Experimental Investigation of the Influence of Boost on Combustion and Particulate Emissions in Optical and Metal SGDI-Engines Operated in Stratified Mode
Bibliographic record
Abstract
Boosting and stratified operation can be used to increase the fuel efficiency of modern gasoline direct-injected (GDI) engines. In modern downsized GDI engines, boosting is standard to achieve a high power output. However, boosted GDI-engines have mostly been operated in homogenous mode and little is known about the effects of operating a boosted GDI-engine in stratified mode. This study employed optical and metal engines to examine how boosting influences combustion and particulate emission formation in a spray-guided GDI (SGDI), single cylinder research engine. The setup of the optical and metal engines was identical except the optical engine allowed optical access through the piston and cylinder liner. The engines were operated in steady state mode at five different engine operating points representing various loads and speeds. The engines were boosted with compressed air and operated at three levels of boost, as well as atmospheric pressure for comparison. The fuel used was market gasoline (95 RON) blended with 10% ethanol. The spark plug and injector were mounted in parallel with the intake valves. The gas motion induced by the engine head was primarily tumble motion with a small amount of swirl. Results on particulate emissions indicated that nucleation mode particulates increased with increasing boost. In contrast, agglomeration mode particulates decreased with increasing boost pressure. The combustion was found to consist of a yellow flame in the center of the combustion chamber and a pre-mixed blue flame in the perimeter. The optical studies indicated that the flame area decreased with increasing boost.
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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".