Large-eddy simulation of vortex interaction in pitching-fixed tandem airfoils
Bibliographic record
Abstract
In this study, the interaction of vortices generated from an oscillating airfoil with a hindfoil placed downstream of the oscillating forefoil at low-Reynolds-number flow was investigated numerically. The forefoil entered a deep dynamic stall induced by large-amplitude pitching oscillation. The dynamic stall process is characterized by unsteady separation and the formation of a strong clockwise vortex. A wall-resolved large-eddy simulation approach was applied to compute the flowfield. The numerical measurements were performed for an incompressible flow at a Reynolds number of Re = 30 000 based on chord length with a pitching reduced frequency of K= 0.5, and amplitude of A = 14.1° over Selig–Donovan 7003 airfoils. A single-airfoil case was validated against numerical and experimental measurements. In the present study, we investigated the flowfield and aerodynamic coefficients resulting from the deep dynamic stall of the pitching forefoil and the vortex interaction in tandem-airfoil configuration related to micro-air vehicle applications by employing large-eddy simulation approach. Large-eddy simulation was also compared to two-dimensional unsteady Reynolds-averaged Navier–Stokes simulation to determine the accuracy and validity of the low-fidelity approach in prediction of deep dynamic stall and vortex interaction at low-Reynolds-number flow.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".