Assessment of physical soot inception model in normal and inverse laminar diffusion flames
Why this work is in the frame
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Bibliographic record
Abstract
Despite the extensive studies, accurate and reliable modeling of the soot inception process, especially at high pressure conditions , amenable to multi-dimensional flame simulations remains a challenge. In this study, the physical inception model was comprehensively evaluated in the fully-resolved simulations of laminar normal diffusion flame (NDF) and inverse diffusion flame (IDF) at elevated pressures. The effects of inception models on polycyclic aromatic hydrocarbons (PAHs) and soot predictions were quantitatively analyzed, including the selection of soot precursors and collision efficiency models. The results show that the quantitative PAH predicted by different collision efficiency models can differ by an order of magnitude. Compared to the constant efficiency, the temperature-dependent collision efficiency was found to improve the quantitative PAH predictions and the prediction of the spatial soot distribution in NDF, with an increased level of soot on the flame centerline . The inclusion of small-sized PAH species (such as A 2 , A 2 R 5 , and A 3 ) as soot precursors was also found to improve the quantitative prediction of soot volume fraction. The physical inception model performs well in NDF using the optimal parameters. Moreover, simultaneous measurements of PAH and soot were performed in IDF configuration for the evaluation of the physical inception model. Contrary to NDF, PAHs and soot are formed on the outer side of the flame and cannot be oxidized in IDF. The experiment observed that the PAHs concentration increased in the post-flame region, while the soot concentration remained unchanged. However, the opposite trend was obtained in simulations, that is, the PAHs concentration decreased while the soot concentration increased, because the physical inception model predicts the inception behavior in the post-flame area, resulting in persistent transformation of PAHs into soot particles. To improve the predictions in IDF, the radical effects in the inception process need to be considered in the model.
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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 | 0.000 | 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 | 0.000 |
| 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 it