The rise of single bubbles in rolling fluidized beds
Bibliographic record
Abstract
• Bubble shape varies by nozzle/bed setup; rolling enhances circular profiles. • Rolling causes oscillatory bubble growth, with walls/channels affecting drift. • Rolling evens bubble residence time, reducing wall effects and boosting velocity. • Granular temperature depends on bed setup; rolling drops fluctuations effectively. Marinized bubbling fluidized beds hold promise for reducing ship emissions, but face significant challenges due to sea-induced hydrodynamic instability. This study investigates the dynamics of single bubbles in fluidized beds under simulated marine conditions, using digital image analysis and particle image velocimetry to evaluate the effects of rolling amplitude and frequency, nozzle position, and static inclination on bubble behavior and bed stability. Comparative analyses of vertical, inclined, and rolling beds show that nozzle placement and wall interactions strongly influence bubble trajectories, shapes, and size evolution. Rolling bed oscillations tend to stabilize bubble shapes but introduce bifurcated growth patterns and enhance gas drift near walls, while inclined beds amplify bubble shrinkage through channeling effects. Granular temperature analysis shows that rolling motion mitigates oscillations, redistributes particle kinetic energy, and stabilizes void fraction and bubble behavior despite exacerbated wall channeling at higher rolling amplitudes. These findings provide valuable insights into overcoming hydrodynamic challenges, optimizing fluidized bed designs, and improving their stability and performance for sea-going applications.
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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 | 0.001 | 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".