Drag Force Balance of a Blunt and Divergent Trailing-Edge Airfoil
Bibliographic record
Abstract
M odifying the trailing edge of an airfoil can lead to significant improvements in its aerodynamic performance. Divergent trailing-edge (DTE) airfoils were first introduced to improve the liftto-drag ratio of supercritical airfoils at cruise conditions for commercial aircraft [1]. They are a natural evolution of Gurneyflaps used on low-speed airfoils to increase airfoil lift and reduce profile drag [2]. DTE airfoils are characterized by upper and lower surfaces that diverge from each other over about the aft 30% of chord forming a blunt trailing. This DTE approach was extended to low-speed applications such as wind turbine blades [3]. DTE profiles on wind turbine blades can improve aerodynamic performance and structural strength compared to profiles with sharp trailing edges. Critical to enhancing the aerodynamic performance of these airfoils and optimizing their geometrical configuration is the accurate characterization of surface flow properties as well as the precise determination of their aerodynamic characteristics. The profile drag of airfoils can be determined using three different approaches: dynamic force balance, wake surveys, and direct measurements of the two drag components, that is, skin friction and pressure drag. Direct measurements of drag components along the airfoil surface also provide useful information for characterizing the surface flow (i.e., the boundary-layer state). However, the accuracy of the available direct skin-friction measurement methods was previously questioned [4]. Recently, the oil film interferometry method that relies on local thinning of an oil film under shear stress has been proven to accurately measure skin friction [5]. In the present study, direct measurements of both surface pressure and skin friction are employed to 1) characterize the physics of the surface flow around a DTE airfoil, and 2) estimate accurately the profile drag from direct measurements and compare it with that estimated from a survey in the airfoil far wake.
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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".