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 ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msub> <a:mrow> <a:mi>C</a:mi> </a:mrow> <a:mrow> <a:mi>p</a:mi> </a:mrow> </a:msub> </a:math> ), and then a multiobjective optimization that included both maximizing <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:msub> <c:mrow> <c:mi>C</c:mi> </c:mrow> <c:mrow> <c:mi>p</c:mi> </c:mrow> </c:msub> </c:math> and maximizing the starting torque ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:msub> <e:mrow> <e:mi>Q</e:mi> </e:mrow> <e:mrow> <e:mi>s</e:mi> </e:mrow> </e:msub> </e:math> ) 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 ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:msub> <g:mrow> <g:mi>θ</g:mi> </g:mrow> <g:mrow> <g:mi>p</g:mi> </g:mrow> </g:msub> </g:math> ) and chord length ( <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>c</i:mi> </i:math> ) along the radial direction of the blade, especially at the root part, helps increase the <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:msub> <k:mrow> <k:mi>Q</k:mi> </k:mrow> <k:mrow> <k:mi>s</k:mi> </k:mrow> </k:msub> </k:math> . It was observed that increasing the number of blades does not have a significant effect on the <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M7"> <m:msub> <m:mrow> <m:mi>θ</m:mi> </m:mrow> <m:mrow> <m:mi>p</m:mi> </m:mrow> </m:msub> </m:math> distribution of the selected airfoils, but the <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" id="M8"> <o:mi>c</o:mi> </o:math> 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 <q:math xmlns:q="http://www.w3.org/1998/Math/MathML" id="M9"> <q:mi>c</q:mi> </q:math> , which reduces the generated <s:math xmlns:s="http://www.w3.org/1998/Math/MathML" id="M10"> <s:msub> <s:mrow> <s:mi>Q</s:mi> </s:mrow> <s:mrow> <s:mi>s</s:mi> </s:mrow> </s:msub> </s:math> of blades fitted with this airfoil. The results also establish that by increasing the number of blades from 2 to 3, the power coefficient ( <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" id="M11"> <u:msub> <u:mrow> <u:mi>C</u:mi> </u:mrow> <u:mrow> <u:mi>p</u:mi> </u:mrow> </u:msub> </u:math> ) of the blades fitted with all three airfoils increases, but by further increasing the number of blades from 3 to 4, the change in <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" id="M12"> <w:msub> <w:mrow> <w:mi>C</w:mi> </w:mrow> <w:mrow> <w:mi>p</w:mi> </w:mrow> </w:msub> </w:math> completely depends on the airfoil profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".