Gaseous and Particle Emissions from a Turbo-Jet Engine Operating on Alternative Fuels at Simulated Altitudes
Bibliographic record
Abstract
Gaseous and particle emission assessments on a 1.15 kN-thrust turbojet engine were conducted at five altitudes in an altitude chamber with Jet A-1 fuel, pure Fischer Tropsch (FT), and two mixed fuels of JP-8 with FT or Camelina-based hydro-processed jet fuels. In general, lower emissions in CO₂, NOx, and particle number as well as higher emissions in CO and THC were observed at higher altitudes compared to lower altitudes. These observations, which were similar for all test fuels, were attributed to the reduced combustion efficiency and temperature at higher altitudes. The use of alternative fuels resulted in lower CO₂ emissions, ranging from 0.7% to 1.7% for 50% to 100% synthetic fuel in the fuel mixture at various altitudes. In terms of CO, the use of 100% FT fuel resulted in CO reduction up to 9.7% at 1525 m altitude and up to 5.9% at 9145 m altitude. Significant reduction in particle diameter, number and mass emission rates were observed with the use of alternative fuels due to the low aromatic and sulfur content in the fuels. Higher reductions were observed for increasing percentage of the alternative synthetic fuel in the fuel mixture. With the use of pure FT fuel, up to 80% and 96% reductions in particle number emissions were observed at 1525 m and 9145 m altitudes, respectively. In comparison, a larger particle reduction benefit was observed for the Camelina-based hydro-processed jet fuel than for the FT fuel.
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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".