Flight Altitude Optimization Using Genetic Algorithms Considering Climb and Descent Costs in Cruise with Flight Plan Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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