Impact of pressure-based HACA rates on soot formation in varying-pressure coflow laminar diffusion flames
Bibliographic record
Abstract
There are several processes to occur in soot formation and destruction for which some in the growth regime require a better understanding. In this work, a consistent surface reactivity model, developed in recent years, has been implemented across various sooting laminar flames at varying pressures. The surface reactivity function proposed by Khosousi and Dworkin [1] is employed in the present study. It is based on the temperature history of soot particles. As the functionally dependent model has been derived and validated for atmospheric pressure flames, there are discrepancies between simulation and experiment that can be observed as pressures vary. One reason for these discrepancies could be explained by the fact that chemical reaction rates for the soot growth mechanism at atmospheric combustion do not adequately characterize the kinetics at higher pressures. Based on a recently published study [2], the elementary reaction rates that compose the Hydogen-Addition-Carbon-Abstraction soot surface growth mechanism depend on pressure and an empirical pressure scaling factor to account for this pressure dependence has been introduced. It has been determined that after applying the new empirical pressure scaling factor for the soot growth mechanism, the performance of the functionally dependent surface reactivity model improves in the wing regions of the flame for pure-ethylene flames; however, there is minimal change on the wings for the nitrogen-diluted flames. Additionally, the quantity for soot concentration along the centerline of all flames is nearly independent of the surface reactivity model chosen and needs further investigation. For the flames investigated, it is concluded that pressure dependent HACA rates do not alleviate all discrepancies between the numerical and experimental results and that further work is required.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".