Aerodynamic optimal design of wind turbine blades using genetic algorithm
Bibliographic record
Abstract
Wind power has been widely considered and utilized in recent years as one of the most promising renewable energy sources. In the current research study, aerodynamic analysis of the upwind three-bladed horizontal axis turbine is carried out using blade element momentum theory (BEM), and a genetic algorithm (GA) is applied as an optimization method. Output power generation is considered as an objective function, which is one of the most common choices of objective function. The optimization variables also involve chord and twist distribution variations and the placement of the airfoil sections along the blade length. The optimal blade shape is investigated for the maximum output power at a specific wind speed, rotor diameter and airfoil profile. The modified BEM results are compared with an experimental measurement indicating reliable results. The results show that considering placement of the airfoils as design variables in addition to chord and twist rate has a significant effect on the optimal output power. Finally, the objective function (output power) is improved by 10% compared to the base function.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".