Assessment of the sensitivity of high-lift airfoil performance to flap gap and overlap
Bibliographic record
Abstract
• A detailed parametric study investigates the effects of flap gap and overlap on high-lift airfoil performance. • CFD simulations using the SST turbulence model show good agreement with experimental PIV data. • Increasing flap gap and overlap leads to reduced lift and higher drag, with overlap having the greater impact. • Lift enhancement is strongly correlated with suction peak pressure, slot jet velocity, and slot length. • Slot flow momentum improves wake mixing but increases flap recirculation due to slot geometry. A numerical investigation and experimental validation of the flow dynamics of a high-lift airfoil were performed to develop a numerical model for an extended parametric study. Although Computational Fluid Dynamics (CFD) has become a valuable design tool for the aerospace community in the quest for improved wing design, several challenges remain in accurately predicting the flow field of a stalled multi-element airfoil owing to inadequate modelling choices and insufficient grid resolution. In this study, the commercial solver ANSYS Fluent was used to conduct a parametric study by varying the flap gap and overlap of a Fowler flap with a flap deflection of 40° and an angle of attack of 0° The results showed that the velocity, vorticity, and turbulence intensity fields predicted by the 2D steady-state RANS solutions using Menter's SST turbulence model were in good agreement with the experimental PIV data. The parametric study revealed that the wing performance decreased as the slot length increased, which was attributed to the aggravation of flap stall. An empirical correlation relating lift performance to flow and geometric parameters was developed, providing a simplified predictive framework for the considered angle of attack and flap deflection.
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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".