Emission Analysis of HCCI Combustion in a Diesel Engine Fueled by Butanol
Bibliographic record
Abstract
Advances in engine technology in recent years have led to significant reductions in the emission of pollutants and gains in efficiency. As a facet of investigations into clean, efficient combustion, the homogenous charge compression ignition (HCCI) mode of combustion can improve upon the thermal efficiency and nitrogen oxides emission of conventional spark ignition engines. With respect to conventional diesel engines, the low nitrogen oxides and particulate matter emissions reduce the requirements on the aftertreatment system to meet emission regulations. In this paper, n-butanol, an alcohol fuel with the potential to be derived from renewable sources, was used in a light-duty diesel research engine in the HCCI mode of combustion. Control of the combustion was implemented using the intake pressure and external exhaust gas recirculation. The moderate reactivity of butanol required the assistance of increased intake pressure for ignition at the lower engine load range. The upper load range was limited by the excessive pressure rise rate at a modest engine load. Within this possible load range, an analysis of the hydrocarbon species in the exhaust was performed using Fourier transform infrared spectroscopy to gain insight into the combustion characteristic of butanol in HCCI. A comparison is made with gasoline fuel to compare the reactivity of butanol in this pair of high volatility fuels.
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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 | 0.000 | 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".