Numerical Study using RANS model to Predict Loading of a Wind Turbine Blade with a Trailing Edge Flap
Bibliographic record
Abstract
Abstract A numerical study was conducted with a 3D steady state Computational Fluid Dynamics (CFD) model using k-ω SST at SATP conditions for air. The mesh geometry is based on a S833 airfoil with a trailing edge flap to predict the lift and moment loads. This airfoil is representative of a wind turbine blade. A set of static CFD simulation cases were conducted with a free stream velocity of 29 m/s at an angle of attack (α) between -4° and 20°. A trailing edge flap is also present on the airfoil which was operated between an angle (αf) of -20° and 20°. The airfoil has a chord of 178 mm with the trailing edge flap covering 20% of the length and chord-based Reynolds number of 350,000. The CFD results were compared against an experimental study performed in a closed-loop wind tunnel at the University of Waterloo. The wind tunnel has a contraction ratio of 9:1 and a cross section of 0.61 m square. The experimental data showed that lift forces increased at a larger angle of attack and larger downward flap angle up until stall. Moment on the blade is relatively constant regardless of the angle of attack, however, increases significantly with upward flap angle. The CFD results showed very similar results in both the lift and moment coefficient, with all angle configurations having over 90% agreeability excluding α near +/- 20°.
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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".