An experimental study on the ignition temperature of iron particles in an electrically-heated drop-tube furnace
Bibliographic record
Abstract
This manuscript reports on experimental research addressing the ignition temperature of iron particles in hot furnace gases and compares this experimental data to previous experiments and theoretical models. Iron particles of different sizes were injected into an electrically heated drop tube furnace in oxygen-containing gases with different mole fractions of nitrogen diluent (including air). Experiments were also conducted using other diluents (helium, argon and carbon dioxide) at a fixed mole fraction of oxygen. Though the ignition temperature of a combustible material is an extrinsic property influenced by structural and operating parameters, useful approximations can still be derived from experiments and theory. This investigation corroborated the onset and probability of ignition by three independent techniques: (a) visual observation of the luminosity of particles, (b) physical examination of the particle size and shape characteristics of the collected products of combustion and (c) assessment of the chemical composition of the collected particles. Results revealed that particle size, shape and particle concentration appeared to be influential, as 28–32 µm spherical particles started to ignite at temperatures above 1037 K, 45–53 µm spherical particles at temperatures above 1022 K, and irregular 49–62 µm particles at temperatures above 968 K. Ignition temperatures increased with decreasing particle concentrations. Tuning the oxygen mole fraction (in the range of 15–100 %) in nitrogen had no discernible effect on ignition temperature. Replacing nitrogen with helium increased the ignition temperature significantly. Replacing nitrogen with argon or carbon dioxide slightly influenced the ignition 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".