Distributed parameter model and experimental validation of a compressive-mode energy harvester under harmonic excitations
Bibliographic record
Abstract
This paper presents the modeling and parametric analysis of the recently proposed nonlinear compressive-mode energy harvester (HC-PEH) under harmonic excitation. Both theoretical and experimental investigations are performed in this study over a range of excitation frequencies. Specially, a distributed parameter electro-elastic model is analytically developed by means of the energy-based method and the extended Hamilton’s principle. An analytical formulation of bending and stretching forces are derived to gain insight on the source of nonlinearity. Furthermore, the analytical model is validated against with experimental data and a good agreement is achieved. Both numerical simulations and experiment illustrate that the harvester exhibits a hardening nonlinearity and hence a broad frequency bandwidth, multiple coexisting solutions and a large-amplitude voltage response. Using the derived model, a parametric study is carried out to examine the effect of various parameters on the harvester voltage response. It is also shown from parametric analysis that the harvester’s performance can be further improved by selecting the proper length of elastic beams, proof mass and reducing the mechanical damping.
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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 | 0.001 | 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".