Control of mixing process in a novel micro-mixer
Bibliographic record
Abstract
There are many different types of micro-mixers which have been designed to enhance mixing efficiency of fluid flow in micro-channels. The output of micro-channels integrated with micro-mixers in some cases may need to be in a certain range. In this article we design a new type of micro-mixer by utilizing conductive surfaces and walls with variable zeta potential. Interaction of the induced charged electro-kinetic (ICEK) phenomenon (due to existence of conductive surfaces) and producing diverse electro-osmotic boundary velocity (because of presence of walls with changeable zeta potential) together lead to increase mixing efficiency of the system significantly. Numerical simulation are performed to analyze the system and the results show that by using this micro-mixer with different zeta potential applied to walls, we can have a wide range of mixing efficiency between 30% and 85%. Moreover, we applied a fuzzy logic controller (FLC) to the system to manage the percentage of mixing efficiency within this range. This controller based on desired mixing efficiency; determine the amount of zeta potential on each wall and thus we can reach the amount of required mixing efficiency.
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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 | 0.000 | 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 | 0.000 | 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".