Simulation of Sloped-Bed Tuned Liquid Dampers Using a Nonlinear Shallow Water Model
Bibliographic record
Abstract
This research aims to develop an efficient and accurate model for simulating tuned liquid dampers (TLDs) with sloped beds. The model, based on nonlinear shallow water equations, is enhanced by introducing new terms tailored to each specific case. It employs the central upwind method and Minmod limiter functions for flux and interface variable assessment, ensuring both high accuracy and reasonable computational cost. While acceleration, slope, and dissipation are treated as explicit sources, an implicit scheme is utilized for dispersion discretization to enhance the model’s stability, resulting in matrix equations. Time discretization uses the fourth-order Runge–Kutta scheme for precision. The performance of the model has been evaluated using several test cases including dam-breaks on flat and inclined beds and run-up and run-down simulations over parabolic beds, which are relevant to sloshing in tanks with sloped beds. It accurately predicts phenomena such as asymmetric sloshing waves, especially in sloped beds, where pronounced waves occur. Dispersion and dissipation terms are crucial for capturing these effects and maintaining stable wave patterns. An initial perturbation method assesses the tank’s natural period and numerical diffusion. Furthermore, the model integrates with a single-degree-of-freedom (SDOF) system to create a TLD model, demonstrating enhanced damping effects with sloped beds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".