Passive Vibration Control of Beams Subjected to Random Excitations with Peaked PSD
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it