Turbulence Intensity Effects on Laminar Separation Bubbles Formed over an Airfoil
Bibliographic record
Abstract
The effects of freestream turbulence intensity on the mean topology and transition characteristics of laminar separation bubbles forming over the suction side of a NACA 0018 airfoil are investigated experimentally for angles of attack between 0 and 20 deg, chord Reynolds numbers between 100,000 and 200,000, and freestream turbulence intensities between 0.09 and 2.03%. The results show that increasing freestream turbulence intensity results in earlier transition and reattachment, contributing to an overall decrease in separation bubble length. At lower angles of attack, this is accompanied by a minor decrease in lift, whereas at prestall angles of attack and higher turbulence intensity levels, lift increases and stall is delayed. Spatial amplification rates of disturbances in the separated shear layer are shown to decrease at elevated levels of turbulence intensity, indicating that the earlier transition is attributed solely to the larger initial amplitude of perturbations. At elevated turbulence intensity levels, a broader range of unstable frequencies is detected in the separated shear layer, with the central frequency of the unstable band showing moderate variations with the level of freestream perturbations. The results indicate a change in the transition process at higher freestream turbulence intensity levels, indicative of bypass transition in the boundary layer. The degree of influence of freestream turbulence intensity on the separation bubble is shown to decrease as the chord Reynolds number is increased.
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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 | 0.000 | 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 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".