A computational study on transition mechanism of dripping to jetting flow in a flow‐focusing geometry
Bibliographic record
Abstract
Abstract 2D simulations have been performed to investigate flow regimes in a flow‐focusing geometry by changing the dispersed phase and continuous phase velocities. The dispersed phase is polydimethylsiloxane (PDMS), and the continuous phase is water. Simulations have been performed in a range of oil–water viscosity ratio from 3 to 50, and interfacial tension ranges from 0.0118 to 0.002 N/m. The walls of the microchannel are considered to be poly(methyl methacrylate) (PMMA) surfaces. The contact angle (θ) of an oil droplet in the presence of water wetting the PMMA surface is 140°. Our study observed two types of flow regimes, namely dripping and jetting, by changing the dispersed phase and continuous phase velocities. The sequential time steps of void fraction contour have been presented to explore the droplet formation mechanism. The droplet pinch‐off time and jet growth time have been calculated for the dripping and jetting regime, respectively. The outcomes are summarized in the form of a flow pattern map at a viscosity ratio of 12 and interfacial tension of 0.0118 N/m, which shows the transition boundary between dripping and jetting phenomena. The simulated transition boundary agrees well with the analytical solution available in the literature. The effect of oil–water viscosity ratio and interfacial tension on droplet size is also investigated. These findings will help understand different flow regimes and their transition in a flow focusing geometry and will directly apply to microfluidic platform‐based devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".