Detailed modeling of aluminum particle combustion – From single particles to cloud combustion in Bunsen flames
Bibliographic record
Abstract
A numerical model for aluminum cloud combustion which includes the effects of interphase heat transfer, phase change, heterogeneous surface reactions, homogeneous combustion, oxide cap growth and radiation within the Euler–Lagrange framework is proposed. The model is validated in single particle configurations with varying particle diameters. The combustion process of a single aluminum particle is analyzed in detail and the particle consumption rates as well as the heat release rates due to the various physical/chemical sub-models are presented. The combustion time of single aluminum particles predicted by the model are in very good agreement with empirical correlations for particles with diameters larger than 10 μm. The prediction error for smaller particles is noticeably reduced when using a heat transfer model that is capable of capturing the transition regime between continuum mechanics and molecular dynamics. The predictive capabilities of the proposed model framework are further evaluated by simulating the aluminum/air Bunsen flames of McGill University for the first time. Results show that the predicted temperature distribution of the flame is consistent with the experimental data and the double-front structure of the Bunsen flame is reproduced well. The burning rates of aluminum in both single particle and particle cloud configurations are calculated and compared with empirical correlations. Results show that the burning rates obtained from the present model are more reasonable, while the correlations, when embedded in the Euler–Lagrange context, tend to underestimate the burning rate in the combustion stage, particularly for the considered fuel-rich flames.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".