Study on High Efficiency Energy Harvesting Using Piezoelectric Coupled Beam With Self-Tuning Process
Bibliographic record
Abstract
Studies on renewable energy harvesting have experienced significant growth due to the increasing demand of portable electronic devices and wireless sensor networks. We introduce the first time a beam energy harvester coupled with piezoelectric layers and stack actuators subjected to harmonic base excitation for achieving efficiency energy harvesting with a new developed self-frequency-tuning process. The self-frequency-tuning process of the harvester is realized by an adjustable axial force, which is generated by a piezoelectric stack actuator, through a feedback filtering electrical circuit. By the feedback filtering circuit, the value of axial force is determined by the amplitude of the output voltage generated on piezoelectric layers to tune the first natural frequency of the beam harvester close to the excitation frequency leading to a resonance of the harvester system. To describe and simulate the energy harvesting and the self-tuning process, a mathematical model is presented to calculate the dynamic response of the harvester as well as the output electric charge and voltage from piezoelectric layers for adjusting the axial force. It is noted that an iteration process is indispensable for the tuning process because of the transient nature of the vibrating system. A novel iteration numerical model is hence developed, and the whole energy harvesting process is divided into many short periods to represent the iteration steps and the self-tuning process. From numerical simulations, it shows that the self-tuning process helps increase the efficiency of the harvester, especially when the harvester is tuned close to its resonant state.
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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".