<scp>Discrete element method</scp> study of the mixing and heat transfer behaviour of a binary‐size granular system in a rotating drum
Bibliographic record
Abstract
Abstract Understanding the internal solid motion and heat transfer behaviour within rotating drums is paramount for their design and operation across various industries. The discrete element method (DEM) is utilized to elucidate the general flow, mixing, and heat transfer characteristics of particles within rotating drums. Following model validation, this study delves into the mixing behaviour and heat transfer patterns of binary‐size particles in the rotating drum, while also assessing the impact of size ratio and rotating speed. The findings reveal that variations in particle size result in noticeable radial segregation, consequently affecting the heat transfer dynamics of solid phase within the system. Higher rotating speeds enhance mixing and dispersion of solid phase but lead to a decrease in the averaged particle temperature. Furthermore, the heat flux exhibits a negative correlation with particle size. Distinct heat transfer behaviours are observed among particles of different sizes in both active and passive areas, with larger particle size ratios exacerbating segregation, potentially impacting final product quality. In summary, these findings offer crucial insights into heat transfer phenomena in rotating drums, aiding in the design and operation of apparatus.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".