Precision mist injection strategy for enhanced hydrodynamic stability in oscillating bubbling fluidized beds
Bibliographic record
Abstract
This study investigates precision mist injection into a cuboidal pseudo-2D fluidized bed on a robotic sea wave simulator to stabilize bubbling and homogenize hydrodynamics under 9° inclination and 0.1 Hz rolling motion. Digital image analysis and particle image velocimetry are used to evaluate the effects of mist injection on defluidization, void fraction, particle motion, and fluidization regime changes. Liquid injection effectively reduces bubble and slug sizes and controls particle velocities without causing defluidization/agglomeration. Symmetric injection is ineffective in inclined beds and does not significantly reduce slug size in rolling beds, but does reduce bubble size. Asymmetric injection consistently performs better, especially in rolling conditions, by reducing bubble size and velocity and reducing slugging. Double point injection proves to be the most reliable and significantly reduces bed maldistribution in rolling configurations. These results suggest potential offshore applications, where mist-induced surface changes reduce sensitivity to sea-like motion. • Precise liquid injection reduces bubble and slug size, and particle velocity. • Non-targeted mist injection is to be avoided for inclined beds. • Targeted injection outperforms non-targeted option for curbing slug control. • Successful reduction of slugs in rolling fluidized bed is achieved.
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 | 0.000 | 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) | 0.000 | 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".