The Numerical Investigation of a New Passive Micromixer With Improved Tesla Structure
Bibliographic record
Abstract
In this paper, 3D numerical simulations are performed to investigate the mixing process within an improved Tesla micromixer. This improved Tesla micromixer applies the flow separation/recombination and converging/diverging principles to enhance mixing. A portion of the working fluid, which separates from the main flow, enters the Tesla side branch and mixes with the main flow again at the exit of the Tesla unit. The tested volume flow rate ranges from 1 μL/min to 100 μL/min. Grid independence is carried out to minimize the effect of numerical diffusion. Optimization is done to determine three parameters, which are the gap ratio (H/W), the mixing cell number (N), and the angle at the gap inlet (β). The effects of these three parameters on mixing are investigated at a volume flow rate of 100 μL/min. The simulation results show that the gap ratio is the most important factor. Three parameters are selected as H/W = 50/200, N = 10 and β = 90° for further investigation. The traditional Tesla micromixer is also simulated for comparison with the present design. The mixing efficiency is approximately 60% in the range of the tested volume flow rate. The improved micromixer has better mixing efficiency than the traditional Tesla micromixer when the volume flow rate is less than 50 μL/min.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".