Multi-Objective Optimization and Optimal Airfoil Blade Selection for a Small Horizontal-Axis Wind Turbine (HAWT) for Application in Regions with Various Wind Potential
Bibliographic record
Abstract
The type of airfoil with small wind turbine blades should be selected based on the wind potential of the area in which the turbine is used. In this study, 10 low Reynolds number airfoils, namely, BW-3, E387, FX 63-137, S822, S834, SD7062, SG6040, SG6043, SG6051, and USNPS4, were selected and their performance was evaluated in a 1 kW wind turbine in terms of the power coefficient and also the startup time, by performing a multi-objective optimization study. The blade element momentum technique was utilized to perform the calculations of the power coefficient and startup time and the differential evolution algorithm was employed to carry out the optimization. The results reveal that the type of airfoil used in the turbine blade, aside from the aerodynamic performance, completely affects the turbine startup performance. The SG6043 airfoil has the highest power coefficient and the BW-3 airfoil presents the shortest startup time. The high lift-to-drag ratio of the SG6043 airfoil and the low inertia of the turbine blades fitted with the BW-3 airfoil make them suitable for operation in windy regions and areas with low wind speeds, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".