Acoustic emission-based flow noise detection and mechanism analysis for gas-liquid two-phase flow

•Optimization of sensor design for quantitative measurement of flow noise.•The flow noise signal is separated and characterized using Hilbert Huang transform.•The flow noise was distinguished using Hurst index and a mathematical model of the noise was developed.•The ARIMA model was used to predict t...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2021-07, Vol.179, p.109480, Article 109480
Main Authors: Zhao, Ning, Li, Chaofan, Jia, Huijun, Wang, Fan, Zhao, Zhiyue, Fang, Lide, Li, Xiaoting
Format: Article
Language:English
Subjects:
Acoustic emission
Acoustic emission testing
Acoustic noise
ARIMA model
Autoregressive models
Confidence intervals
Emission analysis
Flow distribution
Flow velocity
Flowing noise
Gas flow
Heat transfer
Hilbert Huang Transform
Noise measurement
Phase transitions
R/S analysis
Regression analysis
Separations and predictions
Signal to noise ratio
Statistical analysis
Two phase flow
Void fraction
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Summary:•Optimization of sensor design for quantitative measurement of flow noise.•The flow noise signal is separated and characterized using Hilbert Huang transform.•The flow noise was distinguished using Hurst index and a mathematical model of the noise was developed.•The ARIMA model was used to predict the energy change of the slug and bubble flow. The interphase forces of gas-liquid two-phase flow produce flow noise, which contains abundant two-phase flow information, such as two-phase flow rate, flow pattern, void fraction, etc. In this study, the acoustic emission technique is utilized to measure the flow noise of gas-liquid two-phase flow quantitatively. The signal is separated and identified by the Hilbert Huang transform, the R/S analysis, and the flow noise mathematical model. The Autoregressive Integrated Moving Average (ARIMA) time series model is used to predict the energy change of bubble flow. The results show that the Hilbert Huang transform can separate the noise signal. According to the R/S analysis, IMF1-IMF3 is the interaction noise of the gas and liquid. The results show that the prediction results all fall within the 95% confidence interval. It provides new approaches for the quantitative analysis of gas-liquid two-phase flow and flow pattern transitions.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2021.109480