Hydrodynamic characteristics of gas–solid fluidization at high temperature
Bibliographic record
Abstract
Abstract Effect of temperature on the hydrodynamics of bubbling gas–solid fluidized beds was investigated in this work. Experiments were carried out at different temperatures ranged of 25–600°C and different superficial gas velocities in the range of 0.17–0.78 m/s with sand particles. The time‐position trajectory of particles was obtained by the radioactive particle tracking technique at elevated temperature. These data were used for determination of some hydrodynamic parameters (mean velocity of upward and downward‐moving particles, jump frequency, cycle frequency, and axial/radial diffusivities) which are representative to solids mixing through the bed. It was shown that solids mixing and diffusivity of particles increases by increasing temperature up to around 300°C. However, these parameters decrease by further increasing the temperature to higher than 300°C. This could be attributed to the properties of bubble and emulsion phases. Results of this study indicated that the bubbles grow up to a maximum diameter by increasing the temperature up to 300°C, after which the bubbles become smaller. The results showed that due to the wall effect, there is no significant change in the mean velocity of downward‐moving clusters. In order to explain these trends, surface tension of emulsion between the rising bubble and the emulsion phase was introduced and evaluated in the bubbling fluidized bed. The results showed that surface tension between bubble and emulsion is increased by increasing temperature up to 300°C, however, after that it acts in oppositely.
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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 | 0.000 |
| 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".