Effect of the geometry on the performance of the Maxblend<sup>™</sup> impeller with viscous Newtonian fluids
Bibliographic record
Abstract
Abstract Experimental and numerical investigations have been carried out to assess the effect of the vessel geometry (number of baffles) and the shape of the Maxblend impeller (configuration of the bottom paddle and angle of the upper grid) with viscous Newtonian fluids in the laminar and lower turbulent mixing regimes. Two parameters have been explored namely the power consumption of the impeller and the mixing time. Videos of the discoloration process have also been taken to get access to the mixing patterns. A nominal 50‐l vessel has been used in the experiments. The number of baffles has been varied from 1 to 4, and the bottom paddle has been modified by making openings to allow flow passage. Finally, the effect of using a straight grid in the upper part of the Maxblend has been studied under the same conditions. It is shown that the number of baffles does not have a significant effect on the power consumption, the mixing evolution, and the mixing time regardless of the flow regime. Making openings in the bottom paddle allows for the destruction of the segregated zones at the bottom of the tank at the expense of a very slight increase in power consumption and mixing time. Finally, the power and mixing time are both increased with a straight grid Maxblend. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".