Correction for particle loss in a regulatory aviation nvPM emissions system using measured particle size
Bibliographic record
Abstract
To reduce the adverse impact of civil aviation on local air quality and human health, a new international standard for non-volatile Particulate Matter (nvPM) number and mass emissions was recently adopted. A system loss correction method, which accounts for the significant size-dependent particle loss, is also detailed to predict nvPM emissions representative of those at engine exit for emissions inventory purposes. As Particle-Size-Distribution (PSD) measurement is currently not prescribed, the existing loss correction method uses the nvPM number and mass measurements along with several assumptions to predict a PSD, resulting in significant uncertainty. Three new system loss correction methodologies using measured PSD were developed and compared with the existing regulatory method using certification-like nvPM data reported by the Swiss and European nvPM reference systems for thirty-two civil turbofan engines representative of the current fleet. Additionally, the PSD statistics of three sizing instruments typically used in these systems (SMPS, DMS500 and EEPS) were compared on a generic aero-engine combustor rig. General agreement between the three new PSD loss correction methods was observed, with both nvPM number- and mass-based system loss correction factors (kSL_num and kSL_mass) within ±10% reported across the engines tested. By comparison, the existing regulatory method was seen to underpredict kSL_num by up to 67% and overpredict kSL_mass by up to 49% when compared with the measured-PSD-based methods, typically driven by low nvPM mass concentrations and small particle size. In terms of the particle sizing instrument inter-comparison, an agreement of ±2 nm for the GMD and ±0.08 for the GSD was observed across a range of particle sizes on the combustor rig. However, it was seen that these differences can result in a 19% bias for kSL_num and 8% for kSL_mass for the measured-PSD-based methods, highlighting the need for further work towards the standardisation of PSD measurement for regulatory purposes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".