Design and Development of Bladeless Vibration-Based Piezoelectric Energy–Harvesting Wind Turbine
Bibliographic record
Abstract
To meet the growing energy demand and increasing environmental concerns, clean and renewable fluid energy, such as wind and ocean energy, has received considerable attention. This study proposes a bladeless wind energy–harvesting device based vortex-induced vibrations (VIV). The proposed design is mainly composed of a base, a hollow mast, and an elastic rod. The proposed design takes advantage of vortices generated when the airflow interacts with the mast, and the flow splits and then separates and generates vortices that eventually make the elastic rod oscillate, and out of these oscillations, energy can be harvested. Different airflow disruption geometries are studied and tested numerically and experimentally to identify the most effective shape and orientation for converting wind energy to electric energy. Computational fluid dynamics (CFD) modeling and simulations were performed on the elastic mast, a VIV device’s core wind energy–collecting component, to guide the device’s design. These simulations examined the mast-produced lift coefficient, velocity, pressure, and vorticity contours of different mast geometries. The mast’s vibration energy under different wind intensities was also experimentally tested using a scaled model in the wind tunnel. The level of converted electric power was measured and monitored using piezoelectric sensors mounted at different locations on the mast. The experimental study identified the ideal orientation angle of the mast and the best location for the piezoelectric sensors for harnessing more energy. The experiments confirmed the CFD simulation results that a complex cylinder design produces more power. The combined numerical and experimental studies led to an environmentally friendly, new VIV design with much improved power generation capabilities.
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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 | 0.001 | 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".