Experimental Characterizations and Estimation of the Natural Frequency of Nonlinear Rubber-Damped Torsional Vibration Absorbers
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
The natural frequency of a rubber-damped torsional vibration absorber (TVA) depends on the excitation amplitudes and frequencies in a highly nonlinear manner. This is due to nonlinear shear properties of the rubber ring. In this study, the nonlinear static and dynamic shear characteristics of a rubber ring, and the natural frequency of a nonlinear TVA are experimentally characterized firstly. Since a rubber ring employed in a rubber-damped TVA is usually in the compression state, its static and dynamic shear properties depend upon the compression ratio and dimensions apart from the chemical ingredients in a highly complex manner. The prediction of the natural frequency of a rubber-ring TVA thus poses considerable complexities. In this study, a special fixture is designed and fabricated for characterizing shear properties of a rubber ring subject to different compression ratios. The shear properties are subsequently characterized using different constitutive models, and a methodology for identifying the model parameters is presented considering the measured properties. Second, a methodology for estimating the natural frequency of the TVA is proposed, and the effectiveness of the proposed method is demonstrated through comparisons of the estimated natural frequency with the measured values. The results of the study suggest that the model using fractional derivatives to characterize nonlinear shear properties of a rubber ring can be effectively used to obtain accurate estimation of natural frequency of a nonlinear TVA over a wide range of excitations. The natural frequency of a TVA can thus be accurately estimated before prototyping using the experimental and modeling methods developed in this paper.
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