Vertical flight profile optimization for a cruise segment with RTA constraints
Bibliographic record
Abstract
ABSTRACT This paper presents the results of a research performed at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticty (LARCASE), at ÉTS, concerning optimisation strategies for cruise flight segments with imposed flight time (delimited by waypoints with required time of arrival constraints). Specifically, a new algorithm is presented that identifies the optimal vertical navigation profile (flight altitude and speed optimisation) for a cruise segment with imposed lateral navigation profile, bounded by two waypoints with required time of arrival constraints. The set of evaluated vertical navigation profiles are characterised by identical altitudes and speeds at their initial and final waypoints (at the beginning and the end of the cruise segment under optimisation), a maximum of one altitude step (relative to the initial altitude), and are flown at constant speed. This study investigates the flight performance increase (total cost reduction) for a flight along the optimal vertical navigation profile, relative to a flight at the optimal speed and initial cruise altitude. The evaluation was performed using a medium haul transport aircraft flight performance model, for three lateral navigation profiles and three wind profiles. The algorithm is targeted for Flight Management Systems platforms, to provide the optimal flight trajectory for the imposed lateral flight profile and time constraints.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".