Flutter Analysis Using Quasi-Steady Time-Domain Flutter Derivatives
Bibliographic record
Abstract
To be able to perform nonlinear flutter analyses for bridges, time‐domain approaches should be used instead of Scanlan’s formulation of self‐excited forces. Thus, this paper addresses the development and validation of a modified quasi‐steady time‐domain model similar to Scanlan’s approach that is based on the velocity and acceleration of the bridge deck. In this formulation, quasi‐steady time‐domain flutter derivatives measured in the wind tunnel through forced‐vibration tests at absolute constant velocity and acceleration are used. For this, a unique test rig, which can be used either for free‐ or forced‐vibration tests, was utilized. By measuring the time‐domain flutter derivatives of the Great Belt Bridge, their nondimensionalization with respect to the bridge‐deck width, velocity and acceleration of the deck is validated. Then, time‐domain flutter analyses are performed using this new model. They agree with the experimental critical speed and the prediction using Scanlan’s model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".