Multi-Tuned Optimum Vibration Absorbers for Curved Beams
Bibliographic record
Abstract
Detailed investigations on the vibration suppression of beam-type structures using Multiple Tuned Mass Damper (MTMD) technology has been carried out in this study. A general curved beam has been utilized as a case study to illustrate the developed optimum design methodology. The governing differential equations of motion for the curved beam with the attached MTMD systems have been derived, and then solved using the finite element method. A hybrid optimization methodology, which combines the global optimization method based on Genetic Algorithm (GA) and the local optimization technique based on Sequential Quadratic Programming (SQP), has been developed. This has been utilized to find the optimum design parameters (damping coefficient, spring stiffness and position coordinate) of the attached Tuned Mass Damper (TMD) systems in order to suppress the vibration levels at a particular mode or several modes, simultaneously. Finally, a design principle for vibration suppression of beam-type structures using the MTMD technology has been proposed through extensive numerical investigations.
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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) | 0.000 | 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 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".