Improving the Performance of a Piezoelectric Energy Harvester Using a Tip Spring-Mass System
Bibliographic record
Abstract
Vibration-based energy harvesting using piezoelectric materials has gained considerable attention over the past decade. Currently, most piezoelectric energy harvesters (PEHs) are single resonance frequency based. The performance of a single-resonance PEH is often limited to only one resonance frequency. This paper discusses the possibility of improving the performance of a bimorph PEH by tuning the PEH using a spring-mass system attached to the bimorph’s free end. Through adding the spring-mass system, the PEH’s resonance frequency can be tuned to match the ambient vibration frequency, and its voltage/power-generating capability can be improved. An electromechanical model of the PEH is derived based on the Lagrange multiplier method. The model is then used in a harmonic base excitation case study, and the coupled electromechanical outputs are discussed. Simulation results show that the spring-mass attachment can create two resonant frequencies, making the PEH capable of working efficiently at two different frequencies in a low-frequency level. It is also shown that by properly selecting the spring stiffness and the mass, the voltage and power output of the PEH can be greatly increased as compared to a single bimorph without the spring-mass system.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".