Source Apportionment of Particulate Matter from a Diesel Pilot-Ignited Natural Gas Fuelled Heavy Duty DI Engine
Bibliographic record
Abstract
In recent years there has been a growing awareness that particulate matter, especially fine diesel particulate, is a health concern. This has stimulated research to develop new technologies to reduce particulate emissions without increasing nitrogen oxide (NOx) emissions or fuel consumption. Westport Innovations has developed a technology involving high pressure direct injection and combustion of natural gas for medium and heavy-duty engine platforms. At practical compression ratios, the natural gas will not auto-ignite, so a diesel pilot injection is used for ignition. Thus, the soot emissions can have contributions from the combustion of natural gas, diesel pilot, or lubricating oil. While the soot emissions with natural gas as the main fuel are significantly lower than in a conventional diesel engine, it remains important to determine where the soot is coming from to aid in emission reduction strategies. In this study, the contribution of the pilot fuel (a biodiesel blend with higher 14C content than diesel fuel) was determined using accelerator mass spectrometry (AMS) measurements of 14C in the exhaust particulate. Results indicate that the pilot fuel contribution to soot ranges from 4-40% over the tested operating conditions; correspondingly, the contribution by natural gas and lubricating oil combined ranges from 60-96%. The highest fraction of soot from the pilot source is at low load without exhaust gas recirculation. The lowest fraction of soot from the pilot source is at high load with exhaust gas recirculation, i.e. the conditions contributing most to mode-averaged emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".