Numerical Study on Surface Oxidation of Carbonaceous Nano- and Micro- Particles in a Heavily Sooting Ethylene Turbulent Jet Flame
Bibliographic record
Abstract
A hybrid finite element volume FEV method is further extended to simulate the formation and full oxidation of carbonaceous nanoand microparticles in a heavily sooting ethylene turbulent non-premixed flame. Regarding the aerosol dynamics and chemistry, we use the two-equation soot model, based on the acetylene-route nucleation, and take into account the soot oxidation via oxygen molecules. Encountering with a full soot oxidation phenomenon, we need to respect the full physics of flow in our simulations. So, we use the physical upwinding influence scheme PIS to approximate the soot mass fraction fluxes at all cell faces very accurately. For a more complete reaction consideration, we implement a detailed kinetics scheme consisting of 111 chemical species and 1835 chemical reactions, while the flamelet model is utilized for more accurate combustion modeling. For turbulence closure, we employ the two-equation standard κ-e turbulence model incorporated with additional wall functions. The turbulence-chemistry interaction is taken into account in the pre-computed flamelets using the presumed shape probability density functions PDFs. The radiation effects of the most important radiating species and soot are taken into account supposing an optically thin limit. The governing equations are solved thorough two sequential global stiffness matrices. Comparing with the experimental measurements, the current solution successfully predicts the temperature field and the volume fraction distribution of carbonaceous nanoand microparticles very reliably.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".