Soot Emission Reduction from Post Injection Strategies in a High Pressure Direct-Injection Natural Gas Engine
Bibliographic record
Abstract
Compression ignition engines, including those that use natural gas as the major fuel, produce emissions of NOx and particulate matter (PM). Westport Inc. has developed the pilot-ignited high-pressure direct-injection (HPDI) natural gas engine system. Although HPDI engines produce less soot than comparable conventional diesel engines, further reductions in engine-out soot emissions is desired. In diesel engines, multiple injections can help reduce both NOx and PM. The effect of post injections on HPDI engines was not studied previously. The present research shows that late injection of a second gas pulse can significantly reduce PM and CO from HPDI engines without significantly increasing NOx or fuel consumption. In-cylinder pressure measurements were used to characterize the heat release resulting from the multiple injections. Experiments showed that most close-coupled split injection strategies provided no significant emissions benefit and less stable operation. However, post injection of 15-20% of the fuel 1.5-2.5 ms after the end of the first injection can reduce PM and CO by over 80%. Using this strategy has only a small effect on other emissions and fuel consumption. Methane emission is reduced about 25%, NOx changes is almost within the variability of results, maximum pressure of cylinder increases within 5bar and fuel consumption will increase about 1%. Based on the literature for diesel engines, we expect that enhanced mixing due to the second injection and increased local temperature within the cylinder may be major contributors to the soot reductions.
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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 | 0.001 | 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".