Pseudoelastic SMA Spring Elements for Passive Vibration Isolation: Part I – Modeling
Bibliographic record
Abstract
In this work, the effect of pseudoelastic response of shape memory alloys (SMAs) on passive vibration isolation has been investigated. This study has been conducted by developing, modeling, and experimentally validating a SMA-based vibration isolation device. This device consists of layers of preconstrained SMA tubes undergoing pseudoelastic transformations under transverse dynamic loading. These SMA tubes are referred to as SMA spring elements in this study. To accurately model the nonlinear hysteretic response of SMA tubes present in this device, at first a Preisach model (an empirical model based on system identification) has been adapted to represent the structural response of a single SMA tube. The modified Preisach model has then been utilized to model the SMA-based vibration isolation device. Since this device also represents a nonlinear hysteretic dynamical system, a physically based simplified SMA model suitable for performing extensive parametric studies on such dynamical systems has also been developed. Both the simplified SMA model and the Preisach model have been used to perform experimental correlations with the results obtained from actual testing of the device. Based on the studies conducted, it has been shown that SMA based vibration isolation devices can overcome performance trade-offs inherent in typical softening spring-damper vibration isolation systems. This work is presented as a two-part paper. Part I of this study presents the modification of the Preisach model for representing SMA pseudoelastic tube response together with the implemented identification methodology. Part I also presents the development of a physically based simplified SMA model followed by model comparisons with the actual tube response. Part II of this work covers extensive parametric study of a pseudoelastic SMA spring-mass system using both models developed in Part I. Part II also presents numerical simulations of a dynamic system based on the prototype device, results of actual testing of the device and correlations of the experimental cases with the model predictions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".