Laminar Flame Speed Measurements and Modeling of Alkane Blends at Elevated Pressures With Various Diluents
Bibliographic record
Abstract
Laminar flame speeds at elevated pressure for methane-based fuel blends are important for refining the chemical kinetics that are relevant at engine conditions. The present paper builds on earlier measurements and modeling by the authors by extending the validity of a chemical kinetics mechanism to laminar flame speed measurements obtained in mixtures containing significant levels of helium. Such mixtures increase the stability of the experimental flames at elevated pressures and extend the range of laminar flame speeds. Two experimental techniques were utilized, namely a Bunsen burner method and an expanding spherical flame method. Pressures up to 10 atm were studied, and the mixtures ranged from pure methane to binary blends of CH4/C2H6 and CH4/C3H8. In the Bunsen flames, the data include elevated initial temperatures up to 650 K. There is generally good agreement between model and experiment, although some discrepancies still exist with respect to equivalence ratio for certain cases. A significant result of the present study is that the effect of mixture composition on flame speed is well captured by the mechanism over the extreme ranges of initial pressure and temperature covered herein. Similarly, the mechanism does an excellent job at modeling the effect of initial temperature for methane-based mixtures up to at least 650 K.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".