Evaluation of turbulence models for unsteady separation
Bibliographic record
Abstract
Unsteady separation is a phenomenon that occurs in many flows due to time-varying adverse pressure gradients and results in increased drag, decreased lift, and loss of efficiency or failure in flow devices. Therefore, it is important to predict and analyze unsteady separation. Turbulence models for the RANS equations are commonly used in the industrial design process due to their low computational cost; however, their performance in predicting steady separations is unsatisfactory, and very few studies investigate unsteady separation. Our goal is to use high-fidelity Large-Eddy Simulation results to evaluate the accuracy of the K - W, K - E, and Spalart Almaras turbulence models in unsteady separation. By using an identical grid, numerical scheme and consistent boundary conditions to the LES calculations we are able to isolate modelling errors. All three turbulence models capture the general features of this complex unsteady flow correctly, with only small discrepancies from the LES. Memory effects due to the periodic nature of the unsteadiness have been found to contribute to the model’s success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".