Pseudoelastic SMA Spring Elements for Passive Vibration Isolation: Part II – Simulations and Experimental Correlations
Bibliographic record
Abstract
In Part II of this two-part study, system simulations and experimental correlations of a Shape Memory Alloy (SMA) based vibration isolation device (briefly described in Part I) has been presented. This device consists of layers of preconstrained SMA tubes undergoing pseudoelastic transformations under transverse dynamical loading. In Part II, detailed description of the prototype vibration isolation device, its experimental setup, and actual experimental test results are presented. An extensive parametric study has been conducted on a nonlinear hysteretic dynamical system, representing this vibration isolation device utilizing a physically based simplified SMA model and a Preisach model (an empirical model based on system identification) developed in Part I. Both the physically based simplified SMA model and the modified Preisach model have been utilized to perform experimental correlations with the results obtained from actual testing of the device. Based on the investigations, it has been shown that variable damping and tunable isolation response are major benefits of SMA pseudoelasticity. Correlation of numerical simulations and experimental results has shown that large amplitude displacements causing phase transformations of SMA components present in such a device are necessary for effective reduction in the transmissibility of such dynamical systems. It has also been shown that SMA-based devices can overcome performance trade-offs inherent in typical softening spring-damper vibration isolation systems. In terms of numerically predicting the experimental results, it has been shown that the Preisach model gave relatively accurate results due to better modeling of the actual SMA tube behavior. However, for a generic parametric study, the physically based simplified SMA model has been found to be more useful as it is motivated from the constitutive response of SMAs and hence, could easily incorporate different changes in system conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".