Numerical investigation of gas–liquid and liquid–liquid <scp>T</scp> aylor flow through a circular microchannel with a sudden expansion
Bibliographic record
Abstract
Abstract This paper investigates a CFD‐based analysis for gas–liquid and liquid–liquid Taylor flows through a circular axisymmetric microchannel with a sudden enlargement. A series of simulations are conducted by exploring the influence of different superficial velocity ratios, apparent viscosities, and channel expansion on the hydrodynamics of slug flow. A concentric junction introduces dispersed airflow into a continuous flow of water for gas–liquid flow, and the junction introduces dispersed water into a continuous flow of dodecane for liquid–liquid flow. The air‐bubble and water‐slug evolution processes, slug breakup, and slug expansion are investigated. In all cases, the lengths of air bubbles and water slugs increase with increasing superficial velocity ratio, particularly before the expansion. For gas–liquid flow, the apparent viscosity ratio causes a fluctuating interface over the uniform film region. However, the water slug length is shorter and the film region is slightly thicker in liquid–liquid compared to gas–liquid flow. The numerical analysis developed in this paper is in good agreement with the existing correlations and experimental data in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".