Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments
Bibliographic record
Abstract
This paper presents a new method for the geometrical construction of an optimal vertical flight plan associated to a provided lateral flight plan defined as a succession of waypoints characterized by their along-the-track distance relative to the first waypoint and their constraints. The principal objective of the proposed method is the minimization of the total number of vertical flight plan segments, whose slope values closest match the values set for their corresponding flight phase and altitude. The main advantage of the proposed method is that it constructs the optimized vertical flight plan employing faster—and less-intensive computations than methods based solely on aircraft performance models. Also, the proposed algorithm has the advantage of generating ground-fixed predicted vertical flight plans which, when flown, are less sensitive to varying wind conditions, thus, smaller trajectory deviations than those computed using solely the model-based algorithms. Two implementations corresponding to different trade-offs between conflicting preferred gradient and minimal segment length constraints were compared. The results show that a vertical flight path segment’s construction and resulting configuration is dependent on the configuration of the vertical flight plan segments that precede it. The results also show that for a majority of the test cases, the resulting flight plans computed using the two implementations were identical. Moreover, even when the flight plans were not completely identical, many of the corresponding segments were identical.
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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 | 0.000 |
| 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 | 0.000 | 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".