Bubble behaviour investigation in a wet fluidized bed using digital image analysis
Bibliographic record
Abstract
Abstract Due to the presence of liquid bridge forces, wet particles reveal totally different fluidization behaviours than dry fluidized beds. This paper studies the bubble dynamics of wet Geldart‐D particles in a wet 2D fluidized bed. A digital image analysis method is adopted to identify bubbles and extract bubble properties based on MATLAB software. During fluidization, the bubble feature analysis of optimized binary images captures the bubble size variation, bubble fraction, and bubble shape. The results show that the increasing liquid saturation promotes the gas holding capacity of the emulsion phase, leading to the decreasing bubble fraction. When the particle size grows, the stability of the emulsion phase is promoted and hence the average bubble diameter increases. As the liquid saturation increases, the enhancing liquid bridge forces limit the bubble growth and promote the bubble breakage, which contributes to the small bubbles. With respect to the wet particles, of which the diameter is 1 mm and the liquid saturation is over 15%, slugging is likely to be observed, resulting in the increasing equivalent bubble diameter. The growing liquid saturation increases the wide aspect ratio, decreases the shape factor, and disperses the dependence between the two parameters. This indicates that small and irregular bubbles are likely to be seen in wet fluidized beds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".