Measurement of Aircraft Engine Non-Volatile PM Emissions: Results of the Aviation-Particle Regulatory Instrumentation Demonstration Experiment (A-PRIDE) 4 Campaign
Bibliographic record
Abstract
This study reports the first of a kind data on aircraft engine non-volatile particulate matter (nvPM) number- and mass-based emissions using standardized systems. Two compliant sampling and measurement systems operated by Missouri University of Science and Technology (Missouri S&T) and Empa were evaluated during the Aviation - Particle Regulatory Instrumentation Demonstration Experiment (A-PRIDE) 4 campaign at the SR Technics facilities in Zürich, Switzerland, in November 2012. The Missouri S&T and Empa systems were compared during a series of dedicated engine tests using a CFM56-5B4/2P engine source, and maintenance engine testing using CFM56-7B24/3 and PW4168A engine sources at a range of engine operating conditions. These two compliant systems were found to agree within 6% of each other in terms of nvPM number-based emissions, and within 15% for nvPM mass-based emissions. For the three engine sources studied, at several engine power conditions the mass instruments approached their limit of detection, resulting in high measurement uncertainties. Ancillary instrumentation was used to determine PM size distributions, chemical composition, and effective density from mass-mobility experiments. Particle geometric mean mobility diameter ranged 20–45 nm, and geometric standard deviation varied from 1.55 to 1.9 for the three engine types studied. The fraction of PM organic content measured in the emissions from the CFM56-5B4/2P engine was ∼4% while the size-dependent particle effective density was parameterized with a mass-mobility exponent of 2.57 and a pre-factor of 0.606. Results of this study will contribute to the development of the new nvPM emissions certification standard and emissions inventories from commercial aviation operations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".