Characterization of Emissions From the Use of Alternative Aviation Fuels
Bibliographic record
Abstract
Alternative fuels for aviation are now a reality. These fuels not only reduce reliance on conventional petroleum-based fuels as the primary propulsion source, but also offer promise for environmental sustainability. While these alternative fuels meet the aviation fuels standards and their overall properties resemble those of the conventional fuel, they are expected to demonstrate different exhaust emissions characteristics because of the inherent variations in their chemical composition resulting from the variations involved in the processing of these fuels. This paper presents the results of back-to-back comparison of emissions characterization tests that were performed using three alternative aviation fuels in a GE CF-700-2D-2 engine core. The fuels used were an unblended synthetic kerosene fuel with aromatics (SKA), an unblended Fischer Tropsch synthetic paraffinic kerosene (SPK) and a semi-synthetic 50-50 blend of Jet A-1 and hydroprocessed SPK. Results indicate that while there is little dissimilarity in the gaseous emissions profiles from these alternative fuels, there is however a significant difference in the particulate matter emissions from these fuels. These differences are primarily attributed to the variations in the aromatic and hydrogen contents in the fuels with some contributions from the hydrogen-to-carbon ratio of the fuels.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".