An Experimental Investigation of S.I. Engine Operation on Gaseous Fuels Lean Mixtures
Bibliographic record
Abstract
The operation of S.I. engines on lean or diluents containing gaseous fuel-air mixtures is attractive in principle since it can provide improved fuel economy, reduced tendency to knock and low NOx emissions combined with a possible improvement to the operational life of the engine. However, the overall flame propagation rates then tend to drop sharply as the operational mixture is excessively leaned or diluted with CO2 or N2. The paper presents experimental data obtained in a single cylinder, variable compression ratio, S.I., CFR engine when operated on a number of gaseous fuels and some of their mixtures. A gradual leaning of the operating mixture can affect adversely in turn, emissions of CO and unburned fuel and cyclic variation. The extent of deterioration in these operating parameters is shown to correlate well with the corresponding values of the combustion period, a key combustion indicator. Similar effects were observed when adding diluents to stoichiometric CH4-air mixtures. The addition of H2 to CH4 tends to accelerate the flame propagation and improve combustion stability but enhances the formation of NOx, especially for lean mixtures operation. A discussion of the possible reasons for the trends observed is presented together with outlining some possible measures to obtain low NOx emissions while keeping satisfactory rates of flame propagation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".