Control of the separated flow around an airfoil using a wavy leading edge inspired by humpback whale flippers
Bibliographic record
Abstract
The influence of spanwise geometrical undulations of the leading edge of an infinite wing is investigated numerically at low Reynolds number, in the context of passive separation control and focusing on the physical mechanisms involved. Inspired by the tubercles of the humpback whale flippers, the wavy leading edge is modeled using a spanwise sinusoidal function whose amplitude and wavelength constitute the parameters of control. A direct numerical simulation is performed on a NACA0020 wing profile in a deep stall configuration ( α = 20 ° ), with and without the presence of the leading edge waviness. The complex solid boundaries obtained by varying the sinusoidal shape of the leading edge are modeled using an immersed boundary method (IBM) recently developed by the authors [Pinelli et al., J. Comput. Phys. 229 (2010) 9073–9091]. A particular set of wave parameters is found to change drastically the topology of the separated zone, which becomes dominated by streamwise vortices generated from the sides of the leading edge bumps. A physical analysis is carried out to explain the mechanism leading to the generation of these coherent vortical structures. The role they play in the control of boundary layer separation is also investigated, in the context of the modifications of the hydrodynamic performances which have been put forward in the literature in the last decade.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".