Bibliographic record
Abstract
Soot models have been investigated for several decades and many fundamental models exist that prescribe soot formation in agreement with experiments and theories. However, due to the complex nature of soot formation, not all pathways have been fully characterized. This work has numerically studied the influence that aliphatic based inception models have on soot formation for coflow laminar diffusion flames. CoFlame is the in-house parallelized FORTRAN code that was used to conduct this research. It solves the combustion fluid dynamic conservation equations for a variety of coflow laminar diffusion flames. New soot inception models have been developed for specific aliphatics in conjunction with polycyclic aromatic hydrocarbon based inception. The purpose of these models was not to be completely fundamental in nature, but more so a proof-of-concept in that an aliphatic based mechanism could account for soot formation deficiencies that exist with just PAH based inception. The aliphatic based inception models show potential to enhance predicative capability by increasing the prediction of the soot volume fraction along the centerline without degrading the prediction along the pathline of maximum soot. Additionally, the surface reactivity that was used to achieve these results lied closer in the range of numerically derived optimal values as compared to the surface reactivity that was needed to match peak soot concentrations without the aliphatic based inception models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".