An Improved Smooth Rotation Correction for the Spalart–Allmaras Turbulence Model for Better Off-Body Vortex Prediction and Vortex–Vortex Interaction Effects
Bibliographic record
Abstract
This work presents a new modification of the Spalart–Allmaras (SA) turbulence model, named SA-R23, to improve the capture of off-body vortices in the flow by means of a smooth Rotation correction. The new rotation correction has the same basics as the one of Dacles-Mariani et al. (SA-R) (Dacles-Mariani et al. 1995. “Numerical/experimental Study of a Wingtip Vortex in the near Field.” AIAA Journal 33 (9): 1561–1568), but has more favourable numerical properties especially for high-order Navier–Stokes solvers. The key mission of the -R models is to prevent the intense dissipation of the cores free vortices by the Spalart–Allmaras model; other eddy-viscosity models have similar issues. This makes Delta and similar wing configurations excellent cases for validation, which is facilitated by recent three-dimensional experimental measurement systems. The tightened vortices also have a favourable impact on commercial-airplane high-lift systems, helicopter and open-rotor blade-vortex interactions, vortex generators, cavitation and contrails. This paper also shows the ability of metric-based anisotropic mesh adaptation combined with the -R models to accurately capture complex off-body flow physics. The approach is challenged on a particularly complex test case: a generic military aircraft with varying leading-edge sweep at high angle of attack. Detailed flow-field comparisons are shown, as well as the mesh convergence of aero coefficients and force polars.
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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 | 0.001 |
| 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 | 0.001 |
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".