Dynamics of partially collapsing pulsed fluidized bed
Bibliographic record
Abstract
Abstract The use of flow pulsation as an effective assisted fluidization technique has been suggested in a number of applications, such as drying of food and pharmaceutical products, dry beneficiation of coal, and deagglomeration of nanopowders, owing mainly to its cost‐effectiveness and ease of implementation. The efficacy of this technique is, however, greatly affected by the frequency of pulsation since it controls the collapse dynamics of the fluidized bed. In this study, using ultrafine hydrophilic nanosilica with strong agglomeration tendencies, the pulsation frequency was controlled to allow only partial collapse of the bed between two successive pulsations while the global and local dynamics in different bed regions were carefully monitored. Besides the usual advantages associated with assisted fluidization techniques, such as lower minimum fluidization velocity, higher bed expansion, and elimination of bed non‐homogeneities, the continuous and intense solid motion imparted by the partial bed collapse caused a more substantial increase in the overall pressure drop than the one obtained with the conventional unassisted fluidization. The results of the frequency domain analysis highlighted the fact that the effect of pulsation event was felt differently in different regions of the bed depending upon the amplitude of the pulsation. These results were further corroborated by the similarity analysis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".