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Multiscale Characterization of Flow Instability for Gas-Liquid Two-Phase Flow.
Qing-Ming Sun1,2, Qing-Chao Yu1, Di Ba1,3
1School of Mechanical and Electrical, Qiqihar University, Qiqihar 161000, China.
Entropy (Basel, Switzerland)
|February 27, 2026
Summary
A new method, time-shift multiscale equiprobable symbolic sample entropy (TMESE), accurately characterizes gas-liquid two-phase flow instability. Bubble flow is most unstable, slug flow least, providing insights for engineering applications.
Area of Science:
- Fluid dynamics
- Nonlinear dynamics
- Complex systems analysis
Background:
- Gas-liquid two-phase flow instability poses risks to industrial systems.
- Accurate characterization of multiscale dynamics is crucial but challenging.
- Existing methods struggle with complex flow patterns.
Purpose of the Study:
- To introduce a novel method, time-shift multiscale equiprobable symbolic sample entropy (TMESE), for characterizing gas-liquid two-phase flow instability.
- To analyze the dynamic behaviors of bubble, slug, and churn flows using TMESE.
- To establish a quantitative measure for multiscale flow instability.
Main Methods:
- Development and application of the time-shift multiscale equiprobable symbolic sample entropy (TMESE) method.
- Validation using four evaluation metrics on eight typical time series.
- Qualitative and quantitative analysis of bubble, slug, and churn flow dynamics.
Main Results:
- TMESE effectively captures the evolutionary features of different gas-liquid flow patterns.
- The joint distribution of average TMESE and complexity index (CI) reliably quantifies multiscale flow instability.
- Bubble flow demonstrated the highest instability, followed by churn flow, with slug flow being the least unstable.
- Increased gas or liquid superficial velocity correlated with higher average TMESE and CI values.
Conclusions:
- TMESE offers a robust approach for characterizing gas-liquid two-phase flow instability.
- The TMESE-based complexity index provides a reliable quantitative measure for assessing flow instability.
- Findings support enhanced prediction and control strategies for industrial gas-liquid flow systems.
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