Experimental and numerical investigations of the dynamic interaction of tuned liquid damper–structure systems
Bibliographic record
Abstract
Tuned liquid dampers (TLDs) are dynamic vibration absorbers used in suppressing structural vibration under wind and seismic loads. They are easy to design and implement with low cost and low maintenance. However, due to their highly nonlinear behavior, it is difficult to establish representative models for TLDs that are accurate for a wide range of operations. In this paper, a new numerical model (finite volume method/finite element method (FVM/FEM method)) is introduced by simultaneously using finite volume and finite element approaches to represent fluid and solid domains, respectively. In order to assess the accuracy of the FVM/FEM results a state of the art experimental technique, namely real-time hybrid simulation (RTHS), is used. During the RTHS the response from the TLD is obtained experimentally while the structure is modeled in a computer, thus capturing the TLD–structure interaction in real-time. By keeping the structure as the analytical model, RTHS offers a unique flexibility in that a wide range of influential parameters are investigated without modifications to the experimental setup. This is not possible in traditional shake-table dynamic tests where a physical model of the structure needs to be built and tested together with the TLD. As a result, the verification of the numerical models for TLD–structure interaction available in the literature only consider a smaller, restricted dataset. In this study three numerical models from the literature are selected and together with the FVM/FEM developed here, the accuracies of these four models are assessed in comparison with RTHS results that consider a wide range of influential parameters. Results show that the proposed FVM/FEM model can accurately predict TLD behavior in both sinusoidal and ground motion forces and Yu’s model is the most accurate among the investigated simplified models.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".