Development of a Maximum Energy Extraction Control for the Smart Spring
Why this work is in the frame
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Bibliographic record
Abstract
Most active vibration suppression approaches have attempted to suppress structural vibrations through the use of active material actuators, such as piezoceramic, that are incorporated into a structure to act directly against vibratory loads. These approaches require the actuators to simultaneously supply significant force and deflection to effectively suppress vibration. Unfortunately, successful implementation of these approaches has been hindered by the electromechanical limitations of piezoceramic actuators due to high power requirements in active vibration control applications. The Smart Spring concept is a unique approach that is designed to actively control combinations of dynamic impedance characteristics of a structure, such as the stiffness, damping, and effective mass to suppress vibration. The Smart Spring does not use actuators to perform work directly against excitation loads, but rather adaptively varies the effective structural impedance properties. Therefore, the piezoceramic actuators in the Smart Spring are not required to simultaneously produce large forces and deflections. Thus, the concept requires considerably less power because it enables active vibration control in an indirect manner. This study demonstrates the ability of the Smart Spring to control dynamic impedance characteristics of a structure through numerical simulations and experimental investigations. In addition, the development of a feedback control system is demonstrated. According to the control strategy, the impedance characteristics of the Smart Spring are continuously changing in order to maximize the extraction of the mechanical energy of the system.
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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