Broadband piezoelectric vibration energy harvesting using a nonlinear energy sink
Bibliographic record
Abstract
Abstract A piezoelectric vibration energy harvester (PVEH) is capable of converting waste or undesirable ambient vibration energy into useful electric energy. However, conventional PVEHs typically work in a narrow frequency range, leading to low efficiency in practical application. This work proposes a PVEH based on the principle of the nonlinear energy sink (NES) to achieve broadband energy harvesting. An alternating current circuit with a resistive load is first considered in the analysis of the dynamic properties and electric performance of the NES-based PEVH. Then, a standard rectifying direct current (DC) interface circuit is developed to evaluate the DC power from the PVEH. To gain insight into the NES mechanism involved, approximate analysis of the proposed PVEH systems under harmonic excitation is sought using the mixed multi-scale and harmonic balance method and the Newton–Raphson harmonic balance method. In addition, an equivalent circuit model (ECM) of the electromechanical system is derived and circuit simulations are conducted to explore and validate the energy harvesting and vibration absorption performance of the proposed NES-based PVEH. The response is also compared with that obtained by direct numerical integration of the equations of motion. Finally, the optimal resistance to obtain the maximum DC power is determined based on the Newton–Raphson harmonic balance method and validated by the ECM. In general, the NES-based PVEH can absorb the vibration from the primary structure and collect electric energy within a broad frequency range effectively.
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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 | 0.000 |
| 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".