Hydrodynamics in a Gas‐Solids Fluidized Bed Using X‐Ray Fluoroscopy and Pressure Fluctuation Measurements
Bibliographic record
Abstract
Abstract In this work, non‐intrusive techniques were used to characterize the hydrodynamics in a gas‐solids bubbling fluidized bed using polyethylene powder and glass beads of comparable mean diameter (dp = 360 µm) but different density. X‐ray fluoroscopy measurements and pressure fluctuations were performed on a pseudo 2‐dimensional gas‐solids fluidized bed. Bubble properties were captured from X‐ray fluoroscopy measurements. Similarities and differences of flow behavior of the two particle systems were revealed from comparison of bubble properties. Bubble properities normally varied similary with operating conditions for the two particle systems, while bubble sizes for the glass beads system are larger than those for the polyethylene system. Wavelet analysis of pressure fluctuations was applied to investigate the gas and solids phase flow behavior. Multi‐scale flow behavior was extracted from the standard deviation of the decomposed coefficient series. Flow behavior due to particles and bubbles of different sizes were captured at different decomposition levels of pressure fluctuations, which is difficult to know from analysis of the original signal. Results extracted from X‐ray fluoroscopy and pressure fluctuation measurements were consistent, suggesting that conventional pressure fluctuation measurements can be effectively used for investigation of the bubbling behavior.
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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 | 0.001 |
| 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 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".