Phase based Control of a Novel Beam-Shape MRE-based Adaptive Tuned Vibration Absorber
Bibliographic record
Abstract
Semi-active adaptive tuned vibration absorbers (SATVAs) can be effectively utilized to attenuate the unwanted vibrations in a broad range of tonal excitations. If the excitation frequency of the primary system changes with time, it is desirable to adaptively tune the natural frequency of the absorber to track the excitation frequency. To this end, the stiffness of the SATVA can be altered. The current study investigates the phase based control of a proposed MRE based beam-like structure as a semi-active adaptive tuned vibration absorber. The SATVA consists of a sandwich beam with MRE core layers constrained by thin elastic plates located on the top, bottom and also in the middle, as well as the electromagnets attached at the free end of the sandwich beam. The function of electromagnets is twofold: providing the required magnetic field to the MRE layers and also serving as the absorber’s active mass. Upon application of a controllable external magnetic field through the current applied to the electromagnets, the stiffness of the MRE layers and consequently the SATVA’s natural frequency can be controlled. In this study, first using the finite element dynamic modelling, an equivalent single-degree-of-freedom model of SATVA based on its fundamental mode of vibration has been derived. Then, using the variation of MRE’s shear modulus with respect to the applied magnetic field, the variation of the natural frequency of SATVA with respect to the applied magnetic field has been evaluated. Finally, a control law based on the phase difference between the relative accelerations of the absorber and host structure, has been utilized to evaluate the magnetic field required by the absorber to track the time varying tonal excitation. The performance of the control law is then demonstrated and compared with the passive system.
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.
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 | 0.000 |
| 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 | 0.001 | 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".