Comparison of Particle Number Emissions from In-Flight Aircraft Fueled with Jet A1, JP-5 and an Alcohol-to-Jet Fuel Blend
Bibliographic record
Abstract
The aviation sector has begun to adopt alternative fuels in an effort to reduce net greenhouse gas (GHG) emissions and reduce their impact on climate change. While many lab and flight-based studies have been completed for hydro-treated esters and fatty acids (HEFA) and Fischer–Tropsch (FT) alternative fuels, only one lab study has been conducted on alcohol-to-jet synthetic paraffinic kerosene (ATJ-SPK) fuels. Here we report results from the Civil Aviation Alternate Fuels Contrails and Emissions with high blend Biojet (CAAFCEB) project which was conducted in order to gather in-flight emissions data and compare an ethanol-based ATJ-SPK fuel blend and conventional JP-5 fuel to conventional Jet A1 fuel. A research aircraft was flown while fueled with the different fuels and in-flight cruise measurements were made by a second research aircraft gathering emissions and contrail data. In this study we report particle number emission index ratios with effective cutoff diameters of 15 and 7.7 nm for total particles and 13 nm for nonvolatile particles for GE CF700-2D2 engines at cruise. The ATJ-SPK blend was found to significantly reduce total and nonvolatile particle number emissions by up to 97% compared to Jet A1 fuel, likely due to the much lower aromatic and sulfur content and higher hydrogen content of the fuel. On the other hand, the total particle emissions for the JP-5 were found to have been 4% smaller than for Jet A1; this small difference is likely due to the similar fuel compositions.
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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 | 0.000 | 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".