Four- and Three-Dimensional Aircraft Reference Trajectory Optimization Inspired by Ant Colony Optimization
Why this work is in the frame
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Bibliographic record
Abstract
A methodology of aircraft reference trajectory optimization inspired by the ant colony optimization is used in this paper to find the most efficient trajectory in terms of fuel burn and flight cost during the cruise phase. Weather conditions are taken into account in computing the most economical trajectory. The algorithm is designed in two consecutive stages. First, the reference trajectory is optimized in three dimensions. Then, the most economical combination of Mach numbers that fulfills the required time of arrival constraint over that three-dimensional trajectory is found, creating a four-dimensional reference trajectory. Different simulation tests consisted of trajectories following a fixed altitude geodesic trajectory, as well as of real as-flown flights. Simulations revealed that the ant colony algorithm was able to find the most efficient trajectory and the flight cost was 6.82% more economical than the geodesic reference trajectory. Moreover, tests showed that the ant colony algorithm was able to find a four-dimensional trajectory close to the real flight plan trajectory, providing an optimization average of 0.91%. Studies showed that making a three-dimensional trajectory fulfilling the required time of arrival constraint led to an important loss of 2.4% of optimization due to the Mach number changes.
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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 | 0.001 | 0.004 |
| 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