Landing gear ground manoeuvre statistics from automatic dependent surveillance-broadcast transponder data
Bibliographic record
Abstract
ABSTRACT Landing gear are exposed to cyclic loads from the ground manoeuvres that aircraft perform in-service. Variability is observed in the loading magnitude associated with ground manoeuvres, along with the per-flight variability in ground manoeuvre occurrence and sequencing. Whilst loading magnitude variability has been widely characterised, significant assumptions are required regarding manoeuvre occurrence and sequencing when constructing landing gear load spectra for fatigue design. These assumptions are required due to the limited availability of data concerning ground manoeuvre occurrence and sequencing relating to aircraft in-service and require validation to facilitate the design of more efficient components. ‘Big-Data’ approaches, employing Automatic Dependent Surveillance-Broadcast (ADS-B) transponder data, enable aircraft ground tracks to be identified. This paper presents a methodology to characterise the variability in ground manoeuvre occurrence and sequencing using ADS-B data sourced from Flightradar24 ® for a wide-body aircraft fleet. Using statistics generated for the fleet, it was identified that significant variability exists in the occurrence and sequencing of turning and braking manoeuvres. The statistics also validate existing assumptions, including that the proportional share of left and right turning manoeuvres is equal. Finally, this paper discusses the utility of ADS-B datasets for constructing landing gear load spectra and monitoring of landing gear in-service.
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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) | 0.002 | 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 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".