A quantitative analysis of the ignition characteristics of fine iron particles
Bibliographic record
Abstract
Ignition of iron particles in an oxidizing environment marks the onset of self-sustained combustion. The objective of the current study is to quantitatively examine the ignition characteristics of fine iron particles (i.e., 1µm- to 100µm-sized) governed by the kinetics of solid-phase iron oxidation. The oxidation rates are inversely proportional to the thickness of the oxide layer (i.e., following a parabolic rate law) and calibrated using the experimentally measured growth of iron-oxide layers over time. Steady-state (i.e., Semenov’s analysis) and unsteady analysis have been performed to probe the dependence of the critical gas temperature required to trigger a thermal runaway (namely, the ignition temperature Tign) on particle size, initial thickness of oxide layer, inert gas species, radiative heat loss, and the collective heating effect in a suspension of particles. Both analyses indicate that Tign depends on δ0, i.e., the ratio between the initial oxide layer thickness and particle size, regardless of the absolute size of the particle. The unsteady analysis predicts that, for δ0≲0.003, Tign becomes independent of δ0. Under standard conditions in air, Tign is approximately 1080 K for any particle size greater than 5µm. The ignition temperature decreases as the thermal conductivity of the oxidizing gas decreases. Radiative heat loss has a minor effect on Tign. The collective effect of a suspension of iron particles in reducing Tign is demonstrated. The transition behavior between kinetic-controlled and external-diffusion-controlled combustion regimes of an ignited iron particle is systematically examined. The influences of initial oxide-layer thickness and particle temperature on the ignition delay time, τign, of iron particles are parametrically probed. A d2-law scaling between τign and particle size is identified. Possible sources of inaccuracy are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".