Assessment of Aliphatic Based Soot Inception in Laminar Diffusion Flames
Bibliographic record
Abstract
Soot models are key components of computation fluid dynamic combustion codes that attempt to prescribe how soot is formed. However, due to the complex nature of soot formation, not all pathways may have been fully characterized. This work investigates numerically the influence that an aliphatic-collision (open-chain hydrocarbon) based soot inception model has on soot formation for coflow ethylene/air and methane/air laminar diffusion flames. In the literature, prediction of the soot volume fraction along the centerline of coflow ethylene flames is lacking in accuracy. Similarly for methane flames, soot formation on the wings are under predicted by many models. A new collision based inception model has been developed for specific aliphatics, and applied using an existing framework for molecular collision, in conjunction with pyrene based inception. The purpose of this model is not to be completely fundamental in nature, but more so a proof of concept in that by using physically realistic values for surface reactivity and collision efficiency, this collision mechanism can account for soot formation deficiencies that exist with just polycyclic aromatic hydrocarbon (PAH) based inception. Using this new model, the peak soot volume fraction along the centerline of an ethylene flame can be increased while the peak soot volume fraction along the wings remains unchanged, showing potential to significantly improve the model's predicative capability. Applying this model to a methane flame has resulted in an increase in the soot volume fraction in both the centerline and the wings, again improving predictive capability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".