Effect of fuel composition on properties of particles emitted from a diesel–natural gas dual fuel engine
Bibliographic record
Abstract
The effective density and mixing state of particles emitted from a natural gas–diesel dual fuel engine are investigated. Measurements were conducted at three different fuel compositions including 100% diesel fuel (0% NG), 75% diesel–25% natural gas (25% NG) and 50% diesel–50% NG (50% NG). The particle effective density was measured using a differential mobility analyzer in series with a centrifugal particle mass analyzer. A catalytic stripper at 350 °C was employed upstream of the centrifugal particle mass analyzer in order to remove the semi-volatile material from the solid particles to measure the effective density of non-volatile particles as well as the particle mixing state. A scanning mobility particle sizer was used to measure the particle size distribution. The particle mass concentration was also measured using several techniques including cavity-attenuated phase-shift particulate matter single-scattering albedo, laser-induced incandescence, thermal-optical analysis, photoacoustic spectroscopy, and integrated particle size distribution. The semi-volatile number and mass fractions are found to be lower than 15%. The effective density functions of particles at 0% and 25% NG are within 6% of each other; however, the effective density values of particles at 50% NG are lower than those of the 0% NG by up to 35%. The mass-mobility exponent varies in the range of 2.42–2.51 and 2.38–2.54 for undenuded and denuded particles, respectively. For the mass concentration measurements, photoacoustic spectroscopy agrees with thermal-optical analysis within 5%, while all the other techniques measure up to 50% deviations relative to thermal-optical analysis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".