Modeling Droplet Collisions in a Converging Microchannel
Bibliographic record
Abstract
Multiphase flows in microchannels represent interests for various technologies, such as microfluidics, and are related to natural processes, including flows in small blood vessels. Earlier studies of droplet dynamics in microchannels have assumed aligned initial droplet positioning, neglecting the random nature of droplet interactions. This work numerically investigates how droplet interaction behavior in a narrowing microchannel correlates with droplet positioning and the Reynolds number. Interface-resolved computational fluid dynamics (CFD) simulations enable a 28 nm near-interface mesh resolution. In each of the 550 cases, droplet coordinates are randomly generated to avoid selection bias. The study identifies four distinct regimes of droplet interaction. Droplet dynamics in each regime, including satellite droplet formation, are examined. With 340 CPU-years of computations, this work is the first statistical study of CLSVOF simulations for binary collisions. The results obtained improve our understanding of binary collision mechanisms in microchannels. These findings can be useful for the design of microfluidic devices and the development of reliable biomedical simulation tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".