Assessment of Turbulence Model Predictions for an Aero-Engine Centrifugal Compressor
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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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".