Improving Airfoil Drag Prediction
Bibliographic record
Abstract
An improved formulation of drag estimation for thick airfoils is presented.Drag underprediction in XFOIL like viscous-inviscid interaction methods can be quite significant for thick airfoils used in wind turbine applications (up to 30% as seen in the present study).The improved drag formulation predicts the drag accurately for airfoils with reasonably small trailing edge thickness.The derivation of drag correction is based on the difference between the actual momentum loss thickness based on free stream velocity and the one based on the velocity at the edge of the boundary layer.The improved formulation is implemented in the most recent version of XFOIL and RFOIL (an aerodynamic design and analysis method based on XFOIL, developed by a consortium of ECN, NLR and TU Delft after ECN acquired the XFOIL code.After 1996, ECN maintained and improved the tool.) and the results are compared with experimental data, results from commercial CFD methods like ANSYS CFX and other methods like DTU-AED EllipSys2D and CENER WMB.The improved version of RFOIL shows good agreement with experimental data. Nomenclatureα Angle of attack ∆θ Error in θ δ Boundary layer thickness δ * Boundary layer displacement thickness ∞ Subscript for incident free stream condition ρ Density of fluid θ Boundary layer momentum thickness ξ, η Streamline space coordinates A, B G -β equilibrium locus coefficients airf oil Subscript for airfoil parameters c Airfoil chord length C τ EQ Equilibrium maximum shear stress coefficient c d Sectional drag coefficient c l Sectional lift coefficient D Drag e Subscript for boundary layer edge condition
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".