Optimal tuning of SMA inerter for simultaneous wind induced vibration control of high-rise building and energy harvesting
Bibliographic record
Abstract
Abstract This study proposes shape memory alloy based inerter combined with electromagnetic transducer for both vibration control of buildings and energy harvesting. The proposed device has non-linear spring made of shape memory alloy for its excellent load-deformation characteristics. The hysteretic behavior of this smart material is capable of dissipating significant amount of energy. The conventional viscous damping is also replaced by a motor, which offers flexibility in damping while converting the mechanical energy into power. The optimal performance of this device demands precise tuning of its parameters for vibration control of the building, which is exposed to random wind load. This, in turn, advocates for the solution of stochastic non-linear optimization problem, which is the main aim of this study. It is proposed in two steps i.e. adopt equivalent linearization for efficient input–output characterization followed by an ensemble surrogate analysis for stochastic response quantification. A seventy six storied benchmark building is used for numerical demonstration, which clearly establishes the superiority of the passive device for simultaneous vibration control and energy harvesting over the possible range of wind speeds. The results show that the ensemble surrogate model is very efficient to predict the responses compared to a single surrogate model. Overall the performance of the controller is impressive and can be adopted for further experimental investigation prior to its use in prototype buildings.
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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 | 0.000 | 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".