Investigation and Optimization of Blade Tip Winglets Using an Implicit Free Wake Vortex Method
Bibliographic record
Abstract
Novel outer-blade geometries such as tip winglets can increase the aerodynamic power that can be extracted from the wind by tailoring the relative position and strengths of trailed vorticity. This design space is explored using both parameter studies and gradient-based optimization, with the aerodynamic analysis carried out using LibAero, a free wake vortex-based code introduced in previous work. The starting design is the NREL 5MW reference turbine, which allows comparison of the aerodynamic simulation for the unmodified blade with other codes. The code uses a Prandtl-Weissinger lifting line model to represent the blade, and vortex filaments as the flow elements. A fast multipole method is implemented to accelerate the influence calculations and reduce the computational cost. This results in higher fidelity aerodynamic simulations that can capture the effects of novel geometries while maintaining sufficiently fast run-times (on the order of an hour) to allow the use of optimization. Gradients of the objective function with respect to design variables are calculated using the complex step method which is accurate and efficient. Since the vortex structure behind the rotor is being resolved in detail, insight is also gained into the mechanisms by which these new blade designs affect performance. It is found that adding winglets can increase the power extracted from the wind by around 2%, with a similar increase in thrust. It is also possible to create a winglet that slightly lowers the thrust while maintaining very similar power compared to the standard straight blade.
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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 | 0.001 |
| 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".