The Numerical Investigation of an Interdigital Micromixer With the Circular-Sector Obstacles
Bibliographic record
Abstract
In this paper, a passive interdigital micromixer with the circular-sector obstacles is proposed and the mixing performance is estimated by numerical simulation. The tested Reynolds numbers range from 0.01 to 10. Flow recirculation or vortices seems impossible to generate to enhance the mixing at such low Reynolds numbers. Hence, molecular diffusion is the dominant mixing mechanism. Based on the diffusion principle, enlarging the mixing length, reducing the diffusion length and increasing the surface area between species are major methods to obtain mixing enhancement. In order to achieve rapid mixing, shortening the mixing length is necessary. However, the reduced mixing length induces the decreased mixing time which the species take to mix. The circular-section obstacles are placed in the straight microchannels to enlarge the contact surface area between species. The flow path is distorted after passing the obstacles so that the real mixing length increases compared with traditional T-shape micromixers. Furthermore, flow advection takes a part role in mixing since the velocity direction is no longer perpendicular to diffusion direction. Different geometries and layouts of obstacles are analyzed for optimization. The results of optimal design show the worst mixing efficiency, around 50%, occurs at Re = 1. In order to improve the lower limitation of mixing efficiency, the duplicated layouts of obstacles in lateral direction with interdigital inlet are applied to reduce the diffusion path and increase the interface area so that the mixing efficiency could be enhanced. The results show that the mixing efficiency could achieve 85% at Re ≤ 1 with a low pressure drop of 100 Pa. It has the potential to be used in applications with low Reynolds numbers.
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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 | 0.000 | 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".