Research on Optimization of Aircraft Climb Trajectory considering Environmental Impact
Bibliographic record
Abstract
Trajectory-based operation is a new technology that will be developed in the next generation of air traffic management. In order to clarify the optimization space of fuel consumption and emission impact on the environment under the specific operation limitation of air traffic management in the process of aircraft climb, an aircraft climb performance parameter optimization model considering the environmental impact is established. First, the horizontal and vertical climb models are established for the aircraft climb process, and then the optimization objectives are constructed by considering the impact of fuel consumption, exhaust emissions on air temperature, and the convenience of the flight process. Finally, the multiobjective model is solved by genetic algorithm. The B737-800 civil aviation aircraft is selected for simulation experiment to analyze the impact of speed change on the optimization target. The results show that with the change of speed, the fuel consumption and temperature rise are different, and the climb performance parameters of the aircraft are affected by the maximum RTA. By optimizing the flight parameters of the aircraft, it can effectively reduce the impact of flight on the environment and provide theoretical support for the sustainable development of civil aviation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".