Nonlinear multimodal model for an annular tuned sloshing damper equipped with damping screens
Bibliographic record
Abstract
Annular tuned sloshing dampers equipped with damping screens are studied experimentally and analytically. A nonlinear multimodal model is presented to simulate the coupled response among the lowest order sloshing modes in a tank equipped with damping screens, which leads to velocity-squared damping. Shake table tests are conducted on annular tanks with various inner radii, water depths, screen orientations, and base excitation amplitudes. The proposed model is evaluated by comparing the predicted and measured sloshing forces, energy dissipation per cycle, and wave heights. The predicted sloshing forces and energy dissipation per cycle are in good agreement with the measured results. The wave heights show larger discrepancies, including phase shifts; however, the peak amplitudes are captured with reasonable accuracy for the tests conducted. Secondary resonances lead to multiple peaks in the frequency response plots when higher order sloshing modes become excited through modal coupling. Plots created to indicate which secondary resonances are likely to occur for a given liquid depth ratio indicate that it may not be possible to avoid all secondary resonances. Radial damping screens can be strategically positioned within the tank to provide the desired level of damping to the fundamental sloshing modes, as well as a reasonable amount of damping to higher order modes that are susceptible to secondary resonance excitation. Since existing linearized models for annular tuned sloshing dampers equipped with damping screens do not capture the important nonlinear response characteristics of these devices, the proposed model fills an important research gap necessary to facilitate their effective design.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".