Control strategies for an experimental morphing wing model
Bibliographic record
Abstract
The paper presents the control strategies used in an experimental morphing wing model starting from the open loop architecture until a real time optimized closed loop architecture. Three control methods are exposed here, methods designed to obtain and maintain some optimized airfoils during the wind tunnel tests. Also, for all designed architectures the experimental control results are shown. First method uses a database stored in the computer memory, database which contains some optimized airfoils correlated with the airflow cases as combinations of Mach numbers and angles of attack. The method is based on a controller that takes as reference value the necessary displacement of the actuators from the database in order to obtain the morphing wing optimized airfoil shape. The second method uses a similar controller as the first method but the control loop is built around the changes of the Cp values calculated by XFoil software in two fixed positions along the chord of the wing, positions associated to two Kulite sensors linked through aerodynamic interdependence with the actuators positions. The third control method is based on the pressure information received from the sensors and on the transition point position estimation. It includes, as inner loop, the first control method of the actuation lines. The method uses an optimizer code which finds the best actuators configuration in order to maximize the position of the transition, i.e. at the end of optimization sequence the transition should be found nearest possible to the trailing edge.
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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) | 0.004 | 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".