Correction for particle loss in a regulatory aviation nvPM emissions system using measured particle size
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it