Impact of Fuel Properties on Diesel Low Temperature Combustion
Bibliographic record
Abstract
Extensive empirical work indicates that exhaust gas recirculation (EGR) is effective to lower the flame temperature and thus the oxides of nitrogen (NOx) production in-cylinder in diesel engines. Soot emissions are reduced in-cylinder by improved fuel/air mixing. As engine load increases, higher levels of intake boost and fuel injection pressure are required to suppress soot production. The high EGR and improved fuel/air mixing is then critical to enable low temperature combustion (LTC) processes. The paper explores the properties of the Fuels for Advanced Combustion Engines (FACE) Diesel, which are statistically designed to examine fuel effects, on a 0.75L single cylinder engine across the full range of load, spanning up to 15 bar IMEP. The lower cetane number (CN) of the diesel fuel improved the mixing process by prolonging the ignition delay and the mixing duration leading to substantial reduction of soot at low to medium loads, improving the trade-off between NOx and soot. Low load performance, such as cycle-to-cycle variability, was worsened by less effective post flames and the less reactive nature of the mixture. The higher volatility of the lower T90 fuels promoted the evaporation of the injected fuel spray, thereby accelerating the fuel/air mixing process and improving the homogeneity of cylinder charge. The T90 effect on ignition delay is minor, yet the improved vaporization and mixing translated in a noticeable soot reduction. The higher aromatic contents in the diesel fuels prolonged the ignition delay. However, the soot emissions were not necessarily reduced according to the extended ignition delay. The soot trend competed with the aromatics, which are precursors of soot.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".