Modeling and analysis of a structure semi-active tuned liquid damper system
Bibliographic record
Abstract
A tuned liquid damper (TLD), which is similar to a tuned mass damper (TMD), is a type of dynamic vibration absorber (DVA) that can be employed to reduce wind induced resonant vibrations of a structure. Improved TLD performance could be realized by equipping TLDs with variable energy dissipating capabilities such as damping screens, which can be adjusted through a certain mechanism, permitting optimal control performance to be maintained over a wide range of loading conditions in a semi-active mode of control. In this paper, a control strategy based on a gain scheduling scheme is utilized by controlling the inclination angle of the damping screen(s) and consequently the screen loss coefficient value(s). The gain scheduling control strategy is employed on a simple single-story structure equipped with a semi-active TLD (SA-TLD) in order to maintain the optimal damping value (ζTLD − opt) based on averaged or instantaneous structural response tracking and a prescribed look-up table. Results are assessed using experimental values from tests conducted on conventional passive TLDs. A performance comparison between a semi-active TLD control system and a conventional passive TLD control system is carried out. The fluid response amplitude for a SA-TLD is also investigated and compared to that of a passive TLD. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".