Efficacy of EGR and Boost in Single-Injection Enabled Low Temperature Combustion
Bibliographic record
Abstract
Exhaust gas recirculation, fuel injection strategy and boost pressure are among the key enablers to attain low NOx and soot emissions simultaneously on modern diesel engines. In this work, the individual influence of these parameters on the emissions are investigated independently for engine loads up to 8 bar IMEP. A single-shot fuel injection strategy has been deployed to push the diesel cycle into low temperature combustion with EGR. The results indicated that NOx was a stronger respondent to injection pressure levels than to boost when the EGR ratio is relatively low. However, when the EGR level was sufficiently high, the NOx was virtually grounded and the effect of boost or injection pressure becomes irrelevant. Further tests indicated that a higher injection pressure lowered soot emissions across the EGR sweeps while the effect of boost on the soot reduction appeared significant only at higher soot levels. Moreover, the peak soot values were observed to shift towards lower intake oxygen values. With high levels of EGR, the increased carbon monoxide emission largely followed the NOx-Soot trade-off (reduced oxygen concentration, 11∼14% of the working fluid), while the increased hydrocarbon emission was attributed to both the lowered flame temperature and the further reduced oxygen concentration (8∼12%).
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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".