The Influence of Injector Deposits on Mixture Formation in a DISC SI Engine
Bibliographic record
Abstract
This paper presents a follow on study from earlier work investigating the influence of fuel parameters on the deposit formation and emissions from a direct injection stratified charge spark ignition engine. It was shown that injector fouling was the main reason for the increase in unburned hydrocarbon emissions and spray visualizations supported these results. The hypothesis is that the deposit buildup in the injector caused the increased hydrocarbon emissions due to an increased wall film formation. To further verify the findings, Phase Doppler Anemometry measurements at simulated engine conditions, were performed. Measurements recorded on the injector axis 20 mm downstream from the injector orifice, showed that the initial pre-jet velocity was 30% higher and the drop mean diameter was 5% larger in the case of a used injector compared to a new injector. Based on these investigations, spray files were set-up in the 3-D CFD-code AVL FIRE™. A moving calculation mesh, capturing the main geometrical features from the Mitsubishi GDI® combustion system, was created. With this calculation mesh, the influence of important parameters on the mixture formation was studied for the two injectors. Simulations showed that the mass of fuel captured in the wall film with a new injector was equivalent to 10% of the total injected fuel mass. With a fouled injector this rose to around 30%. This supports the hypothesis that the injector fouling makes a significant contribution to the increased amounts of hydrocarbon emissions found in engine tests.
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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".