Anisotropic Eddy Viscosity in the Secondary Flow of a Low-Speed Linear Turbine Cascade
Bibliographic record
Abstract
The final losses within a turbulent flow are realized when eddies completely dissipate to internal energy through viscous interactions. The accurate prediction of the turbulence dissipation, and therefore the losses, requires turbulence models which represent, as accurately as possible, the true flow physics. Eddy viscosity turbulence models, commonly used for design level computations, are based on the Boussinesq approximation and inherently assume the eddy viscosity field is isotropic. The current paper compares the computational predictions of the flow downstream of a low-speed linear turbine cascade to the experimentally measured results. Steady-state computational simulations were performed using ANSYS CFX v12.0 and employed the shear stress transport (SST) turbulence model with the γ-Reθ transition model. The experimental data includes measurements of the mean and turbulent flow quantities. Steady pressure measurements were collected using a seven-hole pressure probe and the turbulent flow quantities were measured using a rotatable x-type hotwire probe. Data is presented for two axial locations: 120% and 140% of the axial chord (Cx) downstream of the leading edge. The computed loss distribution and total bladerow losses are compared to the experimental measurements. Differences are noted and a discussion of the flow structures provides insights into the origin of the differences. Contours of the shear eddy viscosity are presented for each axial plane. The secondary flow appears highly anisotropic, demonstrating a fundamental difference between the computed and measured results. This raises questions as to the validity of using two-equation turbulence models, which are based on the Boussinesq approximation, for secondary flow predictions.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".