Numerical Calculation of Quench Distance for Laminar Premixed Flames Under Engine Relevant Conditions
Bibliographic record
Abstract
The quenching of premixed laminar flames at various constant pressures was studied through numerical simulation, with the Trajectory Generated Lower Dimensional Manifold (TGLDM) method used to employ detailed chemical mechanisms for stoichiometric methane and heptane flames. The method was validated at lower pressures and wall temperatures. The laminar flame speed predicted by the TGLDM method agrees reasonably well with experimental data reported in the literature. The peak heat flux at quenching was found to be under-predicted by 30-40% of the most current experimental data. The quench distance was calculated for pressures of 1, 2, 20 and 40 bar, with wall temperatures of 300 and 600 K and fresh gas temperature of 300 K. The quench distance was found to decrease with increasing pressure in a manner similar to previous studies. The value of quench distance for heptane was found to be smaller than that of methane by a factor of ~30% over all pressures. The peak heat flux values were used to evaluate the thermal model of Boust et al., for calculating quench distance and was found to predict the right trend, though the quench distance values are lower than those observed in experiment. The applicability of these results to internal combustion engines is briefly discussed by calculating a rough estimate of the fuel left unburned in the quenching layer for a spark-ignited engine, and a proposal for the computational implementation of Boust's thermal model is explained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".