Reversible Nonlinear Energy Harvester Tuned by Tilting and Enhanced by Nonlinear Circuits
Bibliographic record
Abstract
Nonlinear vibration is capable of effectively extending the frequency bandwidth of energy harvesters. Either hardening or softening nonlinearity has been used in various designs to achieve broad-bandwidth energy harvesting. In this paper, we propose a new method to achieve reversible hysteretic responses, i.e., both hardening and softening nonlinear responses, mechanically without additional magnetic interactions. This tunable nonlinearity endows energy harvesters with a great adaptability to environment. The proposed energy harvester is composed of a flexural center and two mass blocks, supported by a pair of elastic rods that are fixed on a vibration base. Different nonlinear responses are invoked by tilting the fixed-fixed elastic rods at different angles. A lumped-parameter model is developed to simulate the nonlinear electromechanical coupling system, and that is analytically solved by virtue of the high-order perturbation technique. The dynamic responses under different frequencies and accelerations are analytically characterized and compared well with the experimental data measured from a fabricated prototype. Furthermore, a nonlinear conditioning circuit (self-powered series synchronized switch harvesting on inductor) is constructed and tested with the proposed nonlinear energy harvester, with which the performance is enhanced about 200% for both resistive loads and capacitive loads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".