Modeling Flutter Using Deflection-Dependent Strain-Rate Damping
Bibliographic record
Abstract
Flutter is a flow-induced unstable motion in structures that has drawn researchers’ attention in the past decades due to its presence in numerous applications including aviation. Linear and nonlinear models of flutter have been developed. Linear models are simple and accurate for predicting the critical velocity at which flutter occurs. However, they are not capable of describing the post-flutter behavior of structures. Nonlinear models, on the other hand, can properly demonstrate the unstable motion accompanied with the occurrence of flutter but they are highly complicated. In fact, numerical solution of these equations requires extensive computations. As a result, having a model that is both simple and valid for post-flutter simulations is of critical importance. Linear models lose their accuracy when large deflections take place in the structure. This is when the unconsidered tensions that oppose large deflections come into play and render the behavior of the structure nonlinear. Usually, a type of damping relative to strain-rate is assumed for modeling structures under flutter. This paper introduces a deflection-dependant strain-rate damping coefficient to the linear flutter model, so as the deflections grow the restraining forces increase to limit the motion. The new sets of equations are derived and simulations are conducted to ensure the capability of the model to capture the post-flutter behavior. Results are then compared with the results of nonlinear simulation to demonstrate the new model’s compliance with those of nonlinearly-modeled systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".