Blade height impact on self-starting torque for Darrieus vertical axis wind turbines
Bibliographic record
Abstract
Self-starting torque ( T Self - s t a r t i n g ) presents a significant challenge for Darrieus vertical axis wind turbines (DVAWTs), often necessitating external assistance to initiate rotation. This study addresses the issue by optimizing airfoil design, employing embossed blades (EBs), and adjusting blade height ( H ) to reduce T Self - s t a r t i n g . From an analysis of 43 rotors at a chord-based Reynolds number ( Re c ) of 45,192, national advisory committee for aeronautics (NACA) 0015, NACA4412, and NACA4415 rotors were selected for their superior power coefficients ( C p ). These rotors were optimized using double-multiple streamtube theory (DMST) and particle swarm optimization (PSO), focusing on the thickness-to-chord ratio (TCR). Among them, the NACA0015-Opt rotor achieved the highest C p , demonstrating its effectiveness in enhancing DVAWT efficiency. This study also investigates the effect of H on the performance of EBs, comparing H of 35 cm and 75 cm. Experimental findings reveal that combining airfoil optimization with EBs, along with an increased H , leads to a substantial decrease in T Self - s t a r t i n g . Specifically, higher H enhance the aerodynamic performance of EBs by improving airflow over the blade surface, further reducing drag and contributing to a significant reduction in T Self - s t a r t i n g . At a H of 75 cm, the embossed blade Darrieus vertical axis wind turbine (EB-DVAWT) equipped with the optimized NACA0015-Opt rotor required 15.92 %, 17.04 %, 18.12 %, 21.23 %, 52.06 %, 49.23 %, 51.25 %, 35.20 %, 14.12 %, and 9.09 % less T Self - s t a r t i n g at wind velocities ( U ∞ ) of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 9.5 m/s, respectively, compared to the baseline smooth blade Darrieus vertical axis wind turbine (SB-DVAWT) with the original NACA0015 rotor.
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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".