Benchmarking data from the experience gained in engine performance and emissions testing on alternative fuels for aviation
Bibliographic record
Abstract
Abstract Alternative fuel for aviation has been the centre of serious focus for the last decade, owing mostly to the challenges posed by the price of conventional petroleum fuel, energy security and environmental concerns. The downslide in the oil prices in the recent months and the fact that energy security is not considered a major threat in commercial aviation, these factors have worked negatively for the promotion of alternative fuels. However, the continuous commitment to environmental stewardship by Governments and the industry have kept the momentum going towards the transparent integration of renewable alternatives in the aviation market. On the regulatory side, much progress have been made in the same timeframe with five alternative fuels being certified as synthetic blending components for aviation turbine fuels for use in civil aircraft and engines. Another seven alternative fuels are in the various stages of certification protocol. This progress has been made possible because of the extensive performance testing, both at full engine conditions and at engine components level. This article presents the results of engine performance and air pollutant emissions measurements gathered from the alternative fuels qualification testing conducted at the National Research Council Canada over the last seven years. This benchmarking data was collected on various engine platforms at full engine operation at sea level and/or altitude conditions using a variety of aviation alternative fuels and their blends. In order to provide a reference comparison basis, the results collected using the alternative fuels are compared with baseline Jet-A1 or JP-8 conventional fuels.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".