Operating the Spalart-Allmaras turbulence model under high free stream turbulence
Bibliographic record
Abstract
The intent of this work is to investigate and control the numerical behavior of the Spalart-Allmaras one-equation turbulence model under high free stream turbulence (FST). The primary motivation is gas turbines. Two research cases are considered: the two-dimensional zero-pressure gradient flat plate and the subsonic two-dimensional flow past a NACA0021 airfoil. Some improvements of the SA model are proposed to take into account the influence of the free stream turbulence on the boundary-layer features as well as on the aerodynamic coefficients. The improvements include modifications to the equations, as well as altered inflow boundary conditions for the eddy viscosity, and are fairly easy to implement (we do not yet have an adjustable version for different FST levels). With high inflow values, the system of equations creates an eddy-viscosity boundary layer outside the momentum boundary layer, for which a theory akin to that of Blasius is developed and fully validated by numerical results. A combination of the modified model equation and high inflow values brings the velocity profiles on the flat plate close to the experimental results. These high inflow values are at the discretion of the user, however the destruction term of the SA model drastically weakens their penetration into the boundary layer, so that the skin friction displays only a very weak sensitivity to them. This very much reduces the burden on the CFD user. Modeling moderate FST will require relatively simple extensions of the present approach.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".