Self-Starting Characteristics and Flow-Induced Rotation of Single- and Dual-Stage Vertical-Axis Wind Turbines
Bibliographic record
Abstract
Despite offering promising opportunities for wind energy harvesting in urban environments, vertical axis wind turbines face limitations in terms of poor starting characteristics. In this study, we focus on analyzing improvements offered by dual-stage turbines for a range of wind velocities. Numerical simulations are performed for different phase angles between the rotors (a measure of relative angular positions of the blades in the two rotors) to quantify the response time for their starting behavior. These simulations rely on a through sliding mesh technique coupled with flow-induced rotations. We find that for U∞=4m/s, the phase angles of 30∘ and 90∘ substantially reduce starting time in comparison to a single-stage turbine. Dual-stage turbines with a phase angle of 90∘ exhibit similar or better starting behavior for other wind speeds. The phase angle of 0∘ in double-rotor turbines shows the poorest starting response. Moreover, it is revealed that stabilization of shear layers generated by the blades passing through the windward side of the turbine, vortex-entrapment by these rotating blades, and suppressing of flow structures in the middle of the wake enhance the capacity of VAWTs to achieve faster steady angular speed.
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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".