A Usage-Based Analysis Method for Predicting Fleet Fuel Savings Due to Aircraft Improvements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High fuel prices and increasing budgetary pressures are renewing interest in improvements that increase the fuel efficiency of existing aircraft fleets. Proposed improvements range from devices that decrease aircraft drag to reductions in aircraft empty weight. To perform a proper cost-benefit analysis of any of these initiatives, an accurate assessment of the potential fleet-wide reduction in fuel consumption must be obtained. Traditional approaches analyze a limited set of mission profiles or operating conditions to determine the change to fuel burn due to improvements. However, historical, fleet-wide usage data provide greater insight into actual operational conditions and form a better basis for assessments. This paper introduces a method to apply usage data, in the form of cumulative annual time at a range of operating conditions, to evaluate fuel savings. Assumptions and limitations of this usagebased analysis (UBA) method are discussed and validated. A sample case is shown using micro-vanes, a drag-reduction device by Lockheed Martin for the C-130 tactical transport.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it