Tuned Liquid Dampers to Mitigate Wind-Induced Motions of Buildings
Bibliographic record
Abstract
Passive auxiliary dynamic vibration absorbers (DVA), in particular the tuned mass damper (TMD) and tuned liquid damper (TLD), have become accepted devices for reducing the resonant motions of lively tall structures. A TLD is comprised of a rigid tank partially filled with liquid (usually water) and fitted with energy dissipating mechanisms such as screens. Despite the simplicity of a TLD's physical components, its nonlinear dynamic characteristics complicate the prediction of its performance as a DVA. This paper focuses on the application side of tuned liquid dampers fitted with internal damping screens. Experimental data are presented from a research program conducted to provide insight into the performance of a TLD in terms of its efficiency and robustness. The efficiency of a TLD is measured by comparing the effectiveness of a particular TLD to that of an equivalent optimal linear TMD with the same mass ratio. The entire water mass often does not participate in the sloshing motion resulting in an efficiency level that is less than that of an equivalent optimal linear TMD. Robustness of a damper is the measure of insensitivity of its performance to changes in the parameters of the damper and the main structure from their proposed values. In this study, robustness is determined by examining the effectiveness of a TLD under various response amplitudes, the limiting of the sloshing motion by restricting the available freeboard, and varying the natural frequency of the structure itself. Two directional TLDs, which operate simultaneously in two orthogonal directions, are also briefly discussed.
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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) | 0.002 | 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".