Validation of Helicity-Corrected Spalart-Allmaras Model for Corner Separation Prediction in Incompressible Flow with OpenFOAM
Bibliographic record
Abstract
Steady Reynolds-averaged Navier-Stokes (RANS) computations save significant computational resources compared to unsteady RANS and large eddy simulation (LES). However, the ability of most RANS models to accurately predict flow separation in compressor/fan blade rows is limited. Recent research has focused on reducing the computational cost of predicting compressor/fan stall points with steady computations has shown that the helicity-corrected Spalart-Allmaras (SA) turbulence model is able to avoid over-predicting corner separations and thus lead to converged RANS up to the actual stall point. To date, this model has mostly been implemented in in-house codes or in commercial codes as a user add-on, where the source code is not available. In a recent paper, the authors implemented the helicity-corrected SA model in OpenFOAM, an open-source CFD package. In this paper, the differences in the flow field for RANS solutions with the original SA model, the helicity-corrected SA model, and Menter’s shear stress transport (SST) model are highlighted for a linear cascade with incompressible flow. A NACA 65-1810 cascade is used, and computational results are compared to experimental data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".