CFD modelling of dust explosions: Rapid combustion in a 20 L apparatus
Bibliographic record
Abstract
Abstract Dust explosions are combustion of fine dust particles in a rapid reaction regime. They are of relatively common occurrence in industries which have unit operations like grinding, pneumatic conveying, drying, and fine particle collection: generally those units which handle fine particles or “dust” in an oxygen‐rich environment. At present, mitigation and prevention of dust explosion accidents is largely based on operator experience and safety inspection heuristics: knowledge that often cannot be documented or put into scientifically developed safety rules. Part reason for this state of affairs is our lack of fundamental understanding on how a dust explosion progresses after ignition of the dust cloud. In this contribution, with a view towards improving our understanding of dust explosions, we propose a multi‐scale modelling approach for modelling dust explosion phenomena in a standard 20 L Siwek Apparatus. The modelling approach is based on computational fluid dynamics methods treating Aluminum dust cloud as a quasi‐homogeneous phase.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".