Comparative Study of the Blade Number and Airfoil Profile Impacts on the Twist/Chord Distribution of a Small Wind Turbine Blade
Bibliographic record
Abstract
The blade number and airfoil profile effects on the blade shape of a small horizontal-axis wind turbine (SHWT) were investigated. For this purpose, the NACA4412, SG6042, and SG6043 airfoils, as well as 2, 3, and 4 blades, were considered. Then, two optimization processes were used: first, the blades were designed to maximize the power coefficient ( C p ), and then a multiobjective optimization that included both maximizing C p and maximizing the starting torque ( Q s ) was employed. The differential evolution (DE) algorithm was employed to perform the optimization, and the blade element momentum aerodynamic approach was used to conduct the relevant computations. Also, to ensure the performance of the optimal blades, the computational fluid dynamics method was employed as well. The findings revealed that regardless of the number of blades and the type of airfoil, raising the twist angle ( θ p ) and chord length ( c ) along the radial direction of the blade, especially at the root part, helps increase the Q s . It was observed that increasing the number of blades does not have a significant effect on the θ p distribution of the selected airfoils, but the c of the blades fitted with all three airfoils decreases. Regardless of the number of blades, while the geometry of blades utilizing the NACA4412 and SG6042 airfoils are close to each other, the blade with the SG6043 airfoil has the shortest c , which reduces the generated Q s of blades fitted with this airfoil. The results also establish that by increasing the number of blades from 2 to 3, the power coefficient ( C p ) of the blades fitted with all three airfoils increases, but by further increasing the number of blades from 3 to 4, the change in C p completely depends on the airfoil profile.
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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".