Analysis of Power Consumption in Multishaft Mixers
Bibliographic record
Abstract
New definitions of the Reynolds and power numbers proposed by Farhat, Fradette, and Tanguy (2008) for coaxial mixers were discussed and extended to some other types of multishaft mixers not considered previously. The results confirmed that these new correlations are also applicable for the dual shaft mixer configurations and the Superblend mixer. This universal applicability allowed the authors to experimentally investigate and compare the power consumption and the mixing time in the coaxial, the dual shaft, and the Superblend mixers based on uniform criteria. It was found that the Mixel TT–Anchor combination is the most power-efficient combination, whereas the Superblend mixer requires the most power. Besides, a general approach was introduced to predict the power constant of these multishaft mixers. Finally, the limitations of the new correlations were pointed out through the extension study of their applicability in Superblend and Rotor Stator–Paravisc dual shaft mixer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".