Modèle mathématique d’optimisation non-linéaire du bruit des avions commerciaux en approche sous contrainte énergétique
Bibliographic record
Abstract
This thesis develops an mathematical non-linear optimization model of flight paths of two aircraft in approach minimizing the perceived noise on the ground while energetic constraint is considered. This is an analytical model of non-linear and non-convex optimal control governed by a system of ordinary differential equations resulting from the dynamics of flight and with their associated constraints. Our contribution focuses on the mathematical modeling equations, optimization and algorithmic programming of an acoustic non-linear optimization model of two aircraft simultaneously on approach. The addressed issues are the mathematical development of the «correct» 3D model, their flight dynamics, the mathematical modeling of the optimal control of dynamic system, the consideration of fuel consumption by aircraft as a differential equation with a consumption function specific variable depending on the evolution of their dynamics, the mathematical modeling of the instantaneous objective function representing the overall noise of the two approaching aircraft. Resolution deals with the direct method of sequential quadratic programming with confidence regions while AMPL programming language and KNITRO are considered. An indirect method was applied under the Pontryagin maximum principle, followed by a Runge-Kutta symplectic partitioned discretization to demonstrate the commutation between the direct approach and indirect approach. The expected results confirm optimal trajectories reducing ground noise and fuel consumption of two aircraft
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".