Design and characterization of non-linear electromagnetic energy harvester with enhanced energy output
Bibliographic record
Abstract
This paper presents design, analysis, and characterization of a compliant non-linear electromagnetic energy harvester operating on the principle of bi-stability incorporated with a displacement amplification mechanism. The energy harvester engenders energy from ambient vibrations at multiple modes and with frequencies ranging from 80 to 120 Hz. The energy harvester is analytically modeled using the mechanic’s elastic beam theory. Finite Element Analysis (FEA) has been carried out using commercial Finite Element Method (FEM) based software to perform static, fatigue, dynamic and electromagnetic analysis. Bi-stable mechanism has been used to harness energy at a wider frequency bandwidth while lever mechanism is acting as a displacement amplifier for enhancing low frequency vibration amplitude with the amplification factor of 6.24. With such amplification factor, the energy harvester can be deployed to extract vibrations of diminutive level and recast them into electrical energy in the form of generated voltage. Spring steel material, owing to its structural sensitivity, stiffness and higher value of ultimate stress, was used for its fabrication. The use of Neodymium (N52) magnets, copper coils and shaker were put into practice for testing of this device. The coil resistance is 2.5 Ω with number of turns kept at 350. The maximum output voltage of 1.86 V was generated at 106 Hz with root mean square value of 548 mV. Testing results are in accordance with the results obtained through simulations and thus useful electrical power of 11.1 mW can be generated from ambient vibrations at a wider bandwidth of 18 Hz with operating frequency ranging from 99 to 117 Hz.
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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 | 0.001 | 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".