The Ultra Lean Burn Partially Stratified Charge Natural Gas Engine
Bibliographic record
Abstract
It is well known that lean operation of homogeneous- charge spark-ignited engines is effective in increasing thermal efficiency and reducing exhaust emissions. In particular, the lower combustion temperatures provided by a lean air-fuel mixture result in a significant reduction in NOx emissions. Lean operation is normally restricted, however, by the “lean-limit” of combustion, as measured by the air-fuel ratio above which ignition is impossible, or combustion is incomplete. In order to extend the lean limit of operation a new “partially-stratified charge” combustion concept has been developed. This technique relies on the fact that a stronger initial flame kernel produced following the spark event should also be effective in igniting a very lean mixture which may not otherwise ignite, or which may result in incomplete combustion. An innovative spark-plug insert design, in which a small portion of “pilot fuel” is injected directly near the spark plug electrodes, provides a small pocket of relatively rich mixture that ignites more readily than the main, very lean, combustion charge. This has then been used to extend the lean-limit of operation of natural-gas fuelled spark-ignition engines, resulting in reduced brake specific fuel consumption and significantly lower levels of NOx emissions. This process has also been shown to be effective in increasing the stability of combustion, thereby reducing cyclic variations in cylinder pressure. Recent data obtained using this concept in a single-cylinder research engine are reported in this paper. In addition, some initial studies of the partially stratified-charge mixing process are also described.
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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) | 0.001 | 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".