Simulation-Based Optimization of a Piezoelectric Energy Harvester using Artificial Neural Networks and Genetic Algorithm
Bibliographic record
Abstract
The problem of finding optimal design parameters for a piezoelectric energy harvester is studied. An accurate iterative numerical simulation model based on Euler-Bernoulli beam theory is used as the basis for defining a simulation-based optimization problem. Due to the complexity of the simulation model, evaluation of the Objective Function (OF) is difficult and computationally expensive. In order to remedy this problem, an Artificial Neural Network (ANN) model is trained based on a dataset obtained from the iterative numerical simulation. ANN is then used during the optimization process instead of the original expensive-to-evaluate simulation model. Performance evaluation for the ANN is performed using a set of test data. Genetic Algorithm (GA) optimization method based on the trained ANN model is further developed and optimum system parameters are obtained for an energy harvester based on piezoelectric patches and cantilever aluminum beam.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".