Effect of liquid addition on heat transfer in gas-fluidized beds of large light particles
Bibliographic record
Abstract
When liquid is added to large-particle gas-fluidized beds where the liquid density is similar to the particle density, the liquid addition can cause a dramatic increase in the velocity range and intensity of fluidization. In this paper it is shown that this also leads to strong enhancement of the heat transfer between the bed and an immersed tube. Experiments were carried out using a heated tube of outer diameter 25 mm in air-fluidized beds of polystyrene particles or glass beads to which small quantities of water (typically equivalent to 10 per cent of the bed volume) were added. The results from the heat transfer experiments are explained with the aid of observations of the tube surface using an industrial endoscope located inside the tube. The key factors underlying the enhancement of heat transfer are identified to be heat conduction to liquid-solid aggregates, convection to the liquid phase, convection to the gas phase and vaporization.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".