Effect of tip speed ratio on the aerodynamic noise of a small wind turbine: An optimization study
Bibliographic record
Abstract
Abstract A dual-objective optimization study was carried out to illuminate how the tip speed ratio of a small wind turbine affects the aerodynamic noise as well as the blade geometry. A 0.75 kW three-bladed small horizontal axis wind turbine was selected as the case study. Two important goals were considered in the study: maximization of the output power and the minimization of the aerodynamic noise. The former was calculated by the well-known blade-element momentum theory while a validated semi-empirical model was adopted for the computation of the latter. A combination of these goals defined the objective function and the weighted-sum approach was employed to find the optimal values for the design variables of the optimization including the distributions of the chord and the twist angle along the blade together with the tip speed ratio. The extracted power was calculated at the rated wind speed of 10 m/s while the noise was computed at lower wind speeds of 5 and 7.5 m/s. The genetic algorithm technique was adopted for the muti-objective optimization. The results revealed the importance of the blade tip region in producing the emitted aerodynamic noise. Specifically, an increase in the chord and twist values near the tip results in the reduction in the emitted noise. Results also show that a good compromise between the two goals is achievable such that a noticeable reduction in the emitted aerodynamic noise is attainable in exchange for a very small drop in the power coefficient. The optimization results also indicated that the noise could be adequately reduced at the smaller value of the tip speed ratio without a large reduction in output power which is due to the decrease in the rotational speed of the turbine.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".