A Tool to Identify Damping During Large Amplitude Vibrations of Viscoelastic Structures
Bibliographic record
Abstract
Various damping models are available to describe the dissipation energy present in structures. Among them, viscous and viscoelastic damping models are widely used to describe the damping in viscoelastic structures. Recent studies show that both viscous and viscoelastic damping of a structure varies as it experiences large amplitude vibrations. This makes it necessary to adjust the damping values with respect to the maximum vibration amplitude. The knowledge of damping values allows engineers to estimate the maximum vibration amplitude of a structure under various operating scenarios, thus improving the estimate of the safety margin and the efficiency of design. However, the variation of hysteretic damping during large amplitude vibrations was not addressed yet. Hysteretic damping is defined as dissipation energy due to the internal friction between the internal planes of the material and is independent on frequency. Hysteretic damping is well defined in linear vibrations as the ratio of loss and storage energies. The concept is here extended and hysteretic damping is identified during nonlinear vibrations. This paper presents the experimental data measured on a clamped-clamped rubber square plate under harmonic excitation at various force levels. A tool based on temporal method was developed to identify the nonlinear parameters and to calculate the hysteretic damping from the experimental data. The results show that hysteretic damping decreases with excitation frequency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".