The Physics and Manipulation of Dean Vortices in Single- and Two-Phase Flow in Curved Microchannels: A Review
Bibliographic record
Abstract
Microchannels with curved geometries have been employed for many applications in microfluidic devices in the past decades. The Dean vortices generated in such geometries have been manipulated using different methods to enhance the performance of devices in applications such as mixing, droplet sorting, and particle/cell separation. Understanding the effect of the manipulation method on the Dean vortices in different geometries can provide crucial information to be employed in designing high-efficiency microfluidic devices. In this review, the physics of Dean vortices and the affecting parameters are summarized. Various Dean number calculation methods are collected and represented to minimize the misinterpretation of published information due to the lack of a unified defining formula for the Dean dimensionless number. Consequently, all Dean number values reported in the references are recalculated to the most common method to facilitate comprehension of the phenomena. Based on the converted information gathered from previous numerical and experimental studies, it is concluded that the length of the channel and the channel pathline, e.g., spiral, serpentine, or helix, also affect the flow state. This review also provides a detailed summery on the effect of other geometric parameters, such as cross-section shape, aspect ratio, and radius of curvature, on the Dean vortices' number and arrangement. Finally, considering the importance of droplet microfluidics, the effect of curved geometry on the shape, trajectory, and internal flow organization of the droplets passing through a curved channel has been reviewed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".