Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information
Bibliographic record
Abstract
Flight trajectory optimization algorithms reduce flight cost and fuel consumption, thereby reducing the polluting emissions released to the atmosphere. Ground teams and avionics equipment such as the Flight Management System evaluate different routes to minimize flight costs. The optimal trajectory represents the flight plan given to the crew. The resulting flight plan contains waypoints and weather information such as the wind speed and direction and the temperature for each waypoint. The flight plan is normally introduced manually into the Flight Management System. In this paper, genetic algorithms were applied to the waypoints available in a flight plan to find the altitudes that minimize total fuel consumption, taking into account the cruise-climb and cruise-descent steps' costs. The genetic algorithms emulate the evolution process through a predefined number of generations. Here, an individual is defined as a set of altitudes, whose fitness depends on its ability to improve the flight cost. The most-fitted individuals are selected to reproduce and create a new generation of individuals. As new generations are created, the fitness of the individuals improves and an optimal set of altitudes to reduce the flight cost is found. Aircraft fuel consumption in this algorithm was computed using a Performance Database, which was developed and validated by our industrial partner using experimental flight data. This approach differs from the Equations of Motion commonly used in the field and in the literature. Preliminary results showed that the set of altitudes provided by the genetic algorithm reduces the flight cost. This fuel reduction has a direct impact on the level of polluting emissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".