Sensitivity analysis and design optimization of smart laminated beams using layerwise theory
Bibliographic record
Abstract
In the present work, the sensitivity analysis of laminated beams with surface bonded and/or embedded piezoelectric sensors and actuators has been conducted using the finite element model based on the layerwise displacement theory. The finite element formulation also incorporates the interaction between electrical and mechanical fields. The gradients of various constraints and objective functions with respect to the design variables are derived analytically. By combining the coupled layerwise finite element model, the developed analytical gradients and sequential quadratic programming (SQP) technique, an efficient optimization algorithm has been developed. Illustrative examples are presented to demonstrate the capabilities and efficiency of the developed sensitivity analysis and optimization algorithm in both static and dynamic problems. Two main design objectives, namely, mass minimization and actuating force maximization along with displacement and frequency constraints, are considered in the optimization. It has been shown that the use of analytical gradients results in significant reduction in computational efforts in the sensitivity analysis and the design optimization. This advantage is well pronounced in large structural problems or in problems with many design variables.
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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 | 0.000 |
| 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 | 0.001 |
| 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".