Heat transfer characteristics in a pulsating fluidized bed in relation to bubble characteristics
Bibliographic record
Abstract
Abstract A pulsating fluidized bed is operated with two sequential durations designated as an on‐period with injecting fluidization gas and an off‐period without it. The heat transfer coefficient between a vertically immersed heater and bed in a pulsating fluidized bed is measured under various pulse cycles and fluidized particles. The obtained results are compared with those in a normal fluidized bed with continuous fluidization air injection. The relationship between heat transfer coefficients and bubble characteristics, evaluated using a digital video camera, has also been investigated. For certain fluidized particles and operating pulse cycles, the fluidization of particles and the increment of heat transfer coefficients can be obtained under a mean air velocity based on a pulse cycle duration smaller than the minimum fluidization air velocity in a normal fluidized bed. Under the pulse cycles where a static bed through the whole bed is formed in the off‐period duration, the improved heat transfer rate over that in a normal fluidized bed can be measured. This may be attributed to large bubble formation. As heat transfer in the pulsating fluidized bed is obstructed with increasing time to keep a static bed due to the excessive off‐period duration, it is indicated that there is an optimum off‐period duration based on the heat transfer rate. © 2002 Wiley Periodicals, Inc. Heat Trans Asian Res, 31(4): 307–319, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.10038
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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 | 0.001 |
| 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".