Assessment of physical soot inception model in normal and inverse laminar diffusion flames
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".