Liquid Phase Stabilization in Developing Gravity-Driven Slug Flows: Insights From PIV-PLIF Analysis
Bibliographic record
Abstract
Abstract The liquid phase stabilization mechanisms within liquid slugs and the modulating effect of gas density have not been fully understood for gravity-driven vertical slug flows, despite its critical role in flow development and transport phenomena. This study employs particle image velocimetry-planar laser induced fluorescence (PIV-PLIF) technique to investigate the liquid phase stabilization mechanism in the developing region of gravity-driven slug flows. Experiments were conducted for both air-water and CO2-water systems to examine the gas density impact. The stabilization process was systematically characterized by tracking the evolution of velocity vector fields, streamline contours, axial and radial velocity distributions, vorticity fields, and turbulence intensity distributions from the wake of a leading Taylor bubble to the emergence of a trailing Taylor bubble. Furthermore, temporal variations in velocity components, turbulence intensity, and Reynolds shear stress were examined. The results revealed a highly turbulent wake region behind Taylor bubbles, characterized by large-scale vortical structures. As the liquid-phase flow develops within the liquid slug near the upcoming Taylor bubble, the axial velocity profiles become uniform, the radial velocity components attenuate, turbulence intensity and vorticity diminish, and streamline patterns converge toward linearity, signifying progressive stabilization. Temporal analyses also show a significant decline in velocity fluctuations, turbulence intensity, and shear stress along the axial distance. Notably, lower gas density induces elevated turbulence levels, facilitating a more rapid restoration of velocity profiles due to greater momentum diffusion, although the complete relaxation of turbulent structures requires a significantly extended axial distance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".