Source Apportionment of Particulate Matter from a Diesel Pilot-Ignited Natural Gas Fuelled Heavy Duty DI Engine
Why this work is in the frame
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Bibliographic record
Abstract
<div class="htmlview paragraph">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 (NO<sub>x</sub>) emissions or fuel consumption.</div> <div class="htmlview paragraph">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 <sup>14</sup>C content than diesel fuel) was determined using accelerator mass spectrometry (AMS) measurements of <sup>14</sup>C in the exhaust particulate.</div> <div class="htmlview paragraph">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.</div>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it