Diffusion and convection mixing of non‐Newtonian liquids in an optimized micromixer
Bibliographic record
Abstract
Abstract An optimized planar micromixer has been employed to study its performance in mixing liquids with various rheological behaviours. This design takes advantage of a number of passive techniques, including split‐and‐recombination of the channel, contraction of the channel, and embedding diamond‐shaped obstacles in the main channels. Three‐dimensional Navier‐Stokes equations, along with an advection‐diffusion model are solved by means of a finite‐element scheme. Numerical simulations are performed for species with power law indexes ranging from 0.6–1.4, and the resulting mixing efficiencies are compared for different cases. Pressure drop is also evaluated and compared by taking into account the rheological behaviour of the fluids. The results revealed that in the studied range of Reynolds numbers, shear‐thickening fluids present higher efficiencies in the diffusion‐dominated regimes by 5 % at the best case. Shear‐thinning flows have a better performance at higher Reynolds numbers and expedite the diffusion‐advection transition point compared to other regimes. Transition occurs at a Reynolds number of 0.1 for the shear‐thinning regime, while for Newtonian fluids this point is at a Reynolds number equal to 1. Moreover, it is found that the variation of mixing efficiency for shear‐thickening flows with different fluid behaviour indexes is not significant in the studied Reynolds number range.
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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 | 0.000 | 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 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".