Influence of Surface Roughness on the Aerodynamic Losses of a Turbine Vane
Bibliographic record
Abstract
The effects of surface roughness on the aerodynamic performance of a turbine vane are investigated for three Mach number distributions, one of which results in transonic flow. Four turbine vanes, each with the same shape and exterior dimensions, are employed with different rough surfaces. The nonuniform, irregular, three-dimensional roughness on the tested vanes is employed to match the roughness which exists on operating turbine vanes subject to extended operating times with significant particulate deposition on the surfaces. Wake profiles are measured for two different positions downstream the vane trailing edge. The contributions of varying surface roughness to aerodynamic losses, Mach number profiles, normalized kinetic energy profiles, Integrated Aerodynamics Losses (IAL), area-averaged loss coefficients, and mass-averaged loss coefficients are quantified. Total pressure losses, Mach number deficits, and deficits of kinetic energy all increase at each profile location within the wake as the size of equivalent sandgrain roughness increases, provided the roughness on the surfaces is uniform. Corresponding Integrated Aerodynamic Loss IAL magnitudes increase either as Mach numbers along the airfoil are higher, or as the size of surface roughness increases. Data are also provided which illustrate the larger loss magnitudes which are present with flow turning and cambered airfoils, than with symmetric airfoils. Also described are wake broadening, profile asymmetry, and effects of increased turbulent diffusion, variable surface roughness, and streamwise development.
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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 | 0.002 |
| 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 | 0.001 | 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".