Application of a Hygroscopicity Tandem Differential MobilityAnalyzer for characterizing PM Emissions in exhaust plumes from anAircraft Engine burning Conventional and Alternative fuels
Bibliographic record
Abstract
Abstract. In the last several decades, significant efforts have been directed toward better understanding the gaseous and Particulate Matter (PM) emissions from aircraft gas turbine engines. However, limited information is available on the hygroscopic properties of aircraft engine soot particles, in particular their soluble mass fraction (SMF). This parameter plays an important role in the water absorption, airborne lifetime, obscuring effect, and detrimental health effects of these particles. This study reports the description, detailed lab-based performance evaluation of a robust Hygroscopic-Tandem Differential Mobility Analyzer (H-TDMA) and subsequent field deployment to measure the SMF of aircraft engine soot particles in the exhaust from CFM56-2C1 engines burning several fuels during the Alternative Aviation Fuel EXperiment (AAFEX) II campaign. The fuels used were a conventional JP-8, tallow-based hydro-processed esters and fatty acids (HEFA), Fischer-Tropsch, a blend of HEFA and JP-8, and Fischer-Tropsch doped with Tetrahydrothiophene (an organosulfur compound). In all cases the SMF was observed to increase with fuel sulfur content and engine power condition. SMF decreseased with increasing particle size. The highest SMFs (~ 80 %) were found in the smallest particles, typically those with diameters of 10 nm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".