Experimental and analytical investigation of meso-scale slug bubble dynamics in a square capillary channel
Bibliographic record
Abstract
The flow of dispersed gas bubbles in a viscous liquid can create a bubbly, slug bubble, or elongated bubble flow regime. A slug bubble flow, characterized by bubble sizes equal to the hydraulic diameter of the channel, is a transition regime with a complex local flow field that has received little attention in the past. In this study, dynamics of this flow regime in a square capillary with a cross-sectional area of 3 × 3 mm2 was studied analytically and experimentally. The main geometric parameters of the flow field, such as film and corner thicknesses and volume fraction, were calculated for different flow conditions based on a semi-empirical approach. Using velocity fields from particle image velocimetry (PIV), combined with the analytical equations derived, local mean variations of the film and corner flow thicknesses and velocity were analyzed in detail. Analysis of the results reveals a linear relation between the bubble speed and the liquid slug velocity that was obtained using sum-of-correlation PIV. Local backflow, where the liquid locally flows in the reverse direction, was demonstrated to occur in the slug bubble flow, and the theoretical analysis showed that it can be characterized based on the bubble cross-sectional area and ratio of the liquid slug and bubble speed. The backflow phenomenon is only contributed to the channel corners, where the speed of liquid can increase to the bubble speed. However, there is no evidence of reverse flow in the liquid film for the flow conditions analyzed in this study.
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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 | 0.000 | 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) | 0.001 | 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".