Control validation of a morphing wing in an open loop architecture
Bibliographic record
Abstract
Aircraft wings are generally designed and optimized to give the best possible performance for cruise flight conditions. Using conventional control surfaces such as flaps, ailerons, variable wing sweep and spoilers, the structure of aircraft wings is changed for other flight conditions. With the introduction of wing morphing, the flow over an aircraft’s wings can be modified locally to improve the overall wing and aircraft performance during the different flight steps. The goal of this research work is to develop an actuation control principle using a grid consisting of four similar miniature electromechanical actuators for a new morphing wing mechanism. The actuators modify the flexible upper surface of the wing so that the upper flow is modified and consequently the transition point from laminar to turbulence is delayed. The flexible upper wing surface is closed to the wing tip, while the skin is made of composite materials. The first actuation line is located at 32% and the second actuation line is at 48% of the chord. The actuators are fixed on the wing ribs and the top is attached to the flexible skin with screws. A database that relates the actuator displacements and the optimized skin is tailored for different flight conditions. A smart controller based fuzzy logic is designed to control the position of the actuator in real time so that the desired optimized skin corresponding to the desired displacements is obtained and maintained during the flight tests. The feasibility and the effectiveness of the control method are demonstrated experimentally.
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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) | 0.000 | 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".