Visualization of the Effect of a Shroud on Entrainment of Fluidized Solids into a Gas Jet
Bibliographic record
Abstract
Submerged gas jets issuing into fluidized beds are used in many different industries and it is important to have a fundamental understanding of how the gas and surrounding medium interact to understand the flow patterns and how the solids behave in the vicinity of the nozzle jet. Following the discussions of Bohnet and Teifke (1985) and Idelchek (1994) it was decided to qualitatively investigate the effects of altering the region surrounding the contact between the jet and the fluidized bed. Alteration of the flow pattern was accomplished using a semi-cylindrical shroud (0.035 m i.d. and 0.043 m long) that formed a physical barrier around the nozzle tip. The nozzle inner diameter was 0.0016 m i.d. and the motive gas flow rate was supersonic. Videos were recorded of the solids and gas flow patterns via a special transparent plate on the wall of the fluidized bed for a half-jet with and without a shroud at a superficial fluidization gas velocity of 0.11 m/s. Presented below are the original videos and observations derived from the two simple experiments in a fluidized bed with a rectangular cross-section (0.10 by 1.20 m and 2.0 m high). There was a significant difference in the flow patterns of the solids in the vicinity of the nozzle tip when the shroud was present. Furthermore, the jet expansion angle and penetration were observed to decrease by approximately 50% and 43%, respectively.
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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 | 0.000 |
| 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 | 0.001 |
| 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".