Numerical Predictions of Stanton Numbers, Skin Friction Coefficients, Aerodynamic Losses, and Reynolds Analogy Behavior for a Transsonic Turbine Vane
Why this work is in the frame
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Bibliographic record
Abstract
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. The research reported in this article was sponsored by the National Science Foundation (NSF Grant CTS-0086011). Dr. Stefan Thynell and Dr. Richard Smith were the NSF program monitors. The authors also acknowledge Mr. Mike Blair of Pratt & Whitney Corporation, Dr. Hee-Koo Moon of Solar Turbines, Inc., Mr. Edward North, Mr. Ihor Diakunchak of Siemens-Westinghouse Corp., and Dr. Sri Sreekanth and Dr. Ricardo Trindade from Pratt & Whitney—Canada Corporation for guidance and suggestions on this research effort. Technical support from FLUENT Incorporated is also acknowledged. Notes a Based on true chord length.
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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 | 0.001 |
| 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 it