Piezoelectric Energy Harvesting Array of Tethered Bodies Utilizing Flow-Induced Vibrations
Bibliographic record
Abstract
Abstract This paper investigates a mechanical system utilizing flow-induced vibration (FIV) of oscillating bodies in a steady flow. Multiple tethered bluff bodies are elastically mounted and arranged in three different staggered configurations. The hydrokinetic steady water flow is converted to mechanical oscillations via FIV, and the vibration energy is further converted to electrical energy by piezoelectric strips. To achieve the optimal design of the oscillating system, a series of experiments are performed to examine the effect of the optimal spacing between the oscillating bodies. Furthermore, the effect of the flow Reynolds number is investigated in the laboratory conditions for each configuration. The amplitudes and frequency of the oscillations of the mechanical system are analyzed from the measured voltage with the use of a data acquisition system. Furthermore, information for the dominant frequencies, spectra, and power levels is extracted from each individual body to examine the sheltering effect and estimate the power generated by each array configuration. The measured oscillations show that the sheltering effect is greater in the array’s center than at the edges. Moreover, the current experiments showed that the vibrational characteristics of each tethered body are close to the lock-in branch, where the body’s natural frequency matches the shedding frequency as closely as possible.
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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".