Experimental Evaluation of the Smart Spring Impedance Control Approach for Adaptive Vibration Suppression
Bibliographic record
Abstract
Most active vibration suppression approaches have attempted to suppress structural vibration by incorporating active material actuators, such as piezoceramic, within the structure to act directly against vibratory loads. These approaches require the piezoceramic actuators to generate significant force to effectively counteract the vibratory forces. Unfortunately, successful implementation of these approaches has been hindered by the limited displacement capabilities of piezoceramic actuators. The Smart Spring concept is a unique approach to actively control combinations of dynamic impedance characteristics of a structure, such as the stiffness and damping, to suppress vibration in an indirect manner. The piezoceramic actuators employed in the Smart Spring concept are not used to directly counteract excitation loads but rather adaptively vary the effective impedance properties of the structure. This study demonstrates the ability of the Smart Spring concept to control dynamic impedance characteristics of a structure through experimental investigations. Mechanical shaker tests using the proof-of-concept hardware verify the controllability of the impedance properties using the Smart Spring device and its ability to suppress vibration. More importantly, the tests conducted in a wind tunnel demonstrate the performance of the Smart Spring under highly varying unsteady excitation conditions. These experimental investigations confirm the capability of the concept that can easily be implemented as an adaptive mount system to suppress undesired vibration generated by mechanical systems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".