Experimental Investigation of Diesel-Ethanol Premixed Pilot-Assisted Combustion (PPAC) in a High Compression Ratio Engine
Bibliographic record
Abstract
In this work, empirical investigations of the diesel-ethanol Premixed Pilot-Assisted Combustion (PPAC) are carried out on a high compression ratio (18.2:1) single-cylinder diesel engine. The tests focus on determining the minimum ethanol fraction for ultra-low NOx & soot emissions, effect of single-pilot vs. twin-pilot strategies on emissions and ignition controllability, reducing the EGR requirements, enabling clean combustion across the load range and achieving high efficiency full-load operation. The results show that both low NOx and almost zero soot emissions can be achieved but at the expense of higher unburned hydrocarbons. Compared to a single-pilot injection, a twin-pilot strategy reduces the soot emissions significantly and also lowers the NOx emissions, thereby reducing the requirements for EGR. The near-TDC pilot provides excellent control over the combustion phasing, further reducing the need of a higher EGR quantity for phasing control. The minimum ethanol-to-diesel quantity ratio can be quantified by the ‘Heat Release Profile Distribution’ parameter that identifies the transition from the diesel pilot dominated heterogeneous combustion to predominantly premixed, homogenous ethanol combustion and allows the two fuel quantities to be optimized for emissions and efficiency. Test results with NOx-soot emission compliance are presented for the full load range of the test engine using the single-pilot PPAC.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".