Comparison of a Correlation-Based Transitional Model Coupled to SA and kw-SST Turbulence Models
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3973.vid In the present work, the Spalart-Allmaras (SA) turbulence model is compared with the Menter SST two-equation turbulence model from 2003 (SST-2003) while both models are coupled with the gamma-ReThetaT local correlation-based transition model. These three models were implemented in the unstructured finite volumes compressible RANS code CHAMPS. Modification to the Fonset parameters is investigated as a calibration potential. Smoothing and alternative equations from the literature are implemented for Fonset, ReThetaC and Flength to remove the non-differentiable terms and improve residual convergence. Both turbulence models are validated on 2D test cases for different transition mechanisms and flow conditions: the Schubauer and Klebanov and T3A flat plate cases and the NACA0012, S809 and NLF0416 airfoils cases. Results are compared to experimental data and some results from the literature. Both models can capture the transition accurately, but numerical and experimental uncertainties remain high for natural transition cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".