Laminar mixing of non-Newtonian fluids in static mixers: process intensification perspective
Bibliographic record
Abstract
Abstract Static mixers are widely used in various industrial applications to intensify the laminar mixing of non-Newtonian fluids. Non-Newtonian fluids can be categorized into (1) time-independent, (2) time-dependent, and (3) viscoelastic fluids. Computational fluid dynamics studies on the laminar mixing of viscoelastic fluids are very limited due to the complexity in incorporating the multiple relaxation times and the associated stress tensor into the constitutive equations. This review paper provides recommendations for future research studies while summarizing the key research contributions in the field of non-Newtonian fluid mixing using static mixers. This review discusses the different experimental techniques employed such as electrical resistance tomography, magnetic resonance imaging, planar laser-induced fluorescence, and positron emission particle tracking. A comprehensive overview of the mixing fundamentals, fluid chaos, numerical characterization of fluid stretching, development of pressure drop correlations, and derivations of generalized Reynolds number is also provided in this review paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".