Assessment of PAH-Based Precursor Models Using a Seven-Moment Quadrature-Based Closure for Soot Formation Prediction in Non-Premixed Laminar Flames
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-2253.vid The accurate numerical prediction of soot formation in practical combustion devices remains an open challenge. A recently proposed and computationally efficient quadrature-based moment closure based on fractional-order moments of soot particle volume, which is also capable of both capturing the polydispersity and key structural features of soot aggregates, is used here to explore the influence of the choice of polycyclic aromatic hydrocarbons (PAH)-based soot precursors on the formation, evolution, and oxidation of soot in ethylene-air laminar diffusion flames at atmospheric pressures. In particular, a seven-moment Conditional Quadrature Method of Moments (CQMOM) with a fixed quadrature point at the soot particle inception size, resulting in a so-called CQMOM-Radau closure, is used to explore the influence of the choice of soot precusors on soot formation prediction in laminar diffusion flames at atmospheric pressures. The CQMOM-Radau closure of interest involves the solution of a relatively small system of seven moment equations describing the soot transport but yet allows for a bivariate treatment and detailed modelling of the gas-phase chemistry along with treatments for soot particle nucleation, condensation, surface growth, oxidation, coagulation, sintering, obliteration, and fragmentation. The soot formation predictions of the seven-moment closure obtained using several PAH-based soot precursor models are investigated and compared to predictions obtained using a standard acetylene-based two-equation model, as well as available experimental data, for several atmospheric pressure laminar co-flow diffusion flames with ethylene as the fuel. The relative performances of the various PAH-based precursors for predicting the observed soot concentrations, particles sizes, and structure are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".