Particle Swarm Optimization with Required Time of Arrival Constraint for Aircraft Trajectory
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
<div>Global warming has motivated the aeronautical industry to develop new technologies that will reduce polluting emissions. A direct way to achieve this goal is to reduce fuel consumption. Reference trajectory optimization contributes to this goal by guiding aircraft to zones where meteorological conditions are favorable to execute their required missions and thereby to reduce flight costs. In this article, the reference trajectory was optimized in terms of geographical position, altitude, and speed, by taking into account a Required Time of Arrival (RTA) constraint and weather conditions. The algorithm assumes that there is no traffic and that the aircraft can fly anywhere in the search space. The search space was modeled in the form of a unidirectional weighted graph, fuel burn was computed using a numerical model, and the weather forecast was taken into account. The methodology utilized in this article to determine the most economical combinations of parameters that delivered the optimal trajectory was inspired by the Particle Swarm Optimization (PSO) algorithm. Results showed that the algorithm provided acceptable solutions under traffic management constraints. It was observed that the developed algorithm was able to save up to 9.1% (6,800 kg) of fuel burn when there was no RTA constraint for flight trajectories and up to 1.8% (600 kg) of fuel against real, as-flown trajectories with an RTA constraint of ±30 seconds. Because of the nature of the PSO Algorithm, the local best trajectories are extracted and provided as a Trajectory Option Set (TOS), which is similar in cost as the optimal trajectory.</div>
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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 | 0.000 |
| 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