Improving the BADA 3 aerodynamic database for trajectory optimization
Bibliographic record
Abstract
Although commercial aviation has developed strongly during recent years, the optimization of trajectories is still a challenge due to security, environmental and procedural restrictions. In order optimize an aircraft trajectory, and therefore, to model it, first of all, it is required to study the aircraft performance. The aircraft performance model is given by the basic kinematic and dynamic equations, although it is also necessary to know the aircraft aerodynamic model, i.e. its aerodynamic coefficients. Aware of this challenge, EUROCONTROL has developed an aircraft performance model called BADA (Base of Aircraft Data) that contains the aircraft performance model for a large percentage of today's commercial aircraft. This tool has been designed by EUROCONTROL for their own research projects but has finally made available for the R& D collective. The BADA model consists on a set of theoretical concepts in the form of generic polynomials used to calculate the aircraft performance. It also comes with a set of individual data sets for each plane to particularize these polynomials. But still, it remains a generic model, and therefore, it is not really accurate for what the trajectory optimization processes require. This thesis proposes a methodology based on the kinetic approximation of the aircraft performance model and an improvement of BADA by using a software called United States Air Force and Stability Control Digital DATCOM. DATCOM is a software that implements calculation methods of aerodynamic stability and control developed in 1960 by the US Air Force. After developing the model of two common long-haul aircraft, it will be compared with BADA model and a model developed by Ms. Caroline Dietrich, master student who worked as a researcher at Ecole Polytechnique de Montreal. This comparison is performed by modeling the trajectory of a Boeing 767-300ER flight from Toronto to Los Angeles.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".