Data Mining and Statistical Modeling for Flight Test Applications in E-VTOL Aircraft
Bibliographic record
Abstract
Aircraft flight testing is an integral part of aircraft design and development.Flight testing serves as a method to expand and verify the aircraft flight envelope, as well as demonstrate adequate performance, stability, and systems compliance.It is a requirement of aircraft certification authorities to demonstrate aircraft safety and operability through the means of flight test.In the past two decades, data collected during flight test has more than tripled as aircraft systems became more complex and sensor technology improved.In addition to the increased data collected per flight test hour, an aircraft program can require thousands of flight test hours before aircraft entry into service.Coupled with the associated high cost, the need for reliable classification and information mining from flight test data is apparent.This thesis will use data mining and statistical modelling techniques for the application of flight test data analysis in the context of designing an electric Vertical Takeoff or Landing (e-VTOL) aircraft.E-VTOL is a design concept that aims to improve the way that passengers and cargo traverse short distances.This concept leverages electric propulsion and combines the vertical takeoff and landing capabilities of rotorcraft with the forward flight efficiency of conventional aircraft.This thesis presents methods to solve part of the challenges that arise from flight testing this novel aircraft design.Contents v 3.8.2Additional Considerations . . .
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".