Bibliographic record
Abstract
Agile UAVs is a special class of fixed-wing aircraft characterized by high thrust-to-weight ratios (around 2 to 3) and big control surfaces (around 40 to 50% chord) with deflections as large as 50 degrees; and hence capable of extreme maneuvers and aerobatics. The intent of this paper is to model the aerodynamics of agile UAVs for real-time applications such as pilot-in-loop aircraft simulation, accounting for the unique geometry of the aerodynamic/control surfaces, high angles of attack encountered during maneuvers and aerobatics, and also unforeseen changes in the aerodynamics in the event of a crash/accident. Conventional modeling techniques, such as the stability derivatives approach, are not suitable due to the highly nonlinear nature of the aerodynamics and in particular because of the strong coupling of the aircraft states. Hence in the present work, a component breakdown approach is utilized to model agile UAV aerodynamics for the complete angle of attack range. Simplifications have made in the model to retain real-time functionality without losing much accuracy. For the purpose of validation, wind-tunnel testing is done for different angle of attack conditions including completely reversed flow. A good agreement between the simulation and experiments establishes the validity of the proposed approach and the aerodynamics model, with max. rms errors of ~0.15 N and 0.05 N.m in aerodynamic forces and moments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".