Fuel Injection Strategies to Improve Emissions and Efficiency of High Compression Ratio Diesel Engines
Bibliographic record
Abstract
Simultaneous low NOx (< 0.15 g/kWh) & soot (< 0.01 g/kWh) are attainable for enhanced premixed combustion that may lead to higher levels of hydrocarbons and carbon monoxide emissions as the engine cycles move to low temperature combustion, which is a departure from the ultra low hydrocarbon and carbon monoxide emissions, typical of the high compression ratio diesel engines. As a result, the fuel efficiency of such modes of combustion is also compromised (up to 5%). In this paper, advanced strategies for fuel injection are devised on a modern 4-cylinder common rail diesel engine modified for single cylinder research. Thermal efficiency comparisons are made between the low temperature combustion and the conventional diesel cycles. The fuel injection strategies include single injection with heavy EGR, and early multi-pulse fuel injection under low or medium engine loads respectively. The empirical studies have been conducted under independently controlled exhaust gas recirculation, intake boost, and exhaust backpressure. Multiple fuel injection pulses per cycle and heavy EGR have been applied to modulate the homogeneity history in order to improve the phasing and efficiency of the combustion process. The new low temperature combustion trade-off, that is, CO & THC vs. NOx & soot is presented in the context of the different fuel injection strategies.
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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".