Optimization of Wavy-Channel Micromixer Geometry Using Taguchi Method
Bibliographic record
Abstract
The micro-mixer has been widely used in mixing processes for chemical and pharmaceutical industries. We introduced an improved and easy to manufacture micro-mixer design utilizing the wavy structure micro-channel T-junction which can be easily manufactured using a simple stamping method. Here, we aim to optimize the geometrical parameters, i.e., wavy frequency, wavy amplitude, and width and height of the micro channel by utilizing the robust Taguchi statistical method with regards to the mixing performance (mixing index), pumping power and figure of merit (FoM). The interaction of each design parameter is evaluated. The results indicate that high mixing performance is not always associated with high FoM due to higher pumping power. Higher wavy frequency and amplitude is required for good mixing performance; however, this is not the case for pumping power due to an increase in Darcy friction loss. Finally, the advantages and limitations of the designs and objective functions are discussed in the light of present numerical results.
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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) | 0.001 | 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 | 0.000 | 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".