Computational Study of a Transonic Turbine Cascade: Validation and Comparison of Griding Approaches and Challenges
Bibliographic record
Abstract
This paper presents a computational study, with some experimental validation, of a low-turning transonic turbine cascade. A comparison is presented between the time-consuming and difficult to generate hexa-structured meshing approach, and the mostly automated tetra-unstructured meshing approach. The paper compares the predicted flow physics and losses, with discussion of the challenges in griding and convergence between both approaches. Computations were carried out using a commercial RANS solver (ANSYS CFX 12) using the Shear Stress Transport turbulence model, and the Gamma-Theta transition model. The computational domain encompassed a half blade span, and one blade pitch with periodic boundary conditions; griding for both approaches was done using ANSYS ICEM CFD. Computational results from both griding approaches were compared to corresponding experimental data. The outlet Mach number was 0.90. The experiment was carried out using a linear cascade in a blow-down type wind tunnel. Downstream seven-hole pressure probe measurements at 1.8 axial chord lengths from the leading edge provided loss, streamwise vorticity, and secondary kinetic energy distributions and integrated coefficient values. It was found that both griding approaches predicted similar downstream endwall flow structures to those observed in the experiment. The tetra-unstructured mesh solution predicted higher losses, but both predicted lower losses than the experiment. Overall results suggest that for capturing of the basic flow physics, both approaches suffice, with the tetra-unstructured being the much easier approach, but with limitations on the level of grid refinement. For more accurate capturing of the flow physics, the time-consuming and difficult to generate hexa-structured meshing approach can be justified.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".