Sensitivity Analysis and Optimal Design of Smart Peizolaminated Composite Beams
Bibliographic record
Abstract
The static and dynamic interaction between piezoelectric layer and host laminated beam has been investigated using classical laminate theory and first-order shear deformation theory. A finite element model has been developed to study the mechanical and electrical behavior of laminated composite beam with piezoelectric actuators. The numerical results have been compared with those available in the literature in order to validate the accuracy of the model. A design optimization methodology has been developed by combining the finite element model and the sequential quadratic programming technique to improve the structural performance. Further, the optimization algorithm has been improved by performing the sensitivity analysis and analytical gradients of constraints and objective function. Various types of optimization problems including shape control of a beam and mass minimization have been investigated. By careful positioning of the actuators and polarizing them to create bending moment and stretching force when required, a very precise shape control has been achieved as well as considerable reduction in the mass of the structure. It has been observed using the analytical gradients of constraints and objective function analytically can significantly reduce the total number of iteration required to obtain the optimal design.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".