Experimental verification of principal losses in a regulatory particulate matter emissions sampling system for aircraft turbine engines
Bibliographic record
Abstract
A sampling system for measuring emissions of nonvolatile particulate matter (nvPM) from aircraft gas turbine engines has been developed to replace the use of smoke number and is used for international regulatory purposes. This sampling system can be up to 35 m in length. The sampling system length in addition to the volatile particle remover (VPR) and other sampling system components lead to substantial particle losses, which are a function of the particle size distribution, ranging from 50 to 90% for particle number concentrations and 10-50% for particle mass concentrations. The particle size distribution is dependent on engine technology, operating point, and fuel composition. Any nvPM emissions measurement bias caused by the sampling system will lead to unrepresentative emissions measurements which limit the method as a universal metric. Hence, a method to estimate size dependent sampling system losses using the system parameters and the measured mass and number concentrations was also developed (SAE 2017; SAE 2019). An assessment of the particle losses in two principal components used in ARP6481 (SAE 2019) was conducted during the VAriable Response In Aircraft nvPM Testing (VARIAnT) 2 campaign. Measurements were made on the 25-meter sample line portion of the system using multiple, well characterized particle sizing instruments to obtain the penetration efficiencies. An agreement of ± 15% was obtained between the measured and the ARP6481 method penetrations for the 25-meter sample line portion of the system. Measurements of VPR penetration efficiency were also made to verify its performance for aviation nvPM number. The research also demonstrated the difficulty of making system loss measurements and substantiates the E-31 decision to predict rather than measure system losses.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".