Modelling transverse mixing in a rolling drum
Bibliographic record
Abstract
Abstract In this paper, a new model of transverse mixing in a rotating drum is derived from dynamic data collected from an experimental rig. Since the active layer has often been declared as the zone that is responsible for the mixing of solids in a rolling bed, the active layer was characterized so that its properties could be predicted for a wide range of experimental conditions. The mixing model consisted of two correlations, one to predict the mixing rate in the drum and the other to predict the final contact between the two materials. These correlations were linked back to the operational variables of the drum, such as the drumfs loading, size and rotational velocity. By combining these two correlations the time required to fully mix the material in the drum could be predicted. The mixing model was tested against independent data and good agreement was observed between the experimentally derived results and those predicted by the mixing model. Furthermore, this mixing model was designed such that it would be easily applicable to different sized drums. The extrapolation ability was tested on different smaller‐sized drums and found to agree considerably with experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".