Which impeller should be chosen for efficient solid-liquid mixing in the laminar regime?
Bibliographic record
Abstract
The vast majority of solid-liquid mixing studies have focused on high Reynolds number applications with configurations and impeller geometries adapted to this type of regime. However, the mixing of particles in a viscous fluid is an essential element of many contemporary industries. We used the CFD-DEM model previously developed in our group to investigate solid-liquid mixing with close-clearance impellers in the laminar regime of operation. We compared different geometries that is, the double helical ribbon, anchor, Paravisc$^{TM}$, and Maxblend$^{TM}$ impellers. We investigated the impact of fluid viscosity and compared the results with those obtained with the pitched blade turbine, a more commonly used impeller, based on power consumption for equivalent mixing states. This study highlights that the higher the viscosity of the fluid, the more interesting it is to use close-clearance impellers for their ability to generate a strong shear stress and a strong bulk flow in the entire vessel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".