMCECP可以表征气液两相流演化过程中复杂性和结构稳定性的变化,有效地揭示了其动力学特征。
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