Self-adaptive morphing wing model, smart actuated and controlled by using a multiloop controller based on a laminar flow real time optimizer
Bibliographic record
Abstract
The modeling, the design, the numerical simulation and the experimental testing of the control system for a self-adaptive morphing wing model are here exposed. The study was performed during a multidisciplinary research project, involving industrial partners, a research institute and three academic entities. The developed control system is a multiloop one, being designed, simulated and tested in two major steps, correlated with the validation phases of the aerodynamic gains provided by the morphed wing model in terms of the laminar flow improvement over its upper surface. The two validation phases were suggestively called open loop, respectively closed loop; in the first phase the aerodynamic validation was made just by comparing the experimentally obtained results with the numerical optimization obtained ones, while in the second phase the morphing wing model was left free, to adapt itself based on the information related to the transition point position provided by some pressure sensors installed on its upper surface. The used wing model was a rectangular one, equipped with a composite made flexible upper surface, morphed along of two lines by using some shape memory alloy actuators. For the open loop phase a database with some optimized airfoils was generated and a smart controller based fuzzy logic was designed to control the position of the actuators in real time so that the desired optimized skin corresponding to the desired displacements to be obtained and maintained during the flight tests. The closed loop architecture was realized by using a real-time optimization algorithm, which included the actuators controller as inner loop. The algorithm was developed in order to generate real-time optimized airfoils starting from the information received from the pressure sensors and targeting the morphing wing main goal: the improvement of the laminar flow over the wing upper surface.
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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".