A Numerical Study of the Effect of Multi-Injection Strategy on NOx Reduction in DI Diesel Engines
Bibliographic record
Abstract
Diesel engines are becoming more and more popular as a power source for transportation and industry utility because of their better fuel efficiency over gasoline engines. At the same time, more and more stringent emission regulations on internal combustion engines have been used by governments all over the world. NOx emission control has become one of the biggest challenges in the design of Diesel engines. Previous experiments and simulations have shown that the multi-fuel-injection strategy can potentially reduce NOx emission in Diesel engines. In this study, more detailed numerical simulations have been conducted for up to 5 split fuel injections as compared to the conventional single fuel injection strategy to explore the effect of multi-fuel-injection on NOx reduction. KIVA-3V release 2, a multi-dimensional computational code employing combustion model, turbulence model, spray model and NOx production model, has been used in the numerical simulation. The combinations of multi-fuel-injection strategy with EGR (Exhaust Gas Recirculation) technique and multi-hole injectors are investigated as well. The results of this investigation have demonstrated that the use of the multi-fuel-injection strategy can effectively reduce the NOx emission in Diesel engines. Combined with other NOx reduction techniques, multi-fuel-injection strategy is a very promising way to make modern Diesel engines comply with the ever-stringent emission targets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".