Early Pilot Injection Strategies for Reactivity Control in Diesel-ethanol Dual Fuel Combustion
Bibliographic record
Abstract
This paper examines the diesel-ethanol dual fuel combustion at medium engine loads on a single-cylinder research diesel engine with a compression ratio of 16.5:1. The effect of exhaust gas recirculation (EGR) and ethanol energy ratio was investigated for the dual fuel combustion to achieve simultaneously ultra-low NOx and soot emissions. A medium ethanol ratio of about 0.6 was found suitable to meet the requirements for mixing enhancement and ignition control, which resulted in the lowest NOx and soot emissions among the tested ethanol ratios. A double-pilot injection strategy was found competent to lower the pressure rise rate owing to the reduced fuel quantity in the close-to-TDC injection. The advancement of pilot injection timing tended to reduce the CO and THC emissions, which is deemed beneficial for high EGR operations. The reactivity mutual-modulation between the diesel pilot and the background ethanol mixture was identified. The experiments confirmed that the background ethanol mixture could delay the ignition of the diesel pilot. The background ethanol can suppress the low-temperature heat release of the diesel pilot. With the close-to-TDC pilot as a reliable ignition source, the combustion phasing was controlled by the reactivity modulation of the cylinder charge through coordinated adjustment of the fuel quantity of the early injection diesel pilot and the port injection ethanol.
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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".