Integration and Application of a Tool Chain for Environmental Analysis of Aircraft Flight Trajectories
Bibliographic record
Abstract
The German Aerospace Center (DLR) is currently developing a tool chain for the environmental analysis of aircraft flight trajectories. The presented tool chain consists of tools for aerodynamic analysis, engine cycle modelling, flight simulation, and aircraft noise prediction. The aircraft geometry is not modified within the process but provided as an input. The implemented tools come from specialized DLR institutes, are harmonized in input/output format and are integrated into one fully automated analysis process. The \nPHX ModelCenter framework allows for a DLR-wide accessible server/client architecture. \nThe new tool chain is applicable to evaluate arbitrary three dimensional flight trajectories. The focus of the presented work lies on the environmental analysis and optimization of \napproach and departure procedures. The predicted ground noise levels are compared to results from a dedicated DLR flyover noise campaign in 2009. A conventional approach, a \nsteep approach, and a new three dimensional approach procedure have been flight tested with DLR’s flying testbed ATTAS. The new procedure is referred to as Helical Noise Abatement Procedure (HeNAP) due to its helix shape. The ground noise measurements confirm the predicted noise concentration and relocation along the steep approach and the HeNAP compared to the conventional approach procedure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".