Application of the weight function method on a high incidence research aircraft model
Bibliographic record
Abstract
Abstract This paper assesses the application of a new method for system stability analysis, the weight functions method, to the longitudinal and lateral motions of a High Incidence Research Aircraft Model. The method consists of finding the number of weight functions that is equal to the number of differential equations required for system modelling. The aircraft’s stability is determined from the sign of the total weight function; which should be negative for a stable model. The Aero-Data Model In Research Environment (ADMIRE) simulation, developed by the Swedish Defence Research Agency, was used for the aerodynamic aircraft modelling, with the following configurations: Mach number = 0·25, altitude = 500m, angle-of-attack [–10 to 30]°, elevon deflection angle [–30 to 30]°, canard deflection [0° and 25°] and rudder deflection angles [–30° and 30°]. These flight configurations were selected because they are among the flight conditions for Cat. II Pilot Induced Oscillation (PIO) criteria validation, performed on the FOI aircraft model presented in the PIO Handbook by the Group for Aeronautical Research and Technology in Europe, Flight Mechanics/Action Group 12. This aircraft model has a known instability for longitudinal and lateral motions and so a control law was introduced to stabilise its flight.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".