Modeling of Oxidation-Driven Soot Aggregate Fragmentation in a Laminar Coflow Diffusion Flame
Bibliographic record
Abstract
In this study, three different oxidation-driven soot aggregate fragmentation models with 1:1, 2:1, and 10:1 fragmentation patterns are developed and implemented into a laminar coflow ethylene/air diffusion flame, together with a pyrene-based soot model and a sectional aerosol dynamics model. It is found that the average degree of particle aggregation (n p ) in the soot oxidation region is not correctly predicted if oxidation-driven aggregate fragmentation is neglected; whereas the incorporation of aggregate fragmentation significantly improves the n p prediction in the soot oxidation region. Similar results are obtained using the 1:1 and 2:1 fragmentation patterns. However, as the pattern ratio increases to 10:1, appreciable difference in the predicted n p is observed. As the pattern ratio becomes larger, the fragmentation effect diminishes and the predicted n p approaches that of the original model neglecting fragmentation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".