Improving emissions and performance characteristics of lean burn natural gas engines through partial stratification
Bibliographic record
Abstract
A partially stratified charge (PSC) engine concept has been developed to improve the combustion process in a spark ignition engine fuelled by natural gas, operating at lean air-fuel ratios. In general, this technique is effective at increasing fuel conversion efficiency and brake mean effective pressure at relative air-fuel ratios greater than λ = 1.5. Preliminary testing at 2500r/min showed that PSC reduced combustion duration by almost 10 per cent at a given air-fuel ratio. It was also effective in extending the lean limit of engine operation, which meant that the engine could be run at lower loads without throttling. PSC technology provides an alternative method of load control to throttling, and hence reduces associated pumping losses. Further investigation was conducted at 1500 r/min and a lean air-fuel ratio of λ = 1.65. PSC was found to significantly improve engine performance when compared with homogeneous charge operation at the same air-fuel ratio. The optimized data were compared to simple throttled and lean baseline data and a dramatic reduction in NO x emissions was observed, with little efficiency penalty. These initial results demonstrate that the lean-burn, partially stratified charge strategy is a viable means of engine control, and has significant performance and emissions benefits.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".