Effect of renewable fuel components on combustion and emission performance of an HCCI engine
Bibliographic record
Abstract
Renewable fuels usually consist of paraffinic hydrocarbons and are free of sulfur and aromatics. Although most renewable fuels meet the conventional fuel requirements for internal combustion engine applications, differences in fuel properties between renewable fuels and petroleum-based fuels still exist. The effect of renewable fuels on conventional diesel engines has been investigated by many researchers. However, few studies have been conducted on the effect of renewable fuels on combustion and emission performance of homogeneous charge compression ignition(HCCI)engines. In this paper, the combustion and emission characteristics of an HCCI engine are experimentally investigated when four neat renewable fuel components and their blends with a petroleum-based diesel were used. The ratios of renewable fuel components in the blends were changed from zero to 100%. The experiments were conducted over a wide range of operational conditions. Energy efficiency and regulated emissions data were collected and analyzed. The results suggest that compared to the petroleum-based diesel fuel, all four investigated renewable fuel components increase the fraction of heat release during low temperature stage and reduce the ignition temperature when applied to an HCCI engine. As a result, the investigated renewable fuel components advance combustion phasing of an HCCI engine. While three of the four investigated renewable fuel components improve the energy efficiency when blended with the petroleum-based diesel, one renewable fuel component deteriorates energy efficiency. The effects of renewable fuel components on emissions vary, with some reducing emissions while others not having a clear trend affecting emissions.
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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".