Sea Level Performance of a CF-700 Engine Core With Alternative Fuels
Bibliographic record
Abstract
The use of alternative fuels has the potential to enhance energy independence and reduce environmental impact of air travel. It is important to characterize gas turbine operation using such fuels under controlled conditions before implementing them in flight. The performance of a CF-700 engine core was examined in a sea level test facility. The following fuels were tested and will be reported on: 1. Jet A-1 – baseline fuel 2. 100% unblended Hydroprocessed Esters and Fatty Acids (HEFA) synthetic kerosene fuel with aromatics (SKA) 3. 100% unblended Fischer-Tropsch (FT) synthetic paraffinic kerosene (SPK) 4. Blended 50% HEFA-SPK and 50% Jet A-1 Fuel 2 above is an alternative fuel that can potentially be used without blending with conventional fuel. One purpose of the static engine testing was to determine if this fuel was suitable for use on subsequent test flights in a Dassault Falcon 20 aircraft. Engine performance testing was conducted at various power settings for each fuel. Relevant plots of performance are presented, compared and discussed. Transient tests were also performed including slams and chops. Observations of the effects of the different fuels on the engine fuel system are presented as some alternative fuels have the potential to cause seals to shrink and leaks to occur. The leaks observed are noted as are the steps taken to mitigate the problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".