Modeling the Response of Structure–Tuned Liquid Damper Systems Under Large Amplitude Excitation Using Smoothed Particle Hydrodynamics
Bibliographic record
Abstract
Abstract The tuned liquid damper (TLD) is a system used to reduce the response of tall structures. Numerical modeling is a very important tool when designing TLDs. Many existing numerical models are capable of accurately capturing the structure–TLD system response at serviceability levels, covering the range where TLDs are primarily intended to perform. However, these models often have convergence issues when considering more extreme structural excitations. The goal of this study is to develop a structure–TLD model without convergence limitations at large amplitude excitations. A structure–TLD numerical model where the TLD is represented by a two-dimensional incompressible smoothed particle hydrodynamics (ISPH) scheme is presented. The TLD contains damping screens which are represented by a force term based on the Morison equation. The performance of the model is assessed by comparing to experimental data for a structure–TLD system undergoing large amplitude excitations consisting of 4-h random signals and shorter transient signals. The model shows very good agreement with the experimental data for the structural response. The free surface response of the TLD is captured accurately by the model for the lower excitation forces considered, however as the excitation force is increased there are some discrepancies. The large amplitude excitations also result in smoothed particle hydrodynamics (SPH) fluid particles penetrating the boundaries, resulting in degradation of the model performance over the 4-h simulations. Overall, the model is shown to capture the response of a structure–TLD system undergoing large amplitude excitations well.
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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 | 0.001 | 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".