Effect of Injection Strategies on Emissions from a Pilot-Ignited Direct-Injection Natural-Gas Engine- Part I: Late Post Injection
Bibliographic record
Abstract
High-pressure direct-injection (HPDI) in heavy duty engines allows a natural gas (NG) engine to maintain diesel-like performance while deriving most of its power from NG. A small diesel pilot injection (5-10% of the fuel energy) is used to ignite the direct injected gas jet. The NG burns in a predominantly non-premixed combustion mode which can produce particulate matter (PM). Here we study the effect of injection strategies on emissions from a HPDI engine in two parts. Part-I will investigates the effect of late post injection (LPI) and Part II will study the effect of slightly premixed combustion (SPC) on emission and engine performance. PM reductions and tradeoffs involved with gas late post-injections (LPI) was investigated in a single-cylinder version of a 6-cylinder,15 liter HPDI engine. The post injection contains 10-25% of total fuel mass, and occurs after the main combustion event. When timed appropriately, LPI results in significant PM reductions with only small effects on other emissions and engine performance. The morphology of particles produced by LPI is similar to that from conventional HPDI (and also from diesel), but the size and number concentration are reduced. Pulse Isolation experiments and reacting-flow computational fluid dynamics (CFD) modelling indicated that the main PM reduction from LPI comes from reducing the amount of fuel in the first injection, leading to lower PM formation in the main combustion event. The second injection makes an insignificant net contribution to the total PM.
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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 | 0.000 |
| 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".