Toward the Expansion of Low-Pressure-Turbine Airfoil Design Space
Bibliographic record
Abstract
Future engine requirements, including high-altitude flight of unmanned air vehicles as well as an impetus to reduce engine cost and weight, are challenging the current state of the art in low-pressure-turbine airfoil design. These new requirements present low-Reynolds number challenges as well as the need for high-performance, high-lift design concepts. Here, we report on an effort to expand the relatively well established aerodynamic design space for low-pressure turbine airfoils through the application of recent developments in transition modeling to airfoil design. Analytical and experimental midspan performance data and predicted loadings are presented for four high-lift airfoil designs based on the Pack B velocity triangles. The new designs represent a systematic expansion of low-pressure turbine airfoil design space through the application of high-lift design concepts for front- and aft-loaded airfoils. All four designs performed as predicted across a range of operationally representative Reynolds numbers. Full-span loss data for the new high-lift designs reveal increased endwall losses, which, with the application of nonaxisymmetric endwall contouring, have been substantially reduced. Taken holistically, the results presented here demonstrate that accurate transition modeling provides a reliable method to develop optimized, very high-lift airfoil designs. However, further improvements in endwall-loss mitigation technologies are required to enable the implementation of the very high-lift technology presented here in engine systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".