Design and verification of a smart wing for an extreme-agility micro-air-vehicle
Bibliographic record
Abstract
A special class of fixed-wing micro-air-vehicle (MAV) is currently being designed to fly and hover to provide range superiority as well as being able to hover through a flight maneuver known as prop-hanging to accomplish a variety of surveillance missions. The hover maneuver requires roll control of the wing through differential aileron deflection but a conventional system contributes significantly to the gross weight and complexity of a MAV. Therefore, it is advantageous to use smart structure approaches with active materials to design a lightweight, robust wing for the MAV. The proposed smart wing consists of an active trailing edge flap integrated with bimorph actuators with piezoceramic fibers. Actuation is enhanced by preloading the bimorph actuators with a compressive axial load. The preload is exerted on the actuators through a passive latex or electroactive polymer (EAP) skin that wraps around the airfoil. An EAP skin would further enhance the actuation by providing an electrostatic effect of the dielectric polymer to increase the deflection. Analytical modeling as well as finite element analysis show that the proposed concept could achieve the target bi-directional deflection of 30° in typical flight conditions. Several bimorph actuators were manufactured and an experimental setup was designed to measure the static and dynamic deflections. The experimental results validated the analytical technique and finite element models, which have been further used to predict the performance of the smart wing design for a MAV.
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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) | 0.000 | 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) | 0.001 | 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".