Numerical and experimental investigation of Darrieus vertical axis wind turbines to enhance self-starting at low wind speeds
Bibliographic record
Abstract
Wind energy, being renewable, cost-effective, and environmentally friendly, has attracted global attention. However, due to suboptimal performance and limited research, vertical axis wind turbines (VAWTs) lag behind horizontal axis wind turbines (HAWTs) in commercial applications, particularly for large-scale installations. This study aims to improve the self-starting capability of the Darrieus VAWT. While some parameters, such as the number of blades (N) and solidity (σ), have been studied extensively, the airfoil shape has not received as much attention. This study compares the performance of National Advisory Committee for Aeronautics (NACA) airfoils and Selig airfoils at a Reynolds number (Re) of 40,673. The investigation revealed that the NACA0015 airfoil exhibited the highest peak power coefficient ( C p ). Further analysis utilizing an advanced double multiple stream tube (DMST) code in MATLAB increased the peak C p by adjusting the thickness-to-camber ratio (t/c) of the NACA0015 airfoil, resulting in a 12.50 % increase in the maximum achievable C p at a Re of 40,673. This study compared four modes of VAWT operation, utilizing the NACA0015 airfoil and a modified NACA0015 airfoil for both straight-bladed and embossed-bladed VAWTs. The results showed that the modified NACA0015 airfoil for embossed-bladed VAWTs exhibited the best self-starting capability and rotation at wind velocities of 1 to 9 m s -1 . Additionally, the self-starting force required by embossed-bladed VAWTs was lower than that needed by straight-bladed VAWTs due to the ability of the embossed material to enhance airflow attachment to the VAWT and suppress turbulence.
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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".