Immediate Impacts on Particulate and Gaseous Emissions from a T56 Turbo-Prop Engine Using a Biofuel Blend
Bibliographic record
Abstract
Adoption of hydro-processed esters and fatty acid biojet fuels is a critical component for the sustainability of the aviation industry. Aviation biofuels reduce pollution and provide alternatives to conventional fossil fuels. A study of the impacts of biofuels on emissions from a T56 turbo-prop engine was undertaken as a joint effort among several departments of the Government of Canada. In this study, particulate (including particle number and black carbon (BC) mass) and regulated gaseous emissions (CO2, CO, NO, NO2, THC) were characterized with the engine operating on conventional F-34 jet fuel and jet fuel blended with camelina-based hydro-processed biojet fuel (C-HEFA) by 50% in volume. Emissions characterization, conducted after 20-hour ground engine durability tests, showed immediate significant reductions in particle number and BC mass when the engine was operated on the C-HEFA blend. Operating at high speed ground idle (HSGI) using the C-HEFA blend reduced particle number and BC mass emissions by 31% and 50%, respectively. At Take-off engine mode, particle number and BC mass reductions were 22% and 32%, respectively. In addition, particles generated from the C-HEFA blend were also observed to be slightly smaller than those generated from the conventional jet fuel by 2-4 nanometer (nm). CO emissions from the C-HEFA blend were lower by 4-6%. CO2 emissions from the C-HEFA blend were lower by approximately 1%, consistent with the difference in hydrocarbon ratios in the C-HEFA blend.
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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".