Characterization of Fuel Composition and Altitude Impact on Gaseous and Particle Emissions From a Turbojet Engine
Bibliographic record
Abstract
The effects of altitude and fuel composition on gaseous and particle emissions from a turbojet engine were investigated as part of the National Jet Fuels Combustion Program (NJFCP) effort. Two conventional petroleum based jet fuels (a “nominal” and a “worst-case” jet fuel) and two test fuels with unique characteristics were selected for this study. The “worst-case” conventional jet fuel with high flash point and viscosity resulted in reduced combustion efficiency supported by the reduced CO2 emissions and corresponding increased CO and THC emissions. In addition, increased particle number (PN), particle mass (PM), and black carbon (BC) emissions were observed. Operating the engine on a bimodal fuel, composed of heavily branched C12 and C16 iso-paraffinic hydrocarbons with an extremely low cetane number did not significantly impact the engine performance or gaseous emissions but significantly reduced PN, PM, and BC emissions when compared to other fuels. The higher aromatic content and lower hydrogen content in the C-5 fuel were observed to increase PN, PM, and BC emissions. It is also evident that the type of aromatic hydrocarbons has a large impact on BC emissions. Reduction in combustion efficiency resulted in reduced CO2 emissions and increased CO and THC emissions from this engine with increasing altitudes. PN emissions were moderately influenced by altitude but PM and BC emissions were significantly reduced with increasing altitude. The reduced BC emissions with increasing altitude could be a result of reduced combustion temperature which lowered the rate of pyrolysis for BC formation, which is supported by the NOx reduction trend.
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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) | 0.000 | 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 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".