New Methodology for Aircraft Performance Model Identification for Flight Management System Applications
Bibliographic record
Abstract
This paper presents the validation results of a study conducted at the Laboratory of Applied Research in Actives Controls, Avionics, and Aeroservoelasticity to develop a modeling technique for determining a performance model of a particular aircraft using a limited amount of data. This technique was applied to the well-known business jet aircraft, Cessna Citation X. All the reference data used to design the model were generated using an in-house in-flight performance program. These data were subsequently combined with simplified flight mechanics equations in order to estimate various performance and aero-propulsive characteristics of the aircraft. An original identification algorithm was next developed in order to determine a mathematical model describing the fuel flow, as well as the aircraft thrust and drag aerodynamic coefficients. Validation of the study was accomplished by comparing trajectory data predicted by the model with trajectory data measured with a research aircraft flight simulator (RAFS) of the Cessna Citation X. The results show a very good agreement for the flight time, the ground distance traveled, and fuel consumption.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".