Bibliographic record
Abstract
In a search for improvements in airport and air ro utes operational capacity, an acceptable level of wake turbulence encounter could be considered – perhaps by comparing wake vortex encounter (WVE) upsets with discrete turbulent gust design requirements, for which transport aircraft are certificated, a priori. Such a study has been conducted. Analysis of wake vortex encounters from DAS, DFDR and QAR recorded flight data from research aircraft and commercial aircraft encountering inflight upsets from wake vortex encounters has been used successfully to estimate the detailed characteristics of the upset windfield normal, lateral and axial gust components, in body axes, experienced by the aircraft. The body axis gust components are correlated against the gross loads experienced by the WVE aircraft, in time-domain based Lissajous cross-plots. The cross-plots are compared with the design gust-loads estimated by the CAR 4b and early FAR 25 gust load-line design requirements, for 12.5 c ‘1-cosine’ discrete gusts. Comparisons are conducted for a range of WVE aircraft. Agreement with FAR 25 gust load-line estimations is generally quite good, although they disclose concurrent multi-axis gust experiences, with at least one-axis at exceedance magnitude, in the simultaneous presence of autopilot or human-pilot induced manoeuvre loads, whereas design gust requirements need not be applied simultaneously on more than one axis, nor in the presence of pilot-ind uced-manoeuvre loads. A digital FBW system aeroplane with gust alleviation is shown to effectively handle WVE loads.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".