Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow

Abstract

This paper reports the application of the Hilbert–Huang Transform (HHT) to the dynamic characterization of gas–liquid two-phase flow in a horizontal pipeline. A differential pressure fluctuation signal of gas–liquid two-phase flow is adaptively decomposed into Intrinsic Mode Functions (IMFs) through the use of Empirical Mode Decomposition (EMD) methods. Based on the EMD, the associated time–frequency–energy distribution, i.e., the Hilbert spectrum, is obtained for the analysis of the differential pressure fluctuation signal and subsequent identification of its corresponding energy characteristics. The relationship between the energy distribution of the signal and the flow pattern is established. In order to assess the effectiveness of the approach, the results obtained using the HHT are compared with those from Fourier analysis and wavelet based methods. It is found that the extracted energy characteristics give a good indication of the dynamic state of the gas–liquid two-phase flow and thus can be used for flow pattern recognition. The proposed method is a useful tool for the in-depth understanding and subsequent quantitative characterization of gas–liquid two-phase flow.