Nonlinear Multimodal Model for Tuned Sloshing Dampers With Nonflat Bottoms
Bibliographic record
Abstract
Abstract A third-order nonlinear multimodal model is developed for tanks with nonflat bottoms. A six-node finite element model is used to determine the mode shapes of the velocity potential and the associated natural sloshing frequencies. Using this modal information, equations of motion are developed that accommodate the nonlinear coupling among the first three sloshing modes. Damping arising from screens is incorporated into the model using the principle of virtual work. The equations of motion are ordinary differential equations that are solved using the Runge–Kutta–Gill numerical time-stepping method. The model is evaluated with an existing third-order nonlinear multimodal model for a flat-bottom tank and is found to be in excellent agreement. Demonstrative simulations are conducted for tanks with sloped-, boxed-, and ramped-bottoms. The resulting sloshing forces and wave heights at the tank end wall are calculated and presented using time series plots and frequency response plots. The excitation of higher-order sloshing modes through modal coupling results in larger wave heights, and shallower wave troughs. The sloshing forces are less impacted by the responses of higher modes. Secondary resonances are clearly visible in several frequency response plots at frequencies that correspond to the natural sloshing frequency of a higher-order mode divided by an integer. The model is applicable to tanks that are of intermediate water depth with moderate excitation amplitudes, where the response of the second- and third-order sloshing modes are less than the fundamental mode.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".