Optimizing passive micromixers: Enhancing mixing efficiency through computational fluid dynamics and metaheuristic algorithms
Bibliographic record
Abstract
Abstract This paper examines the optimization of micromixers, highlighting their essential role in improving the efficiency of chemical reactions and fluid mixing at a micro‐scale, and using metaheuristic algorithms for a novel passive micromixer featuring bends and twists with offsets. The twists and bends cause constant changes in the direction of liquid flow, leading to chaotic advection that enhances species mixing while minimizing pressure loss. Although increasing channel length generally improves mixing performance, the proposed design's performance was higher than the reference channel for Reynolds numbers (Re) greater than 100. Key findings include achieving an excellent mixing performance of 87.76% at a Reynolds number of 400, with a bend angle of 60° and a twist factor of 4. The Harris hawk optimization (HHO) algorithm proved the most effective for optimizing microfluidic channel designs. This approach offers significant advantages for cost‐effectively optimizing microfluidic channel designs.
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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 | 0.000 |
| 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 | 0.001 |
| 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".