A computational investigation of pressure effects on soot formation in counterflow diffusion flames of methane in MILD conditions
Bibliographic record
Abstract
Moderate or Intense Low-oxygen Dilution (MILD) combustion has been extensively studied as a promising technology to achieve high efficiency and low-emission power generation . The present study numerically investigates the soot formation in non-premixed methane-air flames in MILD conditions at elevated pressures of up to 20 atm. The soot formations under MILD conditions are compared with their conventional counterparts to elucidate the underlying physical and chemical pathways affecting the sooting features. The gas-phase kinetic mechanism is a reduced version of KAUST Aramco PAH Mech 1.0, which has been validated for C 1 and C 2 fuels for the prediction of PAHs (polycyclic aromatic hydrocarbons) species up to coronene (C 24 H 12 ). A sectional method is used for the soot-aerosol model. The soot formation (SF) flame, with a high strain rate under conventional and MILD combustion conditions, is employed for the investigation. An improved consistent soot model comprising a broad range of precursors from A 2 (naphthalene) to A 7 (coronene) is used for the analysis. The results show that MILD combustion produces an extremely low soot compared to its conventional counterparts at high pressures. The inception rate has a larger contribution towards the overall soot mass growth rate when compared with the HACA rate and condensation rate in MILD conditions. Conversely, the HACA rate is higher than the inception and condensation rates in conventional conditions, suggesting that the soot mass growth rate is HACA rate-oriented. The soot volume fraction and particle number density increase with pressure, and their peak values are positioned near the oxidizer side of the stagnation plane for both conventional and MILD conditions. A rise in pressure increases the major precursors for soot formation, such as benzene (A 1 ), naphthalene (A 2 ), pyrene (A 4 ), and coronene (A 7 ) in both conventional and MILD conditions. It also enhances the inception, HACA, condensation, and oxidation rates for soot.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".