Pressure Effect on Hydrodynamics of a High Pressure X-ray Transparent Polyethylene Fluidized Bed
Bibliographic record
Abstract
Hydrodynamics of a polyethylene fluidized bed were studied at different operating pressures (191-2908 kPa) and constant temperature of 30°C. Minimum fluidization velocity (Umf) decreased with increasing of operating pressures. Measured Umf agree well with calculated Umf as derived from the standard deviation of pressure fluctuations using Puncochar’s Method (1985). As expected, with the increase of superficial velocity, amplitude and standard deviation of pressure fluctuation series also increase, which is indication of more vigorous bubbling behavior inside the bed. Power spectral density of pressure fluctuation series indicates similar frequency distribution of the bubbling behavior at different superficial velocities. The dominant frequency from the power spectral was found to be around 1 Hz. Bubble diameter and bubble velocity were estimated from X-ray fluoroscopy images. Bubble diameter and bubble velocity increase with increasing bed height and superficial gas velocity due to bubble coalescence and more gas flowing upwards. At the same fluidization number, the average bubble size slightly decreases with increasing pressure at all bed heights because there is less gas flowing and the bubbles coalesce less rapidly. The bubble velocity is observed to have a small decrease from 191 kPa up to 2200 kPa, and then a substantial increase due to the fact that at higher pressure, the solid circulation increases at the bed.
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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".