Control of Actuation System Based Smart Material Actuators in a Morphing Wing Experimental Model
Bibliographic record
Abstract
The modeling and the experimental testing of the aerodynamic performance of a morphing wing, starting from the design concept phase all the way to the bench and wind tunnel tests phases, are presented here. Several wind tunnel test runs for various Mach numbers and angles of attack were performed in the 6 × 9 ft 2 wind tunnel at the Institute for Aerospace Research at the National Research Council Canada. A rectangular finite aspect ratio wing, having a morphing airfoil cross-section due to a flexible skin installed on the upper surface of the wing, was instrumented with Kulite transducers. The flexible skin needed to morph its shape through two actuation points in order to obtain an optimized airfoil shape for several flow conditions in the wind tunnel: the Mach number varied from 0.2 to 0.3 and the angle of attack between -1 o and 2 o . The two shape memory alloy actuators, having a non-linear behavior, drove the displacement of the two control points of the flexible skin towards the optimized airfoil shape. Unsteady pressure signals were recorded and analyzed and a thorough comparison, in terms of mean pressure coefficients and their standard deviations, was performed against theoretical predictions, using the XFoil computational fluid dynamics code. The acquired pressure data was analyzed through custom-made software created with Matlab/Simulink in order to detect the noise magnitude in the surface airflow and to localize the transition point position on the wing upper surface. This signal processing was necessary in order to detect the Tollmien-Schlichting waves responsible for triggering the transition from laminar to turbulent flow.
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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) | 0.003 | 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".