The effect of drag force on the body frequencies and the power spectrum of a bladeless wind turbine
Bibliographic record
Abstract
New types of bladeless wind turbines and electricity generation are currently one of the most interesting topics in engineering. Electricity generation using structural vibration due to the resonance phenomenon is the concept behind a vortex bladeless turbine. The present study numerically investigated the effects of the drag force on the body frequency of an oscillating bladeless wind turbine. A two-dimensional numerical simulation was performed for a cylinder with a semi-circular cross-flow cross-section in two different cases. This research was conducted for both uncontrolled and controlled oscillating cylinders. The controlling process was performed using a pair of ring magnets as springs with a variable coefficient. The flow field, vibration, vortex shedding, structural frequencies, and resonance phenomena were studied in this research. Finally, the controlled and uncontrolled frequencies of the cylinder are explored, and the power spectra for various velocities are analyzed in two different states, namely, with and without a tuning system. From the results, it can be concluded that the usage of the controlling system in these turbines can significantly regulate the oscillations and increase the frequency value by limiting the vibration amplitude. According to this principle, it can be inferred that increasing the frequency of fluctuations greatly increases the production capacity of these turbines.
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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 | 0.001 |
| 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".