A new framework for optimizing energy harvesting from smart composites integrated with piezoelectric patches utilizing lamination parameters
Bibliographic record
Abstract
Amongst various structures, 2D panels are widely used in many industries and in each application, there are many ambient vibrations which can be converted into electrical energy. The very efficient method to utilize can be using piezoelectric patches which are stuffiest enough for various applications like energy harvesting. This study presents a novel approach for investigating energy harvesting in smart structures using composite panels integrated with piezoelectric patches. The panels are chosen to symmetry-balanced laminated composites, and modal and harmonic analysis conducted using the Rayleigh-Ritz method. To compute the kinetic and potential energy components of the piezoelectric patches at a local level, piecewise Heaviside functions are employed, and these energy components are integrated into the equations of motion together with those of the host composite plate. The results of the numerical method are validated by a commercial finite element software (FEM) COMSOL and there is a good match between FEM and this paper results. By subjecting the laminated composite with piezoelectric patches to forced vibration while varying the lamination parameter, the power output is optimized. The findings emphasize the substantial impact of the lamination parameter on power output, indicating that modifications can result in significant power output increase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".