Nonlinear Series-Type Tuned Mass Damper-Tuned Sloshing Damper for Improved Structural Control
Bibliographic record
Abstract
A novel type of dynamic vibration absorber (DVA) is proposed, which consists of a tuned mass damper (TMD) and tuned sloshing damper (TSD) connected in series to the structure. The system enables the expensive viscous damping devices (VDDs) associated with traditional TMDs to be omitted from the design. A linearized equivalent mechanical model and a nonlinear multimodal model are developed to investigate the proposed system. A TMD–TSD is nonlinear due to the quadratic damping associated with liquid drag, which ensures the system performance is amplitude-dependent. Simple expressions for the optimal TSD–TMD mass ratio, tuning, and damping ratios are employed to design a TMD–TSD coupled to a single degree-of-freedom (SDOF) structure. Frequency response curves for the structure, TMD, and TSD degrees-of-freedom are created for several excitation amplitudes, and the nonlinear behavior of the system response is evident. The performance of the TMD–TSD is evaluated against traditional TMD and TSD systems—with the same total mass—by computing the effective damping produced by each system. The proposed system is found to provide a superior acceleration reduction performance and superior robustness against changes to the frequency of the primary structure. The proposed system is, therefore, an effective and affordable means to reduce the resonant response of tall buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".