Sources of Hydrocarbon Emissions from a Direct Injection Stratified Charge Spark Ignition Engine
Bibliographic record
Abstract
The purpose of this paper is to assess the influence of fuel properties on cycle-resolved exhaust hydrocarbons and investigate the sources of hydrocarbon (HC) emissions in a direct injection stratified charge (DISC) SI engine. The tested engine is a single cylinder version of a commercial DISC engine that uses a wall guided combustion system. The HC emissions were analyzed using both a fast flame ionization detector (Fast FID) and conventional emission measurement equipment. Three fuels were compared in the study: iso-Pentane, iso-Octane and a gasoline of Japanese specification. The measurements were conducted at part-load, where the combustion is in stratified mode. The start of injection (SOI) was altered in relation to the series calibration to vary the mixture preparation time, the time from SOI to ignition. The ignition timing was set at maximum brake torque (MBT) for each test. The measurements indicate that overmixing (quenching) at the boundaries of the air/fuel mixture cloud, and undermixing both in the spray center and on the surface of the piston bowl are the dominating mechanisms for HC emission formation in this engine. There is a fuel dependent optimum injection timing that minimizes the sum of HC from overmixing and undermixing. As the mixture preparation time is reduced undermixing increases and consequently carbon monoxide and soot emissions increase.
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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".