Experimental and Numerical Evaluation of a Scaled-Up Micromixer With Groove Enhanced Division Elements
Bibliographic record
Abstract
A novel passive enlarged micromixer has been proposed and experimentally and numerically investigated in this study over 0.5 ≤ Re ≤ 100. Flow visualization was applied to qualitatively assess flow patterns and mixing, while induced fluorescence was applied to quantify the distribution of species at six locations along the channel length. Numerical simulations were applied to assist in the description of the highly rotational flow patterns. Two individual species are supplied through a total of three lamellae, which are converged prior to entering the main mixing channel, which consists of five groove-enhanced circular division elements. Grooves along the bottom surface of the channel allow for the development of helical flow in each subchannel of the mixing element, while the circular geometry of the mixing elements promotes the formation of Dean vortices at higher Reynolds numbers. The main mixing channel is 2000 μm wide and 750 μm deep, while the total channel length is 137.5 mm. Flow rotation was observed at all investigated Reynolds numbers, though the degree of rotation increased with increasing Re. A decreasing-increasing trend in the degree of mixing was observed, with a critical value at Re = 10. Of the investigated cases, the highest degree of mixing at the outlet was achieved at Re = 0.5, where mass diffusion dominates. A standard deviation of σexp = 0.062 was reported. At Re = 100, where advection dominates and secondary flow develops, a standard deviation of σexp = 0.103 was reported, and the formation of additional lamellae was observed along the channel length due to the merging of rotated substreams. The additional lamellae contributed to the increase in interfacial area and reduction of the path of diffusion.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".