Ignition and Flame Speed Kinetics of Two Natural Gas Blends With High Levels of Heavier Hydrocarbons
Bibliographic record
Abstract
High-pressure experiments and chemical kinetics modeling were performed to generate a database and a chemical kinetic model that can characterize the combustion chemistry of methane-based fuel blends containing significant levels of heavy hydrocarbons (up to 37.5% by volume). Ignition delay times were measured in two different shock tubes and in a rapid compression machine at pressures up to 34 atm and temperatures from 740 K to 1660 K. Laminar flame speeds were also measured at pressures up to 4 atm using a high-pressure vessel with optical access. Two different fuel blends containing ethane, propane, n-butane, and n-pentane added to methane were studied at equivalence ratios varying from lean (0.3) to rich (2.0). This paper represents the most comprehensive set of experimental ignition and laminar flame speed data available in the open literature for CH4/C2H6/C3H8/C4H10/C5H12 fuel blends with significant levels of C2+ hydrocarbons. Using these data, a detailed chemical kinetics model based on current and recent work by the authors was compiled and refined. The predictions of the model are very good over the entire range of ignition delay times, considering the fact that the data set is so thorough. Nonetheless, some improvements to the model can still be made with respect to ignition times at the lowest temperatures and for the laminar flame speeds at pressures above 1 atm and at rich conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".