Experimental Analysis of Microchannel Entrance Length Characteristics Using Microparticle Image Velocimetry
Bibliographic record
Abstract
The study of the entrance region of microchannels and microdevices is limited, yet important, since the effect on the flow field and heat transfer mechanisms is significant. An experimental study has been carried out to explore the laminar hydrodynamic development length in the entrance region of adiabatic square microchannels. Flow field measurements are acquired through the use of microparticle image velocimetry (micro-PIV), a nonintrusive particle tracking and flow observation technique. With the application of micro-PIV, entrance length flow field data are obtained for three different microchannel hydraulic diameters of 500 μm, 200 μm, and 100 μm, all of which have cross-sectional aspect ratios of 1. The working fluid is distilled water, and velocity profile data are acquired over a laminar Reynolds number range from 0.5 to 200. The test-sections were designed as to provide a sharp-edged microchannel inlet from a very large reservoir at least 100 times wider and higher than the microchannel hydraulic diameter. Also, all microchannels have a length-to-diameter ratio of at least 100 to assure fully developed flow at the channel exit. The micro-PIV procedure is validated in the fully developed region with comparison to Navier–Stokes momentum equations. Good agreement was found with comparison to conventional entrance length correlations for ducts or parallel plates, depending on the Reynolds range, and minimal influence of dimensional scaling between the investigated microchannels was observed. New entrance length correlations are proposed, which account for both creeping and high laminar Reynolds number flows. These correlations are unique in predicting the entrance length in microchannels and will aid in the design of future microfluidic devices.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".