Nonlinear flap for passive flutter control of bidimensional wing
Bibliographic record
Abstract
A solution for passive aeroelastic control is presented and tested experimentally on a bidimensional wing setup. The solution consists of a flap integrated in the wing which acts as a secondary mass damper and absorbs energy when aeroelastic instability is encountered. This device is passive, which makes it safe in emergency cases and adds little mass to the system in the fundamental case presented in this paper. If installed on actual aircraft wings, the flap-NES would not substitute but coexist with classic active control methods, adding virtually zero mass to the existing system. Using a flap placed in the airflow enables the control system to benefit from aerodynamic damping with a behavior dependent on wind speed. The device can either have a linear or nonlinear stiffness. It is shown that both options absorb energy from the main system, that is, the wing. The flap acts as a TMD (tuned mass damper) in the linear case and as an NES (nonlinear energy sink) in the nonlinear case. The nonlinear solution not only absorbs more energy at given wind speed but it also is a more suited solution as the damper follows the wind speed dependent frequencies due to its nonlinear feature. In this work, the flap-TMD and the flap-NES are tested experimentally on a bidimensional wing which presents classic flutter.
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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".