Characterization of the Ultrafine and Black Carbon Emissions from Different Aviation Alternative Fuels
Bibliographic record
Abstract
This study reports gaseous and particle (ultrafine and black carbon (BC)) emissions from a turbofan engine core on standard Jet A-1 and three alternative fuels, including 100% hydrothermolysis synthetic kerosene with aromatics (CH-SKA), 50% Hydro-processed Esters and Fatty Acid paraffinic kerosene (HEFA-SPK), and 100% Fischer Tropsch (FT-SPK). Gaseous emissions from this engine for various fuels were similar but significant differences in particle emissions were observed. During the idle condition, it was observed that the non-refractory mass fraction in the emitted particles were higher than during higher engine load condition. This observation is consistent for all test fuels. The 100% CH-SKA fuel was found to have noticeable reductions in BC emissions when compared to Jet A-1 by 28-38% by different BC instruments (and 7% in refractory particle number (PN) emissions) at take-off condition. BC emissions from this fuel were lower than from Jet A-1 by 45-50% (and 25-26% in refractory PN) at idle or cruise condition. The 100% CH-SKA fuel was observed to have a minimum influence on non-refractory PN emissions. A lower volume in naphthalene in the 100% CH-SKA fuel was hypothesized to be one of the factors attributing to the reduced BC emissions when compared to Jet A-1 emissions. For the 50% HEFA-SPK fuel, BC emissions were lower than the BC emissions from Jet A-1 by 58-86% for various engine load conditions. BC emissions from the 100% FT-SPK fuel were lower than from the Jet A-1 by 70-98%. Both the refractory and non-refractory PN emissions from these fuels were lower by comparable magnitude when compared to that from Jet A-1.
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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".