Investigation of the Performance of Turbulence Models With Respect to High Flow Curvature in Centrifugal Compressors
Bibliographic record
Abstract
The goal of this research is to evaluate the performance of three turbulence models with respect to flow with high curvature in a centrifugal compressor stage designed for an aero-engine. The effectiveness of the curvature correction terms in the two-equation turbulence models is the main focus of this study, as implemented in the curvature-corrected shear stress transport (SST-CC) model of Smirnov and Menter. The SST-CC model uses a production multiplier in the k and ω equations. SST-CC results were compared against the SST model and previous simulations by Bourgeois et al. (2011, “Assessment of Turbulence Model Predictions for an Aero-Engine Centrifugal Compressor,” ASME J. Turbomach., 133(1), pp. 1–15) using the Reynolds stress model (RSM–SSG) for stage performance characteristics, experimental velocity profiles at the impeller–diffuser interface, and velocity contours at the diffuser exit. The production multiplier was investigated in the compressor impeller. The comparisons showed that the SST-CC model better predicted the choke region in the pressure characteristic and efficiency characteristic, whereas the SST model better predicted the stall region. However, both models predicted a similar mean flow velocity field. Analysis of the production multiplier demonstrated that the term provided the expected effects near the walls of the convex and concave surfaces. However, away from the walls where turbulent production term was insignificant, the production multiplier showed abnormal predictions. The rotation effects were found to be weaker than the curvature effects near the impeller trailing edge of the current compressor.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".