Two-Phase Flow Phenomena in Pipe Systems

Two-phase flow phenomena in pipe systems arise when gas and liquid phases coexist under confined conditions, giving rise to a rich variety of interfacial dynamics and flow structures. Depending on flow rates, pipe inclination and fluid properties, distinct regimes such as stratified, slug, annular and dispersed flows can develop, each characterised by differing phase distributions, interfacial wave behaviour and turbulence levels. These patterns exert a profound influence on pressure drop, heat and mass transfer, and operational safety in sectors ranging from oil and gas to chemical processing and energy generation. Traditional predictive tools have included drift-flux models and empirical correlations for liquid holdup and pressure gradient estimation, while advanced measurement techniques such as laser-based velocimetry and electrical tomography have revealed detailed velocity fields and recirculation zones. Recent efforts integrate high-speed imaging, ultrasonic sensing and machine-learning algorithms to deliver real-time monitoring and automated regime classification. Such advances enable optimised pipeline design, improved flow assurance and reduced environmental and financial risk on a global scale.

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Two-Phase Flow Phenomena in Pipe Systems publication trend

The graph below shows the total number of articles in two-phase flow phenomena in pipe systems across all publications each year (not limited to Nature Index journals).

Technical terms

Two-phase flow: The concurrent movement of liquid and gas phases within a conduit, characterised by interacting interfaces and varying phase distribution.

Flow regime: A characteristic pattern of phase distribution and flow structure, such as stratified, slug or annular.

Void fraction: The volume fraction of the gaseous phase in a two-phase mixture within a given cross-section.

Drift-flux model: An empirical or semi-empirical framework relating phase velocities and distributions to overall flow properties.

Particle image velocimetry (PIV): A non-intrusive optical method to measure velocity fields by tracking seeded particles illuminated by laser sheets.

Self-supervised learning: A machine-learning technique that derives supervisory signals from raw data to train models without manual annotation.

References

1. Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser. Expert Systems with Applications (2024).
2. An experimental characterization of liquid films in downwards co-current gas–liquid annular flow by particle image and tracking velocimetry. International Journal of Multiphase Flow (2014).
3. Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries. Polymers (2021).
4. An Efficient Drift-Flux Closure Relationship to Estimate Liquid Holdups of Gas-Liquid Two-Phase Flow in Pipes. Energies (2012).