Influence of Fuel Parameters on Deposit Formation and Emissions in a Direct Injection Stratified Charge SI Engine
Bibliographic record
Abstract
This work investigates the influence of fuel parameters on deposit formation and emissions in a four-cylinder direct injection stratified charge (DISC) SI engine. The engine tested is a commercial DISC engine with a wall-guided combustion system. The combustion chamber deposits (CCDs) were analyzed with gas chromatography / mass spectrometry as well as thickness and mass measurements. Intake valve deposits (IVDs) were analyzed for mass, while internal injector deposits were evaluated using spray photography. The CCD build-up was obtained with the CEC1 F-020-A-98 performance test for evaluation of the influence of fuels and additives on IVDs and CCDs in port fuel injected SI engines. The 60 h test is designed to simulate city driving. Four fuels were compared in the study: a base gasoline, with and without a fuel additive, a specially blended high volatility gasoline, and a fuel representing the worst case of European gasolines; neither of the latter two had additives. During the first six hours of the cycle, the hydrocarbon (HC) emissions decreased for all four fuels. The explanation is most likely that the insulating effect of the deposits is beneficial for reducing HC. After the initial decrease, the HC emissions, as well as the misfire frequency, from the base fuel without the additive and the worst case fuel increased with time. Injector fouling was found to be responsible for the increase in HC emissions. The HC emissions from the base fuel with the additive and the high volatility fuel without the additive remained at a fairly constant level after the initial decrease. Although the CCD and IVD masses were greater for the worst case fuel, there was no increase of HC emissions.
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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 | 0.001 |
| 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 | 0.001 | 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".