Adaptive Compensation for Detuning in Pendulum Tuned Mass Dampers
Bibliographic record
Abstract
Detuning, resulting from deterioration, inadvertent changes to structure properties, and design forecasting, can lead to a significant loss of performance in tuned mass dampers (TMDs). To overcome this issue, an adaptive compensation mechanism for suspended pendulum TMDs is proposed. The adaptive pendulum mass damper is a three-dimensional pendulum, augmented with a tuning frame to adjust its natural frequency, and two adjustable air dampers adjust damping. The adjustments for the natural frequency and damping compensation are achieved using a system of stepper motors and a microcontroller. There are two major components in the proposed methodology: identification and control, one followed by the other, in that order. The identification is carried out using spectral information obtained from the structural acceleration responses. The performance of the adaptive pendulum system is studied via both experiments and simulations. The main contribution of this paper is to develop an effective means of compensation for detuning in TMDs, while retaining the simplicity of passive pendulum TMDs. The proposed methodology allows pendulum TMDs to be tuned in place using relatively simple hardware and algorithms, based on ambient vibration measurements only.
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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 | 0.001 | 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 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".