Design, modeling, and optimization of a stepped-beam piezoelectric energy generator under friction-induced vibration
Bibliographic record
Abstract
Abstract A stepped-beam piezoelectric energy generator (PEG) is proposed to harness energy from friction-induced vibration. First, a mathematical model incorporating beam vibration theory, friction model, and piezoelectric equation is developed to describe the dynamics of the system. The energy output of the PEG is evaluated by transient charging simulation, which is validated by experiment in previous literature. A piezoelectric stepped beam prototype is fabricated, and experimental tests under different conditions are conducted to validate the model. Subsequently, a convolutional neural network—long short-term memory neural network is proposed and trained with simulation data, functioning as a surrogate model for predicting the charging process. The well-trained model is used as the fitness function, and genetic algorithm (GA) is employed for parameter optimization. Optimizations under constant total mass (CTM) and varying total mass (VTM) conditions are conducted. The thickness of different sections <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>H</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> is selected as the tuning parameter. Improvements in energy output are achieved when comparing the best and worst fitness values, validating the feasibility of using GA for optimizing the geometry. With a 10% variation ratio and friction locations <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>d</mml:mi> <mml:mrow> <mml:mtext>f</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> = 0.09, 0.18, and 0.27 m, the results under CTM exhibit improvements of 42.9%, 30%, and 37.3%, while the results under VTM show improvements of 24.4%, 15.0%, and 23.2%. Further improvement can be achieved by further tuning the variation ratio and incorporating additional parameters. A potential application in wind energy harvesting and sensing is demonstrated, and the results indicate the feasibility of simultaneous energy harvesting and sensing.
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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 | 0.000 |
| 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) | 0.000 | 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".