Vibration Suppression Using Distributed Tuned Mass Damper Technology
Bibliographic record
Abstract
The Distributed Tuned Mass Damper (DTMD) technology, which is one of the modifications of the classical Tuned Mass Damper (TMD) technique, is defined as the multiple TMD design based on one mode of the primary structures. The modeling procedure for this kind of problem is similar to that for the single TMD design, especially for discrete (or modeled as) primary structures, such as the building-type structures. Therefore, the challenge work in this area is to attain the best vibration suppression performance through an optimally designed DTMD system. From the point of optimization, basically, two typical approaches have been utilized to design the DTMD system. The first one is to directly obtain the transfer function and then define the variance or the Dynamic Magnification Factors (DMF) as objective functions, which was utilized by many researchers. In the other methodology, the transfer function is expressed as a dynamic model with an optimal H2 controller under predefined form. In this study, a new optimal DTMD design approach will be presented, in which the optimization objective function and method will be established based on the Linear Quadratic Controller design method. The presented method provides a simple and straightforward way to design the DTMD system. The numerical example will be presented to illustrate the vibration suppression performance of the optimally designed DTMD system.
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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) | 0.001 | 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 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".