New Adaptive Algorithm Development for Monitoring Aircraft Performance and Improving Flight Management System Predictions
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
To compute the most efficient route that the aircraft has to fly, the flight management system (FMS) needs a mathematical representation of the aircraft performance. However, after several years of operation, various factors can degrade the overall performance of the aircraft. Such degradation can affect the reliability of the aircraft model, and the crew would lose confidence in the fuel planning estimated by the FMS. This paper presents the results of a study in which a new adaptive algorithm is proposed for continuously updating the FMS performance model using cruise flight data. The proposed algorithm combines aircraft performance monitoring techniques with adaptive lookup tables to model the aerodynamic characteristics of the aircraft. The methodology was applied to the well-known Cessna Citation X business aircraft, for which a research aircraft flight simulator was available. The development of this methodology was accomplished by creating an initial performance model, adapting it using flight data in cruise, and finally comparing its prediction with a series of flight data collected with the flight simulator. Results have shown that the proposed methodology was able to reduce fuel flow prediction mean errors by about 5%, whereas the standard deviation was reduced by a factor of 3.4.
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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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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