Gas-liquid slug formation at a rectangular microchannel T-junction: A CFD benchmark case
Bibliographic record
Abstract
Abstract Computational fluid dynamics (CFD) is an important tool for development of microfluidic systems based on gasliquid two-phase flow. The formation of Taylor slugs at microchannel T-junctions has been studied both experimentally and numerically, however discrepancies still exist because of difficulties in correctly representing experimental conditions and uncertainties in the physics controlling slug flow, such as contact line and velocity slip. In this paper detailed methods and results are described for the study of Santos and Kawaji [1] on the comparison of experimental results and numerical modeling. The system studied consisted of a rectangular microchannel Tjunction nominally 100 μm in hydraulic diameter, used to generate Taylor slugs from air-water perpendicular flow. The effect of flow rates on parameters such as slug length, velocity slip, void fraction and two-phase frictional pressure drop were studied. Numerical simulation was performed using FLUENT volume-of-fluid (VOF) model. It is proposed in this paper that this microfluidic problem be taken up by researchers in the field as a benchmark case to test other numeric codes in comparison to FLUENT on the prediction of micro-scale multiphase flow, and also to model in more detail the experimental system described to obtain greater accuracy in prediction of microfluidic slug formation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".