Numerical Analysis of the Kline and Fogleman Airfoil's Effect on the Operation of Straight Darrieus Wind Turbine
Bibliographic record
Abstract
The blade profile selection is paramount for the efficient operation of straight Darrieus wind turbines in terms of torque and power generation. In this work, we have used the Kline-Fogleman Airfoil (KFA) design for the wind turbine blades. The concept of KFA design aims to cause flow separation, vortex formation, and reattachment establishment before the trailing edge. Thus, geometric tests on have been performed on the baseline airfoil NACA0015 as one of the best profiles for operating a straight Darrius wind turbine. A two-dimensional Computational Fluid Dynamic (CFD) model using the two-equation Shear Stress Transport k-ω (SST k-ω) turbulent model was developed in ANSYS/FLUENT software to assess the aerodynamic efficiency of the modified airfoil. Two designs (KFA-2 and KFA-4) were tested initially in the static case. The effects of the opening step angle and its curvature diameter were studied for an angle of attack’s range of -20° to +20°. The rounded KFA-4 design with an opening step angle of 93.6° led to a significant improvement in the lift-to-drag ratio thus, aerodynamic efficiency. Finally, the straight KFA-4 design with the opening step angle of 93.6° revealed a the most advantageous effects on the operation of a straight Darrieus wind turbine for a Tip Speed Ratio less than 1.6 (TSR<1.6). It allowed a noticeable reduction of the dead zone and TSR corresponding to the nominal power, thus consequently improving the starting torque and delaying torque stall.
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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) | 0.000 | 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) | 0.001 | 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".