Design and Numerical Analysis a SAR ‘(Y-T)α’ Micromixer
Bibliographic record
Abstract
A novel split-and-recombined (SAR) '(𝑌 -𝑇) 𝛼 ' micromixer is designed and analyzed numerically.The proposed micromixer is composed of four identical elements that are connected by angles 𝛼 and 𝛽.The value of alpha (𝛼) is varied from 0° to 90° and the value of beta (𝛽) is always kept constant (𝛽 = 0 ° ) to analyze the effect on the SAR process and mixing performance for Reynolds numbers from 1 to 100.The numerical data shows that the SAR process strongly depends on the connecting angle 𝛼; at the mid-range of Reynolds numbers (40 ≤ 𝑅𝑒 < 80) mixer (𝑌 -𝑇) 45 ° shows the highest efficiency (about 90%) whereas (𝑌 -𝑇) 75 ° and (𝑌 -𝑇) 90 ° mixers yield more than 93% efficiency at higher Reynolds numbers (80 ≤ 𝑅𝑒 ≤ 100).Mixer (𝑌 -𝑇) 0 ° (𝛼 = 0 ° ) displays the lowest efficiency among all five examined mixers which is less than 50% at 𝑅𝑒 > 10.The mixing efficiency also varies with the number of elements and Reynolds numbers.The proposed mixer has a significantly lower mixing energy cost (MEC) when compared with a wellknown Tear-drop mixer.In addition, the split-and-recombined process, the influence of secondary flow, and pressure drop characteristics at various Reynolds numbers are presented and discussed.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".