Numerical Modeling of Dynamic Behavior of Annular Tuned Liquid Dampers for Applications in Wind Towers
Bibliographic record
Abstract
Abstract: In this study, the performance of annular liquid tanks as a tuned liquid damper (TLD) in mitigating the vibration of wind turbines was investigated using a numerical model. A proposed hybrid wind tower model composed of a concrete shaft and a steel mast with a height of 150 m was simulated using a single-degree-of-freedom system. The structural domain including the tank wall and a rigid mass was modeled using finite element method, while the fluid domain was simulated by finite volume method using CFX software. A parametric study was carried out to investigate the behavior of annular TLD under harmonic loads for different mass and frequency ratios as well as displacement amplitudes. The damping characteristics of the annular TLD model were derived by comparing the numerical results with an equivalent linear model. In addition, the effectiveness of annular TLD was estimated by comparing the numerically calculated damping ratios with those corresponding to the optimum damping ratio values derived for a particular mass ratio based on the concept of tuned mass damper. It was found that that the annular TLD is effective when the amplitude of excitation is small. Moreover, the response of TLD in terms of nonlinear free surface sloshing and the energy dissipated by the system was discussed. Finally, the effectiveness of annular TLD in reducing the structural response of wind turbine towers under random vibrations was evaluated and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".