Vibration-based electromagnetic energy harvesters for MEMS applications
Bibliographic record
Abstract
This thesis investigates vibration-based electromagnetic energy harvesters (EMEHs) for application in low power autonomous sensors. It makes contributions pertaining to the development of a low cost fabrication technology, analytical modeling, simulations and characterization of EMEHs under harmonic and random vibrations. A novel, low cost, one mask fabrication technology devised in this thesis is used to develop a copper foil-type linear EMEH, and a polydimethylsiloxane (PDMS) membrane type nonlinear EMEH. The voltage and power generated by these harvesters are comparable to existing EMEHs which use more involved fabrication processes. In the membrane type EMEH the inclusion of a more flexible PDMS membrane design reduces the harvester resonant frequency and makes it suitable for extracting energy from low level vibration environments. For acceleration levels greater than 0.1 g, this harvester exhibits a nonlinear behaviour. At higher levels of narrow band random excitations, the device therefore exhibits broadening of the load voltage spectrum in comparison to the response under relatively low levels of narrow band random excitations. Analytical models for linear EMEHs with non-uniform magnetic field for harmonic vibrations are developed. A simple analytical model based on Faraday’s law and uniform gradient of the normal component of the magnetic flux density is developed for EMEHs where the entire coil experiences approximately the same gradient of the normal component of the magnetic flux density. However, for EMEHs where the entire coil is not exposed to the same magnetic flux gradient a more robust model, based on the off-center analytical solution of the magnetic flux density is devised. The simulation results of the developed models are in good agreement with the experimental observations. Analytical models for linear and nonlinear EMEHs under random vibrations are derived. The models are parameterized such that they are applicable to all architectures of EMEHs and can be utilized for designing and performance estimation of EMEHs. Nonlinear harvesters with spring nonlinearity and with combined spring and damping nonlinearity are modeled using the statistical linearization method. The developed models are useful in investigating the effects of the mechanical nonlinearity on the performance and bandwidth of the harvesters under random vibrations.
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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) | 0.002 | 0.001 |
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".