Ignition Improvement for Ultra-Lean Dilute Gasoline Combustion
Bibliographic record
Abstract
In this work, a spatially distributed spark ignition strategy was employed to improve the ignition process of well-mixed ultra-lean dilute gasoline combustion in a high compression ratio (13.1:1) single cylinder engine at partial loads. The ignition energy was distributed in the perimeter of a 3-pole igniter. It was identified that on the basis of similar total spark energy, the 3-pole ignition mode can significantly shorten the early flame kernel development period and reduce the cyclic variation of combustion phasing, for the spark timing sweep tests at λ 1.5. The effect of ignition energy level on lean-burn operation was investigated at λ 1.6. Within a relatively low ignition energy range, i.e. below 46 mJ per pole, the increase in ignition energy via ether 1 pole or 3 pole can improve the controllability over combustion phasing and reduce the variability of lean burn combustion. Higher ignition energy was required in order to enable ultra-lean engine operation with λ above 1.6. With the highest ignition energy achievable for the tested ignition system, the stable operable lean limits at a nominal engine load of 3 bar indicated mean effective pressure (IMEP) was extended by using 3-pole ignition, which consequently increased the thermal efficiency.
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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".