Research on the Combination Optimization Method of Aircraft Climb Parameters Considering the Influence of Pollution Emissions
Bibliographic record
Abstract
In order to find the optimum climb parameters for decreasing aircraft pollution emissions, we establish a calculation model of aircraft pollutant emissions to thoroughly examine the economy of aircraft operation and its impact on the environment, based on the International Civil Aviation Organization reference emission data and Boeing Method 2 which can calculate pollutant emissions in different flight phases by correcting the ICAO emission data. Firstly, we propose the concept of integrated flight cost considering the effect of emissions and establish its calculation model. Secondly, we establish the climb speed optimization models based on the traditional flight cost and integrated flight cost. Then, we analyze the effect of climb performance on the integrated flight cost. Next, we establish a combined optimization model of indicated airspeed and thrust using the genetic algorithm. The model simulation and result analysis are carried out using a wide-body aircraft. Finally, we analyze the effect of the optimization parameters and the sensitivity of the pollution index. The results show that the decrease in the flight speed and thrust effectively reduces the cost of pollutant emission during flight. The combination of (Vc, Tr) with the smallest integrated flight cost reduces the integrated flight cost by 1.28% and emission cost by 4.56%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".