Numerical modeling of sloshing motion in a tuned liquid damper outfitted with a submerged slat screen
Bibliographic record
Abstract
Abstract Tuned liquid dampers (TLDs) are among the most economical and effective passive damping devices. They have been increasingly used to reduce dynamic response and protect structures from failure due to external dynamic excitations. Slat screens are one of the most effective devices used to increase the inherent damping of a TLD, and to reduce the non‐linearity of the free surface motion. A numerical algorithm has been developed to solve the complete non‐linear, moving boundary flow problem in a TLD outfitted with slat screens. The model has been developed to handle conditions leading from small to large interfacial deformations without imposing any linearization assumptions. The numerical algorithm is based on the finite‐difference method. The free surface has been determined using the volume‐of‐fluid method and the donor–acceptor algorithm. The effect of the slat screens has been modeled explicitly by using the partial‐cell treatment method. The present algorithm has been validated against experimental data. The results indicated that the present algorithm is capable of providing accurate details of the flow field inside the TLD and through the screens. These details are essential to improve our understanding of the important parameters governing the performance of a TLD, and hence, to enhance our ability to design better TLDs. Copyright © 2010 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 | 0.001 |
| 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 | 0.000 |
| 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".