In gas and liquid two-phase flow, different flow patterns show their unique nonlinear dynamic characteristics due to the complex interaction between them. To further reveal the underlying dynamic characteristics of this physical phenomenon, this paper presents a new model for the analysis of small channel gas-liquid two-phase flow pattern signals based on the multi-scale normalized Benford probability distribution (MSNBPD) analysis. The first focus area is the difference between the characteristics of a multi-scale approach to Benford’s probability distribution and Benford’s Law (BL) distribution in a typical nonlinear system. Once the terms are defined, the authors show how BL could be applied in a multi-scale based flow-pattern based method to analyze differential pressure signals of gas-liquid two-phase flow in small channels to reveal the underlying dynamic characteristics of different gas-liquid two-phase flow patterns, and their sensitivity to scale changes. In addition, the stability of different flow patterns was revealed by the variable scale range calculation. Flow patterns could then be distinguished based on Euclidean distance and the variable scale range difference of differential pressure signals. This paper also compares the present method with other methods that have been used in flow identification. The results show that this new method can well distinguish nonlinear systems with different dynamic mechanisms. This method can also reveal the underlying dynamic characteristics of a gas-liquid two-phase flow of micro and small channels. The two characteristic values (Euclidean distance and variable scale range difference) proved to be a good distinction for two-phase flow patterns than other time and frequency methods, with several possible universal applications. However, the identification results of micro and small channels were better than that of regular pipe, which shows that this method is more suitable for the identification of micro and small channel flow.

» Author: Hong-Wei Li, Hao-Fei Pei, Di Yang, Bin Sun, Yun-Long Zhou

» Reference: Chemical Engineering Science, Volume 179

» Publication Date: 06/04/2018

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