Particulate Matter Emission Characterization From a Natural Gas Fuelled High Pressure Direct Injection Engine
Bibliographic record
Abstract
High-Pressure Direct-Injection (HPDI) combustion of Natural Gas can reduce the gaseous and Particulate Matter (PM) emissions compared to a conventional diesel engine. Upcoming EPA and EURO emission limits may restrict particle number as well as particle mass. In preparation for these upcoming limits, the PM mass, size and composition was studied from a heavy-duty Cummins ISX engine converted to HPDI operation. To characterize the PM emissions, tests were based around a mid-speed, high-load operating point. Injection timing, equivalence ratio, gas supply pressure, EGR % and diesel injection mass were isolated and varied. PM emissions were characterized by the mobility size distribution, light scattering and filter loading. In addition a novel thermodenuder was used to determine the PM volatile fraction. It was found that EQR and EGR have the greatest effect on PM mass emissions and the correlations between these parameters are evaluated. The mean particles size and number concentrations are again most effected by EGR and EQR with smaller effects from the GRP and diesel pilot. The size distributions of the parameter variations are similar and there are no nucleation mode ultrafine particles observed. The volatile fraction is fairly constant across the parameter variations and is found to be around 18% of the mass and 11% by number of particles at this high load condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".