Methodology for Vertical-Navigation Flight-Trajectory Cost Calculation Using a Performance Database
Bibliographic record
Abstract
Trajectory optimization has been identified as an important way to reduce flight costs and polluting emissions. Due to the power capacity limitations in airborne devices such as the flight management system, a fast method should be implemented to calculate the full trajectory cost. Many flight management systems use a set of lookup tables with experimental data for each flight phase, and they are called performance databases. In this paper, the trajectory flight cost is calculated using a performance database instead of using classical equations of motion. The trajectory to be calculated is composed of climb, acceleration, cruise, descent, and deceleration. The influence of the crossover altitude during climb and descent, as well as step climbs in cruise, was considered. Lagrange linear interpolations were performed within the performance database discrete values to calculate the required values. By providing a takeoff weight, the initial and final coordinates, and the desired flight plan, the trajectory model provides the top-of-climb coordinates, the top-of-descent coordinates, the fuel burned, and the flight time needed to follow the given flight plan. The accuracy of the trajectory costs calculated with the proposed method was validated with an aerodynamic model in FlightSIM®, which is software developed by Presagis®, and with the trajectory cost given by the flight management system benchmark of reference. Results showed that, for the same reference trajectories and for the same inputs, the cost computed by the method proposed in this paper is close to the costs provided by FlightSIM and by the flight management system benchmark or reference.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".