Passive Vibration Control of Beams Subjected to Random Excitations with Peaked PSD
Bibliographic record
Abstract
Vibration suppression in beams subjected to random excitations with peaked Power Spectral Densities (PSDs) is studied in this paper. An optimal Tuned Mass Damper (TMD) system is used to suppress the undesirable vibration. The Timoshenko beam theory is applied to the beam model and the governing equations of motion are solved using the Galerkin method. Using the Sequential Quadratic Programming (SQP) method, the problem is solved to obtain the optimum values of the design variables (i.e. frequency ratio and the damping ratio) of the TMD system. Subsequently, a parametric study is carried out and the effects of the input parameters, such as the mass ratio, structural damping ratio, and the peak frequency of the random excitation on the design variables were investigated. The robustness of the optimal control system is also studied. Based on the PSD of the random excitation and using a Monte Carlo simulation algorithm, a set of numerical data for the excitation force is generated in the time domain and the effectiveness of the designed TMD system is investigated.
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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 | 0.000 |
| 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 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".