Controller and aeroelasticity analysis for a morphing wing
Bibliographic record
Abstract
The main objectives of this research work are: the design and the wind tunnel testing of a controller for a new morphing mechanism using smart materials made of Shape Memory Alloy (SMA) for the actuators, and the aero-elasticity studies for the morphing wing. The finally obtained configuration for the controller is a combination of a bi-positional controller (on-off) and a PI (proportional-integral) controller, due to the two phases (heating and cooling) of the SMA wires' interconnection. Firstly, the controller is used for the open loop development step of a morphing wing project, while, further, it is included as an internal loop in the closed loop architecture of the morphing wing system. In the controller design procedure four step are considered: 1) SMA actuators model numerical simulation for different loading force cases; 2) linear system approximation in the heating and cooling phases using Matlab's System Identification Toolbox and the numerical values obtained in the first step; 3) selecting the controller type and its tuning for each of the two SMA actuators' phases - heating and cooling; and 4) integration of the two controllers just obtained into a single controller. For the controller validation three actions are taken: 1) numerical simulation; 2) bench testing; and 3) wind tunnel testing. For the third part of this study, aeroelastic studies, the purpose is to determine the flutter conditions in order to be avoided during wind tunnel tests. These studies show that aeroelastic instabilities for the morphing configurations considered appears at Mach number 0.55, which is higher than the wind tunnel Mach number limit speed of 0.3. © 2011 by Ruxandra Botez.
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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) | 0.003 | 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".