Airfoil Stall modeling via Roughness Amplification Model Coupled to Correlation-Based Transition Model
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3534.vid This paper investigates the effect of surface roughness on the aerodynamic performance of airfoils using two different turbulence models. A roughness amplification model (Ar), originally designed for the k-w-gamma-ReThetaT local correlation-based transition model, is coupled to the Spalart-Allmaras SA-gamma-ReThetaT transition model to better capture the impact of surface roughness on aerodynamic characteristics. The Aupoix SA roughness extension is utilized as the roughness boundary condition required in the Ar model. The models are implemented in CHAMPS, an unstructured finite volume compressible RANS code. Three airfoils, namely the NACA0009, NACA23018, and NACA23012, are used to evaluate the performance of the models and the results are compared with experimental data. The lift and drag curves are accurately predicted by both models in the pre-stall region, but the maximum lift is slightly overestimated. Overall, the SA-gamma-ReThetaT model better reproduces the effect of roughness than the k-w-gamma-ReThetaT model. Further research should focus on improving the accuracy of the models in predicting stall behavior, notably with careful calibration of the model constants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".