Vertical Navigation Trajectory Optimization Algorithm For A Commercial Aircraft
Bibliographic record
Abstract
Flight trajectory optimization is an alternative to reduce flight costs and contaminant emissions generated by fuel consumption. The objective of this work is to develop an algorithm to find the most economical vertical navigation profile between two points. The global flight cost analyzed is a compromise between fuel burned and flight time. This compromise is achieved using a variable called cost index, which assigns a cost to flight time in terms of fuel consumption. The optimization is performed by calculating a candidate cruise trajectory profile using an aircraft performance database. This candidate cruise profile reduces the search space, as only those profiles around the optimal candidate one are analyzed in terms of their account climb and descent costs. During cruise, step climbs are evaluated at every hour of flight. The different profiles are compared and the most economical one is defined as the optimal vertical navigation. The algorithm was evaluated for a commercial aircraft using the same performance database as a currently operational Flight Management System. The algorithm was developed in MATLAB, and its validation was performed using a complete aerodynamic model in the software FlightSIM developed by Presagis and the profiles generated by the Part Task Trainer of a commercial Flight Management System.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".