A discrete element study of wet particle–particle interaction during granulation in a spout fluidized bed
Bibliographic record
Abstract
Abstract In this article we study the effect of the inter‐particle interaction on the bed dynamics, by considering a variable restitution coefficient. The restitution coefficient is varied in time and space depending on the moisture content due to the particle–droplet interaction and evaporation. This study is done computationally, by using an extended discrete element model (DEM). The examined flow regimes comprise the intermediate/spout‐fluidization regime (B1), spouting‐with‐aeration regime (B2) and the jet‐in‐fluidized‐bed regime (B3). For all flow regimes, the averaged bed height increases with decreasing restitution coefficient. Moreover, the averaged bed height for a variable restitution coefficient is larger for all flow regimes compared to a case with a constant restitution coefficient, indicating that the distribution of the restitution coefficient influences the bed dynamics. The effect of evaporation on the distribution of the restitution coefficient is only observed for the jet‐in‐fluidized‐bed regime (B3), where the background velocity is relatively high leading to enhanced evaporation from the particles in the annulus region. This is reflected in the averaged bed height for the evaporation test case, which is larger compared to a test case without evaporation. A larger bed height for cases with variable restitution coefficient is due to the pressure build up in the spout region caused by the longer closing period of the spout channel. This is confirmed by the recorded pressure fluctuation signal and its root mean square which are larger for the cases with the variable restitution coefficient.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".