Solids Mixing in Marinized Bubbling Fluidized Beds: Gas Distribution Study
Bibliographic record
Abstract
Marinized bubbling fluidized beds hold promise for reducing CO 2, NO X, and SO X in ship exhaust gases, but their use at sea is hampered by a limited understanding of the influence of sea waves on hydrodynamics, heat transfer, and efficiency. To address this gap, this study used direct visualization techniques to investigate the solids mixing and hydrodynamics of bubbling fluidized beds under different gas distribution patterns in pseudo-2D vertical, inclined, and rolling beds. Digital Image Analysis (DIA) was used to determine the Lacey mixing index and the local void fraction, while Particle Image Velocimetry (PIV) was used to capture the particle velocity fields. Additionally, the effects of tilt angles and oscillation parameters of the nonvertical beds were compared with the conventional straight bubbling fluidized bed unit. The uniform inlet distribution of the fluidizing agent was compared with a variety of convex feed distributions at the bed entrance to correct for the negative effects of static/dynamic deviations from bed verticality necessary for optimal operation of bubbling fluidized bed reactors in marine environments.
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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) | 0.001 | 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".