Assessment of Turbulence Model Predictions for an Aero-Engine Centrifugal Compressor
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Bibliographic record
Abstract
The accurate prediction of mean flow fields with high degrees of curvature, adverse pressure gradients, and three-dimensional turbulent boundary layers typically present in centrifugal compressor stages is a significant challenge when applying Reynolds averaged Navier–Stokes turbulence modeling techniques. The current study compares the steady-state mixing plane predictions using four turbulence models for a centrifugal compressor stage with a tandem impeller and a “fish-tail” style discrete passage diffuser. The models analyzed are the k-ε model (an industry standard for many years), the shear stress transport (SST) model, a proposed modification to the SST model denoted as the SST-reattachment modification (RM), and the Speziale, Sarkar, and Gatski Reynolds stress model (RSM-SSG). Comparisons with measured performance parameters—the stage total-to-static pressure and total-to-total temperature ratios—indicate more accurate performance predictions from the RSM-SSG and SST models as compared to the k-ε and SST-RM models. Details of the different predicted flow fields are presented. Estimates of blockage, aerodynamic slip factor, and impeller exit velocity profiles indicate significant physical differences in the predictions at the impeller-diffuser interface. Topological flow field differences are observed: the separated tip clearance flow is found to reattach with the SST, SST-RM, and RSM-SSG models, while it does not with the k-ε model, a larger shroud separation at the impeller exit seen with the SST and SST-RM models, and core flow differences are in the complex curved diffuser geometry. The results are discussed in terms of the production and dissipation of k predicted by the various models due to their intrinsic modeling assumptions. These comparisons will assist aerodynamic designers in choosing appropriate turbulence models, and may benefit future modeling research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it