Aircraft Vertical Route Optimization Deterministic Algorithm for a Flight Management System
Bibliographic record
Abstract
This paper describes an optimization algorithm that provides an economical Vertical Navigation profile plan by finding the combinations of climb, cruise and descent speeds, as well as the altitudes for an aircraft to minimize flight costs. The computational algorithm profits from a space search reduction algorithm to reduce the initial number of speed and altitude combinations. Additional search space reductions were performed with the implementation of the branch and cut algorithm. A bounding function that correctly estimates the flight cost considering step climbs was developed to reduce the number of calculations. The full flight fuel burn cost was obtained using a performance database- based method. The fuel flight cost was computed using the cost index. This algorithm used a performance database instead of equations of motion to compute fuel burn. This database was developed and validated by our industrial partner using real flight experimental data. To validate the algorithm, its results were compared against three different algorithms: an “exhaustive search algorithm”, “Branch and Cut” and “Search Space Reduction Algorithm”. The solution provided by the algorithm was also compared to the solution provided by the commercial flight management system used for this study. These comparisons proved that the developed algorithm systematically found the optimal solution, and these solutions were often significantly better than those provided by a commercial flight management system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".