A perspective on turbulence models for aerodynamic flows
Why this work is in the frame
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Bibliographic record
Abstract
AbstractTurbulence modelling options are discussed in the context of steady aerodynamic flows. After a brief overview of popular turbulence models, four criteria are presented that should be satisfied in order to conclusively evaluate a turbulence model with respect to its ability to predict a specific flow. Many past studies do not meet these criteria. This is followed by some sample results for several turbulence models, including one-equation, two-equation and algebraic Reynolds stress models. The three main conclusions are as follows. First, more combined experimental–numerical studies are needed that meet the four criteria for assessment of turbulence models. Second, of the models studied, the Spalart-Allmaras model provides the most accurate results for the high-lift flows examined. Finally, the most significant factor limiting our present ability to predict many aerodynamic flows accurately is our inability to reliably predict laminar-turbulent transition. Until this issue is addressed, the benefits of an improved turbulence model will be limited.Keywords: turbulence modelsaerodynamicscomputational fluid dynamicsReynolds-Averaged Navier-Stokes equationsturbulent flowsmulti-element airfoilshigh liftalgebraic Reynolds stress models
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it