Bibliographic record
Abstract
Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".