Implications of Sustainable Aviation Fuel for the Rotorcraft Industry
Bibliographic record
Abstract
International governments and the airline industry have set goals to progressively replace aviation fossil fuels with sustainable aviation fuel (SAF). SAF is presently less than 0.1 % of the global aviation fuel supply but projected to be greater than 50% by 2050. SAF is kerosene synthetically produced from sustainable agriculture and recycled waste. SAF has lower contrail-forming particulates and has substantial benefit of lifecycle CO2 reduction from using sustainable feedstocks rather than petroleum. The rotorcraft industry consumes less than 1% of the global aviation fuel supply and is not driving the transition to SAF but will need to have compatibility with SAF. The American Society of Testing and Materials (ASTM) has developed a process in conjunction with the FAA and original equipment manufacturers (OEMs) whereby SAF blends are approved as drop-in equivalent to ASTM D1655 Jet A/A1 fuel and can then be seamlessly distributed and utilized under existing aircraft approvals for Jet A/A1. SAF candidates are comprehensively evaluated by an OEM task group. Global aviation industry, certification agencies, and military are harmonizing around this approach. The FAA has encouraged rotorcraft manufacturers to monitor OEM task group proceedings and provide input for concerns to rotorcraft. A unique rotorcraft concern regarding suction lift fuel systems is explained as an example.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".