Ignition Control of Gasoline-Diesel Dual Fuel Combustion
Bibliographic record
Abstract
The use of gasoline fuels in compression ignition engines, with or without diesel pilots, has shown encouraging progress in engine efficiency and emissions. The dual fuel combustion of gasoline-diesel offers the flexibility of modulating the cylinder charge reactivity, but an accurate and reliable control over the ignition in the dual fuel applications is more challenging than in classical engines. In this work, the gasoline-diesel dual fuel operation is investigated on a single cylinder research engine. The effects of the intake boost, exhaust gas recirculation (EGR) rates, diesel/gasoline ratio, and diesel injection timing are studied in regard to the ignition control. The results indicate that at low load, a diesel pilot can improve the cylinder charge reactivity and reduce emissions of incomplete combustion products. However, as the engine load is raised, the port-injected gasoline tends to auto-ignite prior to the end of the compression stroke, and the NOx and soot emissions are significantly increased owing to the concurrence of the diesel injection event and the gasoline early auto-ignition. The test results also prove that such premature auto-ignition can be withheld by increasing the use of EGR and/or decreasing the gasoline fuelling amount. A reliable ignition control is identified as critical to enable the dual fuel combustion with ultralow NOx and soot.
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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 | 0.001 | 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".