Effects of Particle Size Distribution and Oxygen Concentration on the Propagation Behavior of Pulverized Coal Flames in O<sub>2</sub>/CO<sub>2</sub> Atmospheres
Bibliographic record
Abstract
The ignition and flame propagation behavior of pulverized coal particles in an O 2 /CO 2 atmosphere was studied in a long quartz tube reactor. The effects of mixing ratio of fine (mean diameter 16 μm) and coarse (mean diameter 82 μm) coal particles and oxygen concentration on the ignition characteristics, flame front distance, and flame propagation velocity were investigated by capturing the flame ignition and propagation using a high-speed video camera. The experimental results show that the particle size distribution has a strong influence on the ignition and flame structure of coal particles. Smaller coal particles result in earlier ignition, a smoother flame front, longer flame, and faster flame propagation velocity. Mixing of smaller coal particles with larger ones shortens the ignition delay and enhances the propagation velocity of the flame front. For different coal particle size distributions, the variation of flame propagation velocity with time in general displays an “M”-shaped curve. The curve of flame propagation velocity vs time is single-peaked at 40% oxygen concentration for both coarse and fine particles with mean diameters of 82 and 16 μm, respectively. The effect of oxygen concentration on the flame propagation becomes stronger as the percentage of fine particles increases. The effect of fine coal particles on the volatile release rate of coarse particles was analyzed by numerical simulation. The results show that increasing the ratio of fine coal particles shortens the time for volatile matter release from the coarse coal particles and increases the coarse particle temperature.
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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".