Equivalent Linearized Mechanical Model for Tuned Liquid Dampers of Arbitrary Tank Shape
Bibliographic record
Abstract
This paper presents a model to describe the behavior of sloshing in a general tank with a uniform fluid depth. An equivalent linearized mechanical model is developed for a tuned liquid damper (TLD) with arbitrary tank geometry. The finite element method is employed to determine the mode shapes of the sloshing fluid. In general, the mode shapes of arbitrary tanks will have response components in the x- and y-directions. The mode shapes enable the generalized properties of the sloshing fluid to be determined; these properties are subsequently used to establish equivalent mechanical properties. The nonlinear damping of slat-type damping screens is linearized, permitting it to be included in the model as amplitude-dependent viscous damping. The proposed model is in excellent agreement with existing linearized models for the special cases of rectangular and circular tanks. Sinusoidal shake table tests are conducted on tanks with chamfers placed in selected corners. In the literature, no experimental testing has focused on tanks of arbitrary shape with a constant fluid depth. The proposed model is in good agreement with the experimental results for the mode dominated by motion in the direction of excitation. However, the model is found to underestimate the response of the mode which is dominated by motion perpendicular to the excitation direction. The linearized mechanical model developed can serve as a useful preliminary TLD design tool.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".