Effect of Freestream Turbulence on Airfoil Limit-Cycle Oscillations at Transitional Reynolds Numbers
Bibliographic record
Abstract
Numerical simulations are performed to study the effect of freestream turbulence on small-amplitude limit-cycle oscillations of an airfoil at transitional Reynolds numbers. A one-degree-of-freedom aeroelastic model was coupled with the National Research Council Canada in-house computational-fluid-dynamics code INSflow to perform unsteady Reynolds-averaged Navier–Stokes simulations for flows around a rigid NACA 0012 airfoil in free-to-rotate conditions. Without coupling a transition model, unsteady Reynolds-averaged Navier–Stokes computations based on the commonly used shear-stress-transport turbulence model could not capture the limit-cycle oscillations. This was expected because it had been previously shown that the limit-cycle oscillations were fed by negative aerodynamic damping due to laminar boundary-layer separation. A correlation-based transition model was then implemented in the code and applied to investigate the turbulence effects. The computed results confirmed qualitatively the experimental observations of the turbulence effects on the limit-cycle oscillations by varying the values of the turbulence intensity and length scale. It was observed that the limit-cycle oscillations were more sensitive to the turbulence intensity than to the latter. In general, the freestream turbulence suppressed the limit-cycle oscillations. The pitch amplitude became smaller when the freestream turbulence intensity was increased, which was qualitatively in good agreement with experimental results. The limit-cycle oscillations could not be sustained when the input freestream turbulence level reached around 1.25%. An examination of the unsteady flowfield revealed that the freestream turbulence affected the location of the separation points and suppressed the laminar–turbulent transition process.
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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".