Decoupling pyrolysis and combustion of organic powders to determine the laminar flame speed
Bibliographic record
Abstract
Abstract Determining the laminar flame speed of dusts is far from straightforward. A strong dependency on the experimental setup and the data treatment's high complexity makes it a true challenge. This work compares three complementary experimental setups to measure the laminar flame speed of organic dust (here, cellulose): a modified Hartmann tube, a 20 L sphere, and a micro‐fluidized bed (MFB) burner. The first two consider the flame propagation phenomenon in its globality, which means that numerous steps are involved simultaneously (particle heating, pyrolysis, oxidation, radiative transfer, flame stretching), while the third one decouples pyrolysis and combustion, to focus mainly on the oxidation rate. An MFB was conceived to generate pyrolysis products and burn them in a laminar flame. Unstretched flame velocities determined with the first two setups were consistent and equal to 22.0 and 26.6 cm ∙ s −1 , respectively. Using Silvestrini's equation, values ranging between 14.0 and 33.4 cm ∙ s −1 were obtained according to the dust concentration. With the MFB burner, the flame speed was much higher (135–155 cm ∙ s −1 ), due to the higher temperature of the fresh mixture and the fact that only the oxidation of the pyrolysis gases is considered. A numerical simulation (Chemkin) confirmed these results since the range 135 to 231 cm ∙ s −1 was obtained for equivalence ratios of 0.6 and 1.2, respectively. The discrepancy between the laminar flame speed determined in the sphere or in the tube and that obtained in the MFB highlights the significant influence of particle heating and pyrolysis during a dust explosion.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".