Optimal energy dissipation in a semi-active friction device
Bibliographic record
Abstract
A semi-active device is presented for vibration control using energy dissipation by dry friction at contact surfaces. Semi-active behavior is provided by two piezoelectric stack actuators driven in real time to apply a normal force on a mobile component through two friction pads. Theoretical and experimental results show that there is an optimal constant normal force to maximize the energy dissipated for the case of a harmonic disturbance. In order to improve the energy dissipation by real time control of the normal force, two nonlinear controllers are proposed: (1) the Lyapunov method leading to a nonlinear bang-bang controller law and (2) the feedback linearization approach leading to equivalent viscous friction. The implementation of both strategies is presented and both are experimentally assessed using a clamped-free beam with the semi-active device attached to the beam. It is shown that a proper choice for the parameters of the controllers leads to an increased energy dissipation with respect to the case where the normal force is constant. This dissipation is further increased by adjusting a phase shift in the nonlinear feedback loop in order to avoid a stick-slip motion of the mobile component.
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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 | 0.000 |
| 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".