The Lean Mixture Operational Limits of a Spark Ignition Engine When Operated on Fuel Mixtures
Bibliographic record
Abstract
The operation of spark ignition (SI) engines on lean mixtures is attractive, in principle, since it can provide improved fuel economy, reduced tendency to knock, and extremely low NOx emissions. However, the associated flame propagation rates become degraded significantly and drop sharply as the operating mixture is made increasingly leaner. Consequently, there exist distinct operational lean mixture limits beyond which satisfactory engine performance cannot be maintained due to the resulting prolonged and unstable combustion processes. This paper presents experimental data obtained in a single cylinder, variable compression ratio, SI engine when operated in turn on methane, hydrogen, carbon monoxide, gasoline, iso-octane, and some of their binary mixtures. A quantitative approach for determining the operational limits of SI engines is proposed. The lean limits thus derived are compared and validated against the corresponding experimental results obtained using more traditional approaches. On this basis, the dependence of the values of the lean mixture operational limits on the composition of the fuel mixtures is investigated and discussed. The operational limit for throttled operation with methane as the fuel is also established.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".