Numerical study of the mixing characteristics in an air swirling fluidized bed with monodisperse and multi‐sized particles
Bibliographic record
Abstract
Abstract Powder mixers are used in many industries. In the present work, a new type of air swirling mixer was designed and optimized with eight horizontally arranged inlet pipes at the tangential inlet angle of 35°. The mixing of multi‐sized spherical particles (2.0, 3.0, 4.0, and 5.0 mm) was numerically investigated in the air swirling mixer by coupled computational fluid dynamics–discrete element method. The numerical results showed that multi‐sized particles achieved comparable mixing performance to monodisperse particles. The Lacey index for multi‐sized particles increased initially, and then reached a maximum value at 0.824. The upward velocity of the particles, , increased initially, and then decreased to zero along the bed height. The maximum value of occurred at a height of 40 mm. Particle radial velocity was larger near the wall than at the mixer tube centre area. The smallest particles aggregated in three layers. The collision number of the particles reached a maximum at bed height of 120 mm, which was consistent with the position of the maximum stress of the particles against the tube wall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".