Horizontal flight trajectory optimization considering RTA constraints
Bibliographic record
Abstract
The increasing of flights around the world has led to various problems for the aeronautical industry such as saturated air space and higher levels of fossil fuel consumption. The way in which en-route flights are handled should be improved in order to increase airways’ capacity. A solution is to make aircraft to arrive at specific waypoints at a time constraint called Required Time of Arrival (RTA). Fossil fuel brings as a consequence the release of polluting particles to the atmosphere such as carbon dioxide and nitrogen oxides. It is thus desirable to compute the most economical trajectory in terms of fuel burn while fulfilling the RTA constraint. This article proposes a horizontal reference trajectory optimization algorithm based on the Particle Swarm Optimization technique in order to reduce fuel burn while fulfilling the RTA constraint. Results showed that for a flight without RTA constraint, up to 4% of fuel can be saved comparing against the trajectory of reference. The algorithm was normally able to meet the RTA constrain. However, aggressive RTA constraints might reduce the optimization levels of fuel compared with flights without RTA constraint.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".