Combustion behavior of single iron particles-part I: An experimental study in a drop-tube furnace under high heating rates and high temperatures
Bibliographic record
Abstract
Micrometric spherical particles of iron in two narrow size ranges of (38–45) µm and (45–53) µm were injected in a bench scale, transparent drop-tube furnace (DTF), electrically heated to 1400 K. Upon experiencing high heating rates (104–105 K/s) the iron particles ignited and burned. Their combustion behavior was monitored pyrometrically and cinematographically at three different oxygen mole fractions (21%, 50% and 100%) in nitrogen. The results revealed that iron particles ignited readily and exhibited a bright stage of combustion followed by a dimmer stage. There was evidence of formation of envelope micro-flames around iron particles (nanometric particle mantles) during the bright stage of combustion. As the burning iron particles fell by gravity in the DTF, contrails of these fine particles formed in their wakes. Peak temperatures of the envelope flames were in the range of 2500 K in air, climbing to 2800 K in either 50% or 100% O2. Total luminous combustion durations of particles, in the aforesaid size ranges, were in the range of 40–65 ms. Combustion products were bimodal in size distribution, consisting of micrometric black magnetite particles (Fe3O4), of sizes similar to the iron particle precursors, and reddish nanometric iron oxide particles consisting mostly of hematite (Fe2O3).
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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".