Reduction of Soot Formation in an Optical Single-Cylinder Gasoline Direct-Injected Engine Operated in Stratified Mode Using 350 Bar Fuel Injection Pressure, Dual-Coil and High-Frequency Ignition Systems
Bibliographic record
Abstract
The current trend toward more fuel efficient vehicles with lower emission levels has prompted development of new combustion techniques for use in gasoline engines. Stratified combustion has been shown to be a promising approach for increasing the fuel efficiency. However, this technique is hampered by drawbacks such as increased particulate and standard emissions. This study attempts to address the issues of increased emission levels by investigating the influence of high frequency ionizing ignition systems, 350 bar fuel injection pressure and various tumble levels on particulate emissions and combustion characteristics in an optical SGDI engine operated in stratified mode on isooctane. Tests were performed at one engine load of 2.63 bar BMEP and speed of 1200 rpm. Combustion was recorded with two high speed color cameras from bottom and side views using optical filters for OH and soot luminescence. The results indicated that increasing the fuel injection pressure led to faster burn as well as a reduction in soot luminescence. The ionizing ignition system generated faster initial combustion. Increasing the tumble level reduced the soot luminescence at all injection pressures, but the influence was largest at the lowest fuel injection pressure. The combination of an ionizing ignition system and high fuel pressure was most beneficial for lowering soot luminescence.
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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".