Suitability Study of n-Butanol for Enabling PCCI and HCCI and RCCI Combustion on a High Compression-ratio Diesel Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This work investigates the suitability of n-butanol for enabling PCCI, HCCI, and RCCI combustion modes to achieve clean and efficient combustion on a high compression ratio (18.2:1) diesel engine. Systematic engine tests are conducted at low and medium engine loads (6∼8 bar IMEP) and at a medium engine speed of 1500 rpm. Test results indicate that n-butanol is more suitable than diesel to enable PCCI and HCCI combustion with the same engine hardware. However, the combustion phasing control for n-butanol is demanding due to the high combustion sensitivity to variations in engine operating conditions where engine safety concerns (e.g. excessive pressure rise rates) potentially arise. While EGR is the primary measure to control the combustion phasing of n-butanol HCCI, the timing control of n-butanol direct injection in PCCI provides an additional leverage to properly phase the n-butanol combustion. With the existing engine hardware, n-butanol PCCI outperforms HCCI by offering preferable combustion controllability while both combustion modes meet prescribed targets of low emissions and high efficiency. In RCCI, the auto-ignition of port injected n-butanol tends to occur early in the compression stroke, and thus high EGR and late diesel injection are generally required to attain proper ignition timing and combustion phasing. Although NOx emissions of butanol-diesel dual-fuel combustion meet the target, soot emissions are much higher compared to those in PCCI and HCCI modes.</div></div>
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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 | 0.000 | 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".