Numerical Predictions of Stanton Numbers, Skin Friction Coefficients, Aerodynamic Losses, and Reynolds Analogy Behavior for a Transsonic Turbine Vane
Bibliographic record
Abstract
Stanton numbers, skin friction coefficients, aerodynamic losses, and Reynolds analogy behavior are numerically predicted for a turbine vane using the FLUENT commercial code with a k–ϵ RNG model to show the effects of Mach number, mainstream turbulence level, and surface roughness. Test vane geometry, configuration, and flow conditions match ones from a practical application. Comparisons with experimental data on wake aerodynamic losses are made. Numerical and experimental results show that the magnitudes of integrated aerodynamics losses increase dramatically as the exit Mach number increases from 0.35 to 0.71. Downstream wakes are also widened as the mainstream turbulence intensity level, exit Mach number, or level of surface roughness increases. Deviations of numerically predicted 2 St/C f magnitudes from 2 St/C f ≅ 1 on the vane suction and pressure sides are also presented for a variety of flow conditions.
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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) | 0.000 | 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 | 0.001 | 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".