New control methodology for a morphing wing demonstrator
Bibliographic record
Abstract
A morphing wing can improve the aircraft aerodynamic performance by changing the wing airfoil depending on the flight conditions. In this paper, a new control methodology is presented for a morphing wing demonstrator tested in a subsonic wind tunnel in the open-loop configuration. Actuators integrated inside the wing are used to modify the flexible structure, which is an integral part of the wing. In this project, the actuators are made in-house and controlled with logic control, which is developed within the main frame of this work. The characterization of the flow (laminar or turbulent) over the wing is obtained starting from the pressure signals measured over the flexible part of the wing (upper surface). The signals are acquired by using some pressure sensors (Kulite sensors) incorporated in this flexible part of the wing upper surface. The technique used to collect Kulite pressure data and the post-processing methodology are explained. The recorded pressure data are sometimes subjected to noise, which is filtered before being processed. The standard deviation and power spectrum visualization of the pressure data approaches are used to evaluate the quality of the flow over the wing and estimate the transition point position in the area monitored by the Kulite sensors. In addition, infrared thermography visualization is implemented to observe the transition region over the entire wing upper surface, and to validate the methodology applied to the pressure data in this way. The demonstrator measures 1.5 m chordwise and 1.5 m spanwise. Four miniature actuators fixed on two actuation lines are used to morph the wing. The wing is also equipped with a rigid aileron. The experimental aerodynamic results obtained after post processing validate the numerical prediction for the transition location.
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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 | 0.001 | 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".