A MORPHING WING USED SHAPE MEMORY ALLOY ACTUATORS NEW CONTROL TECHNIQUE WITH BI-POSITIONAL AND PI LAWS OPTIMUM COMBINATION - Part 2: Experimental Validation
Bibliographic record
Abstract
The paper represents the second part of a study related to the development of an actuators control system for a morphing wing application, and describes the experimental validation of the control designed in the first part. After a short presentation of the finally adopted control architecture, the physical implementation of the control is done. To implement the controller on the physical model two Programmable Switching Power Supplies AMREL SPS100-33 and a Quanser Q8 data acquisition card, were used. The inputs of the data acquisition were two signals from Linear Variable Differential Transformer potentiometers, indicating the positions of the actuators, and six signals from thermocouples installed on the SMA wires. The acquisition board outputs channels were used to control power supplies in order to obtain the desired skin deflections. The control validation was made in two experimental ways: bench test and wind tunnel test. All 35 optimized airfoil cases, used in the design phase, were converted into actuators vertical displacements which were used as inputs reference for the controller. In the wind tunnel tests a comparative study was realized around of the transition point position for the reference airfoil and for each optimized airfoil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".