AN IMPROVED MOVING SECTIONAL AEROSOL MODEL OF SOOT FORMATION IN A PLUG FLOW REACTOR
Bibliographic record
Abstract
In conjuction with aerosol dynamics theory and by incorporating complex gas phase and particulate phase chemistry, sectional soot models can provide a detailed description of soot particle structures formed in a combustion environment. A recently developed moving sectional approach has been improved in this study to provide a more accurate description of the soot particle size distribution. The soot prediction for a Plug Flow Reactor (PFR), which is fueled by an ethylene/air mixture (with the equivalence ratio of 2.2), has been compared with experimental data and simulation results provided by both the conventional fixed sectional approach and the method of moments model. The soot inception, surface growth/oxidation, and polycyclic aromatic hydrocarbon (PAH) condensation submodels were based on a previous study and coupled with a detailed reaction mechanism of C2 hydrocarbons. The results show that the improved moving sectional approach calculated a particle size distribution which was in good agreement with the fixed sectional approaches with finer grids, but required less CPU effort (less than 50%). The computed particle size distributions were validated by experimental data. The soot mass concentration and total number density predicted by three sectional approaches have been compared with the method of moments model for different simulation cases and it shows that the four numerical methods of aerosol soot model provided consistent results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".