Effect of inflow conditions on transonic turbine airfoil limit loading
Bibliographic record
Abstract
To better understand airfoil limit loading and the effect inflow conditions have on local efficiency, a computational fluid dynamic investigation was performed for four different transonic turbine airfoils under sub-critical, critical, and supercritical conditions. A computational baseline was established using data previously collected at the Pratt and Whitney Canada High-Speed Wind Tunnel at Carleton University near design conditions using the Reynolds-Averaged Naiver-Stokes shear stress transport k − ω turbulence model with γ transition. The effects of inflow conditions on aerodynamic performance were examined by varying incidence by ± 20°, mainstream turbulence intensity from 5 to 20% and mainstream turbulent length scale from 1 to 100% of the airfoil pitch. Quantitative data of mass-flow averaged Mach numbers, mass-flow averaged flow angles, surface isentropic Mach number distributions and mass-flow averaged total pressure loss coefficients were collected and are presented alongside flow visualizations of numerical Schlieren images to allow for a detailed description of the entire flow domain. Similar to previous experimental work the limit loading pressure ratio and the mass-flow averaged outlet flow angle were strongly correlated with the airfoil outlet metal angle. The influence of inflow conditions was minimal on the exit flow profile with the exception of the mass-flow averaged total pressure loss coefficients. Results show incidence variation to change the total pressure loss coefficient depending on the airfoil, whereas, turbulence intensity and turbulent length scale predicted a drastic increase in loss with increased turbulence level for all airfoils considered.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".