A nonlinear numerical model for sloshing motion in tuned liquid dampers
Bibliographic record
Abstract
Purpose This paper presents a new numerical model that, unlike most existing ones, can solve the whole liquid sloshing, nonlinear, moving boundary problem with free surface undergoing small to very large deformations without imposing any linearization assumptions. Design/methodology/approach The time‐dependent, unknown, irregular physical domain is mapped onto a rectangular computational domain. The explicit form of the mapping function is unknown and is determined as part of the solution. Temporal discretization is based on one‐step implicit method. Second‐order, finite‐difference approximations are used for spatial discretizations. Findings The performance of the algorithm has been verified through convergence tests. Comparison between numerical and experimental results has indicated that the algorithm can accurately predict the sloshing motion of the liquid undergoing large interfacial deformations. Originality/value The ability to model liquid sloshing motion under conditions leading to large interfacial deformations utilizing the model presented in this paper improves our ability to understand the problem of sloshing motion in tuned liquid dampers (TLDs), which would eventually help in constructing more effective TLDs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".